Submitted: February 27, 2004 Revised: May 10, Applied Research Associates, Inc.

Transcription

1 INTRARISK Applied Research Associates, Inc. Submitted: February 27, 2004 Revised: May 10, 2004 Applied Research Associates, Inc. Florida Commission on Hurricane Loss Projection Methodology ASCE 7-98 Wind Map Statewide Inspections and Mitigation Certifications State of Florida Predicted 100 Year Return Period Peak Gust Hurricane Wind Speeds (mph) In Open Terrain Hurricane Hazard Mitigation Grant Program for the Hawaii Hurricane Relief Fund Hurricane Erin, Shading Represents 10 mph Ranges of Peak Gust Speeds in Open Terrain Pensacola LLWSAS (^ Storm Track Eglin AFB B71 (^ (^(^ Hurlburt, Panama City Beach ( Prepared for: Prepared by: FCHLPM Applied Research Associates, Inc. State Board of Administration 8540 Colonnade Center Drive, Ste Hermitage Boulevard Raleigh, North Carolina Tallahassee, Florida Copyright 2004 Applied Research Associates, Inc. For Evaluation by the Florida Commission on Hurricane Loss Projection Methodology

2 Florida Commission on Hurricane Loss Projection Methodology Model Identification Name of Model and Version: HURLOSS 3.2 Name of Modeling Company: Applied Research Associates, Inc. Street Address: 8540 Colonnade Center Dr., Suite 307 City, State, Zip: Raleigh, NC, Mailing Address, if different from above: Contact Person: Frank Lavelle Phone Number: (919) Fax Number: (919) Address: Date: February 27, 2004 Applied Research Associates, Inc. 2 May 1, 2004

3 Applied Research Associates, Inc. 3 May 1, 2004

4 Model Submission Checklist 1. Please indicate by checking below that the following has been included in your submission to the Florida Commission on Hurricane Loss Projection Methodology. Yes No Item X 1. Letter to the Commission X a. Refers to the Expert Certification Form and states that professionals having credentials and/or experience in the areas of meteorology, engineering, actuarial science, statistics, and computer science have reviewed the model for compliance with the Standards X b. States model is ready to be reviewed by the Professional Team X c. Any caveats to the above statements noted with a complete explanation X 2. Summary statement of compliance with each individual Standard and the data and analyses required in the Disclosures and Forms X 3. List of any non-proprietary information and documentation the modeler anticipates presenting to the Commission X 4. General description of any proprietary information the modeler intends to present to the Professional Team X 5. Model Identification X Bound Copies X CD ROMs containing: X a. Submission text in PDF format includes all Standards, Disclosures, and Forms not listed separately in d. below X b. PDF file bookmarked and highlightable X c. Data file names include abbreviated name of modeler, Standards year, and Form name (when applicable) X d. Forms V-2, A-1, A-2, S-1A, S-1B, S-2, S-7, S-8, S-9, and S-12 (for new modeling companies which have not previously provided the Commission with this analysis) in PDF format X e. Forms V-2, A-1, A-2, S-1A, S-1B, S-2, S-7, S-8, and S-9 in Excel format X f. Form S-12 (for new modeling companies which have not previously provided the Commission with this analysis) in ASCII format X 8. Table of Contents X 9. Materials consecutively numbered from beginning to end starting with the first page (including cover) using a single numbering system X 10. All tables, graphs, and other non-text items specifically listed in Table of Contents X 11. All tables, graphs, and other non-text items clearly labeled with abbreviations defined X 12. Standards, Disclosures, and Forms in italics, modeler responses in non-italics X 13. Graphs accompanied by legends and labels for all elements 2. Explanation of No responses indicated above. (Attach additional pages if needed.) Item 7f was provided in ARA s 2002 submission HurLoss 3.2 Francis M. Lavelle February 27, 2004 Model Name Modeler Signature Date Applied Research Associates, Inc. 4 May 1, 2004

5 ARA SUMMARY STATEMENT OF COMPLIANCE Standard Standard Met? General Standards G-1 Scope of the Computer Model and Its Implementation Yes G-2 Qualifications of Modeler Personnel and Independent Experts Yes G-3 Risk Location Yes G-4 Units of Measurement Yes G-5 Independence of Model Components Yes Meteorological Standards M-1 Official Hurricane Set Yes M-2 Hurricane Characteristics Yes M-3 Landfall Intensity Yes M-4 Hurricane Probabilities Yes M-5 Land Friction and Weakening Yes M-6 Logical Relationships of Hurricane Characteristics Yes Vulnerability Standards V-1 Derivation of Vulnerability Functions Yes V-2 Mitigation Measures Yes Actuarial Standards A-1 Underwriting Assumptions Yes A-2 Loss Cost Projections Yes A-3 User Inputs Yes A-4 Logical Relationship to Risk Yes A-5 Deductibles and Policy Limits Yes A-6 Contents Yes A-7 Additional Living Expenses (ALE) Yes Statistical Standards S-1 Use of Historical Data Yes S-2 Sensitivity Analysis for Model Output Yes S-3 Uncertainty Analysis for Model Output Yes S-4 County Level Aggregation Yes S-5 Replication of Known Hurricane Losses Yes S-6 Comparison of Estimated Hurricane Loss Costs Yes S-7 Output Ranges Yes Computer Standards C-1 Documentation Yes C-2 Requirements Yes C-3 Model Architecture and Component Design Yes C-4 Implementation Yes C-5 Verification Yes C-6 Model Maintenance and Revision Yes C-7 Security Yes All data and analyses required in Disclosures and Forms Yes Applied Research Associates, Inc. 5 May 1, 2004

6 ARA Checklist Items 3 and 4 Item 3. List of any non-proprietary information and documentation the modeler anticipates presenting to the Commission. Material to be presented to the Commission will be similar to that presented last year, subject to any changes suggested by the Professional Team during the on-site visit. The material presented will include a general overview of the model, focusing on changes from the previously accepted model. Item 4. General description of any proprietary information the modeler intends to present to the Professional Team. The Professional Team will be shown all documentation binders, validation studies and supporting information for changes in ZIP Codes, terrain, and wind speeds. Any other materials will be dependent upon requests or suggestions from the Professional Team. Applied Research Associates, Inc. 6 May 1, 2004

7 TABLE OF CONTENTS PAGE General Standards G-1 Scope of the Computer Model and Its Implementation G-2 Qualifications of Modeler Personnel and Independent Experts G-3 Risk Location G-4 Units of Measurement G-5 Independence of Model Components Form G-1: Expert Certification Meteorological Standards M-1 Official Hurricane Set M-2 Hurricane Characteristics M-3 Landfall Intensity M-4 Hurricane Probabilities M-5 Land Friction and Weakening M-6 Logical Relationships of Hurricane Characteristics Form M-1: Annual Occurrence Rates Form M-2: Radius of Maximum Winds Form M-3: Maps of Maximum Winds Vulnerability Standards V-1 Derivation of Vulnerability Functions V-2 Mitigation Measures Form V-1: One Hypothetical Event Form V-2: Mitigation Measures Range of Changes in Damage Actuarial Standards A-1 Underwriting Assumptions A-2 Loss Cost Projections A-3 User Inputs A-4 Logical Relationship to Risk A-5 Deductibles and Policy Limits A-6 Contents A-7 Additional Living Expenses (ALE) Form A-1: 30 Hypothetical Events Form A-2: Loss Costs Statistical Standards S-1 Use of Historical Data S-2 Sensitivity Analysis for Model Output S-3 Uncertainty Analysis for Model Output S-4 County Level Aggregation S-5 Replication of Known Hurricane Losses S-6 Comparison of Estimated Hurricane Loss Costs S-7 Output Ranges Form S-1A: Output Ranges Form S-1B: Output Ranges Form S-2: Percentage Change in Output Ranges Form S-3: Percentage Change in Output Ranges by County Form S-4: Zero Deductible Loss Costs by ZIP Code Applied Research Associates, Inc. 7 May 1, 2004

8 Form S-5: Average Annual Zero Deductible Statewide Loss Costs Form S-6: Five Validation Comparisons Form S-7: Official Storm Set Average Annual Zero Deductible Statewide Loss Costs Form S-8: Hurricane Andrew Loss Costs Form S-9: Distribution of Hurricanes by Size Form S-10: Probability of Hurricanes per Year Form S-11: Probable Maximum Loss (PML) Form S-12: Hypothetical Events for Sensitivity and Uncertainty Analysis Form S-1A: Output Ranges Listing Form S-1B: Output Ranges Listing Computer Standards C-1 Documentation C-2 Requirements C-3 Model Architecture and Component Design C-4 Implementation C-5 Verification C-6 Model Maintenance and Revision C-7 Security References Appendix A: Independent Actuarial Review Appendix B: Independent Meteorological Review Appendix C: Professional Credentials of Brian Grant for Expert Review of HurLoss Compliance with the Computer Science Standards Applied Research Associates, Inc. 8 May 10, 2004

9 LIST OF FIGURES PAGE Figure 1. Overview of Hurricane Damage and Loss Modeling Figure 2. High Level Flowchart of Portfolio (Multiple Buildings Multiple Site ) Computer Model Figure 3. ARA Office Locations and Technical Specialties Figure 4. Comparison of Modeled and Observed Wind Speeds at Inland Locations Figure 5. Comparison of Modeled and Observed Wind Speeds at Coastal Locations Figure 6. Comparison of Modeled and Observed Landfalling Counts of Hurricanes by Category (defined by wind speed) and Region Figure 7. The Five Florida Water Management Districts Figure 8. Example Comparisons of Modeled Degradation Rates to Kaplan-Demaria Decay Rates Figure 9. Region Definitions Figure 10. Range of Simulated R max vs. Central Pressure (values shown are the 5 th percentile, mean and 95 th percentile) Figure 11. Maximum One Minute Sustained Wind Speeds for the Historical Storm Set Figure Year Return Period Sustained Wind Speeds at a Height of 10 m Above Ground Figure 13. Flow Chart for Development of Building Vulnerability Functions Figure 14. Losses vs. Wind Speed for Form V-1, Part A Figure 15. Blank Study Disclosure Summary Form Figure 16. Study Disclosure Form for Forms S-1A and S-1B Figure 17. Figure 18. Figure 19. Comparison of Modeled and Observed Mean Content Damage vs. Mean Building Damage Comparison of Modeled and Observed Mean ALE Damage vs. Mean Building Damage Comparison of Historical and Modeled Distributions of Storm Heading Deficit of all Tropical Storms Passing Within 155 miles of Milepost Applied Research Associates, Inc. 9 May 1, 2004

10 Figure 20. Comparison of Historical and Modeled Distributions of the Central Pressure Deficit of all Tropical Storms Passing Within 155 miles of Milepost Figure 21. Distribution of AAL Associated with a 1% CoV in the Wind Speed Model Figure 22. Standard Errors in County Weighted Average Loss Costs Figure 23. Comparison of Modeled and Observed Losses for Homeowner Policies Figure 24. Comparison of Modeled and Actual Losses as a Function of Peak Gust Wind Speed in Open Terrain Figure 25. Standardized Residual vs. Normalized Actual Losses Figure 26. Figure 27. Figure 28. Comparison of Simulated and Observed Key Hurricane Statistics along the Gulf and Atlantic Coasts of the United States Comparison of Modeled and Observed Landfall Rates of Hurricanes as a Function of Intensity in Florida Comparison of Modeled and Observed Annual Number of Landfalling Hurricanes in Florida Figure 29. State of Florida by North/Central/South Counties Figure 30. State of Florida by Coastal/Inland Counties Figure 31. Percent Changes in Ground-Up Weighted Average Loss Costs by County for Owners-Frame Figure 32. Ground-Up Loss Cost for Wood Frame Houses Figure 33. Ground-Up Loss Cost for Masonry Wall Houses Figure 34. Ground-Up Loss Cost for Mobile Homes Figure 35. High Level Flowchart of Portfolio (Multiple Buildings Multiple Site) Computer Model Applied Research Associates, Inc. 10 May 1, 2004

11 LIST OF TABLES PAGE Table 1. Inland Distance of Hurricane Force Winds Table 2. Example of Insurer Loss Calculation Table 3. Percentage Change in Output Ranges Table 4. Historical Per Storm Ground-Up Losses Table 5. Monetary Contribution from Hurricane Andrew for Each Affected ZIP Code Applied Research Associates, Inc. 11 May 1, 2004

12 GENERAL STANDARDS G-1 Scope of the Computer Model and Its Implementation The computer model shall project loss costs for personal lines residential property from hurricane events. The ARA hurricane model provides estimates of loss costs for personal lines residential property from hurricane events, excluding flood and storm surge, except as it applies to ALE. Losses are developed for buildings, the contents of a building, appurtenant structures, and additional living expenses. Gross losses are developed and insured losses are computed using policy information. The model begins to estimate damage to buildings when the peak gust wind speed (in open terrain) produced by a hurricane equals or exceeds 50 mph. This 50 mph peak gust threshold corresponds to a sustained (one minute average) wind speed (in open terrain) of about 40 mph. The methodology used in the model to predict damage and loss given a wind speed will be presented to the professional team during their visit. The historical data sets used in the development and validation of the model do not include loss from flood and storm surge. 1. Specify the model and program version number reflecting the release date. Our current submittal is HurLoss Version 3.2 with a release date of February Provide a concise description of the model. Describe the theoretical basis of the model and include a brief description of the methodology, particularly the wind components, the damage components, and the insured loss components used in the model. Provide precise citations to representative or primary technical papers that describe the underlying model theory. ARA s hurricane model has been developed using wind engineering principles to enable detailed estimates of damage and loss to buildings and their contents due to wind storms. The model uses a peer reviewed (reviewed by both meteorologists and wind engineers) hurricane hazard model that enables the modeling of the entire track of a hurricane or tropical storm. The hurricane windfield model has been more extensively validated than any other published hurricane windfield model. The results of ARA s hurricane hazard model are used directly in the design wind speed map given in ASCE Since the hurricane model simulates the entire hurricane track, whether the storm makes landfall or not, computation of loss does not require a storm to make landfall, but simply requires that the wind speed produced by the storm at any point exceed a predefined minimum value. The model has the ability to treat storms that make multiple landfalls. ARA s physical damage model is based on load and resistance analysis of building components. The physical damage model estimates the damage to the building in terms of failure of building envelope components. Insured loss is estimated from the building damage states Applied Research Associates, Inc. 12 May 1, 2004 General Standards

13 using empirical cost estimation techniques for building repair and replacement. Contents loss is based on an empirical model that relates contents damage to building envelope performance. The building, appurtenance, contents, and loss of use components have been validated with insurance loss data. For portfolio assessments, a fast-running loss function is developed for each building class. These functions are used to estimate losses for each coverage type and deductible in each simulated storm. The computational procedure is straightforward simulation and, typically, 300,000 years of storms are computed. The model can produce losses by coverage, policy, site, zip code, county, state, and portfolio levels. The storm-by-storm and year-by-year output data can be easily postprocessed to obtain average annual loss (AAL), as well as the loss distribution statistics (Probable Maximum Loss, etc.) on a per occurrence or per year basis. Hurricane Hazard Model. The two key components of the hurricane hazard model are: (i) probabilistic models describing the occurrence rates, storm tracks, and intensities, and (ii) the hurricane windfield model. The probabilistic portion of the hurricane hazard model is described in detail in Vickery, et al. (2000b). The key features of the storm track model are the coupling of the modeling of the central pressure with sea surface temperature, and the ability to model curved tracks that can make multiple landfalls. The entire track of a storm is modeled, from the time of storm initiation over the water, until the storm dissipates. The starting times (hour, day and month) and locations of the storms are taken directly from the HURDAT data base. Using the actual starting times and locations ensures that any climatological preference for storms to initiate in different parts of the Atlantic Basin at different times of the year is maintained. The coupling of the central pressure modeling to sea surface temperature ensures that intense storms (such as category 5 storms) cannot occur in regions in which they physically could not exist (such as the New England area) and, as shown in Vickery, et al. (2000b), the approach is able to reproduce the variation in the central pressure characteristics along the United States coastline. In the hurricane hazard model, the storm s intensity is modeled as a function of sea surface temperature until the storm makes landfall. At the time of landfall, the filling models described in Vickery and Twisdale (1995a) are used to model the intensity of the weakening storm. Over land, following the approach outlined in Vickery, et al. (2000b), the storm size is modeled as a function of central pressure and latitude. If the storm exits land into the water, the storm intensity is again modeled as a function of sea surface temperature, allowing the storm to possibly re-intensify and make landfall again elsewhere. The validity of the modeling approach for storms near the coastal United States is shown through comparisons of the statistics historical and modeled key hurricane parameters along the North American coast. These comparisons are given in Vickery, et al. (2000b), where comparisons of occurrence rate, heading, translation speed, distance of closest approach, etc., are given. These comparisons are made using the statistics derived from historical and modeled storms that pass within 250 kilometers of a coastal milepost location. The comparisons are given for mileposts spaced 50 nautical miles apart along the entire United States Gulf and Atlantic coastlines. Hurricane Windfield Model. The hurricane windfield model used in our simulation model is described in detail in Vickery, et al. (2000a). The model uses the results of the numerical solution of the equations of motion of a translating hurricane. The asymmetries in a moving storm are a function of the translation speed of the storm and the nonlinear interactions between the wind velocity vectors and the frictional effects of the surface of the earth. The numerical solutions of the equations of motion of the hurricane have been solved separately for a storm translating over the ocean and for a storm translating over land. The separate solutions were Applied Research Associates, Inc. 13 May 1, 2004 General Standards

14 developed for the over land case and the over water case, since in the over water case, the magnitude of the surface drag coefficient is a function of the wind speed itself, whereas in the over land case the magnitude of the surface drag coefficient is wind speed independent. The outputs of the numerical model represent the integrated boundary layer averaged wind speeds, representative of a long duration average wind, taken as having an averaging time of one hour. The mean one hour average, integrated wind speeds are then combined with a boundary layer model to produce estimates of wind speeds for any height and averaging time. The boundary layer model, described in Vickery, et al. (2000a) and Vickery and Skerlj (2004), is based primarily on the ESDU (1982, 1983) models for the atmospheric boundary layer. The boundary layer model can deal with arbitrary terrain conditions (any surface roughness) changing both the properties of the mean flow field (i.e., the mean wind speed at a given height decreases with increasing surface roughness), as well as the gustiness of the wind (i.e., the gust factor increases with increasing surface roughness). The gust factor portion of the ESDU-based model has been validated through comparisons to gust factors derived from hurricane wind speed traces, as described in Vickery and Skerlj (2004). The entire hurricane wind field model (overall flow field, boundary layer model and gust factor model) has been validated through comparisons of simulated and observed wind speeds. These wind speed comparisons have been performed through comparisons of both the peak gust wind speeds and the 10 minute average wind speeds. The comparisons have shown the windfield model reproduces observed wind speeds well, matching both the gusts and the long period average winds. The model has been validated separately at offshore, coastal and inland stations, taking into account the effects of local terrain and anemometer height on the measured and simulated wind speeds. Damage and Loss Model. ARA s modeling approach for damage and loss employs two separate models. A building performance model, using engineering-based load and resistance models, is used to quantify physical damage. The building physical damage model takes into account the effects of wind direction changes, progressive damage, and storm duration. Economic loss, given physical damage, is estimated using repair and reconstruction cost estimation methods. This process is therefore similar to how an insurance adjuster would estimate the claim, given observed damage to the building. Through direct simulations of thousands of storms with representative buildings, the building performance model produces outputs that are postprocessed into loss functions for building, contents, and loss of use. The damage and loss modeling methodology is shown schematically in Figure 1. The loss projection model uses these fast-running loss functions (vulnerability functions) with the hurricane model to produce insured loss. Both the physical damage model and the loss model developed by ARA have been validated through comparisons of modeled and observed damage data collected after hurricane events. Separate models have been developed to estimate the financial losses to the building, the building s contents, additional living expenses, and losses to appurtenant and exterior structures. Software, Hardware, and Program Structure. ARA s detailed hurricane, building performance, and loss analysis models used to develop the fast-running hurricane loss modules have been developed in FORTRAN and C++. The databases supporting the models include binary data, flat text files, and MS Access files. These tools are run in-house and are not licensed to users. A Hurricane Risk Analysis Portfolio Tool is being developed separately in Microsoft Visual C Applied Research Associates, Inc. 14 May 1, 2004 General Standards

15 The applications require a PC with a Pentium II Intel-based processor, running a Windows NT operating system, a CD-ROM drive, and approximately 12.5GB of disk storage. Translation from Model Structure to Program Structure. ARA uses a modular design approach in converting from model structure to program structure. The analysis process is broken into distinct phases or modules: i.e., Wind, Damage, and Loss. Each of these modules is a stand-alone program. The architecture and program flow of each module are established in a structured design process through the creation of flow charts and/or pseudo-code. Each model element is translated into subroutines or functions on a one-to-one basis. Changes to the models are reflected by one-to-one changes in the software code. Although this approach is not a pure object-oriented design approach, it does incorporate some of the features, such as data encapsulation, that make the object-oriented approach successful. Advanced Hurricane Model Hazard Risk & Track Model Windfield Model Building Performance Engineering Models Loads Resistances Failures Physical Damage Building Losses Fast Running Loss Functions Validation (individual component and end-to-end models) (a) Individual Buildings and Building Class Performance Model Portfolio Type Exposure Inputs Terrain Module Fast-Running Loss Functions Policy Information Hurricane Data Set Loss Analyzer Ground-Up Losses Insured Losses Statistical End-to-End Validation (b) Multiple Site Multiple Building Loss Projections Figure 1. Overview of Hurricane Damage and Loss Modeling Theoretical Basis. As indicated earlier, the hurricane hazard model is developed using the historical database of storms given in HURDAT. ARA s model has been peer reviewed and accepted for publication in the American Society of Civil Engineers publication, The Journal of Structural Engineering. The model, in its entirety, was used to develop the design wind speeds given in ASCE-7-98 for the hurricane prone coastline of the United States. ARA s hurricane model is routinely used by two of the three major North American Boundary Layer Wind Tunnel Laboratories to determine the site-specific hurricane climate risks in terms of directionallydependent speed frequencies for determining design wind loads for major buildings along the United States coastline, Caribbean, and Asia. Applied Research Associates, Inc. 15 May 1, 2004 General Standards

16 The physical damage methodology has been developed using a load and resistance modeling approach. Load and resistance analysis methods are fundamental to an evaluation of building performance to extreme winds and are used extensively in the analysis and design of structural systems, including codified design. Some important theoretical and application references are given by Twisdale and Vickery (1995) in Chapter 20, Extreme Wind Risk Assessment, of the Probabilistic Structural Mechanics Handbook. Key references are highlighted in the reference list with an asterisk. 3. Provide a flow diagram that illustrates interactions among major model components. The main program analysis modules used in the multiple building model are shown in Figure 2. More detailed, proprietary flow-charts of each module will be available for the professional team, on-site visit. Policy Data Zip Code Mapping Terrain Portfolio Mapping Building Class Mapping Hurricane Data Loss Estimation Fast-Running Loss Functions Ground-Up Loss Portfolio Information Deductible, etc. Insured Loss Figure 2. High Level Flowchart of Portfolio (Multiple Buildings Multiple Site) Computer Model Applied Research Associates, Inc. 16 May 1, 2004 General Standards

17 G-2 Qualifications of Modeler Personnel and Independent Experts A. Model construction, testing, and evaluation shall be performed by modeler personnel or independent experts who possess the necessary skills, formal education, or experience to develop hurricane loss projection methodologies. B. The model or any modifications to an accepted model shall be reviewed by modeler personnel or independent experts in the following professional disciplines, if relevant: structural/wind engineering (licensed Professional Engineer (PE)), statistics (advanced degree), actuarial science (Associate or Fellow of Casualty Actuarial Society), meteorology (advanced degree), and computer/information science (advanced degree). These individuals shall abide by the standards of professional conduct if adopted by their profession. ARA s staff involved in the development of the hurricane model has extensive experience in wind engineering and hurricane loss projection. The wind engineering field includes meteorology, structural engineering, bluff body aerodynamics, probability, statistics, and risk analysis. A number of our staff are considered experts in the field of wind engineering and hurricane modeling, as demonstrated through publishing of peer reviewed papers, acting as reviewers for papers related to hurricane modeling submitted to respected professional journals, and through their active involvement in the development of wind related codes and standards in the United States. Independent experts have also provided inputs and review to the model development evaluation. These individuals all abide by standards of professional conduct adopted by their professions. 1. Company Background A. Describe the ownership structure of the modeling company. Describe affiliations with other companies and the nature of the relationship, if any. Indicate if your company has changed its name and explain the circumstances. Applied Research Associates, Inc., is an employee-owned company. ARA is not affiliated with any other company. B. If the model is developed by an entity other than a modeling company, describe its organizational structure and indicate how proprietary rights and control over the model and its critical components is exercised. If more than one entity is involved in the development of the model, describe all involved. The model was developed by ARA. No other entities were involved in the model development. C. If the model is developed by an entity other than a modeling company, describe the funding source for the model. Not applicable. Applied Research Associates, Inc. 17 May 1, 2004 General Standards

18 D. Describe the modeler s services and the percentage of the company s annual income derived from each. ARA is a diversified engineering and applied science research, consulting, and software development firm. ARA has approximately 884 employees with 14 major offices throughout the United States, as shown in Figure 3. The risk analysis, wind engineering, and building performance capabilities are located in Raleigh, NC and Orlando, FL. Our building inspection and wind mitigation certification services are located in both Raleigh and Orlando. The breakout of services by broad classes is currently: Defense Technologies 34% Civil Technologies 16% Environmental Technologies 13% Transportation 12% Tests and Measurements 8% Computer Software 8% Manufacturing 5% Systems Analysis 4% 100% ARA s Technical Capabilities b l Probabilistic Risk and Decision Analysis Software Development Wind Engineering Environmental Engineering Structural Engineering Risk Management Engineering Reliability Facility/Security Engineering Pavements and Geotechnical Engineering Tests and Measurements Explosive Effects Figure 3. ARA Office Locations and Technical Specialties Applied Research Associates, Inc. 18 May 1, 2004 General Standards

19 Our hurricane risk analysis, risk management, insurance applications, and wind engineering services are included in the Civil Technologies. The approximate breakout of these services is: Building Codes and Training 6% Research & Development (Hurricane and Wind) 50% Insurance Studies and Services 40% Building Owners, Consultants, Wind Tunnels 4% E. Indicate how long the model has been used for analyzing insurance company exposures or other such uses. Describe these uses. ARA s model has been used in insurance related analyses and exposures since ARA s hurricane model has also been used to perform individual residential risk/loss analysis and mitigation benefit-costs analyses in the State of Florida since About 2200 homes have been analyzed to date. Beginning in 1999, ARA s models have been used to perform commercial building damage and loss risk assessments. F. Indicate if the modeling company has ever been involved in litigation or challenged by a statutory authority where the credibility of one of its U. S. hurricane model versions was disputed. Describe the nature of the case and the conclusion. None. G. Provide the number of the company s clients in the following categories: ratemaking, reinsurance and capital markets, government. Our hurricane catastrophe modeling/risk management work mix has been approximately: state government (25%), federal government (50%), insurers and reinsurers (20%), building owners/consultants, and wind tunnels (5%). 2. Professional Credentials A. Provide the highest degree obtained (discipline and University), employment or consultant status and tenure, and relevant experience of individuals involved in the primary development of or revisions to the following aspects of the model: The individuals involved in the primary development of the model are listed below. The requested information on education and employment is provided in the following paragraphs. 1. Meteorology Peter J. Vickery, Ph.D., P.E. Peter F. Skerlj, M.E.Sc., P.E. Lawrence A. Twisdale, Jr., Ph.D., P.E. Applied Research Associates, Inc. 19 May 1, 2004 General Standards

20 2. Vulnerability Peter J. Vickery, Ph.D., P.E. Peter F. Skerlj, M.E.Sc., P.E. Jason Lin, Ph.D. Michael A. Young, M.E.Sc., P.E. Lawrence A. Twisdale, Jr., Ph.D., P.E. Chris Driscoll, B.S. 3. Actuarial Science 4. Statistics Peter J. Vickery, Ph.D., P.E. Jason Lin, Ph.D. Peter F. Skerlj, M.E.Sc., P.E. Marshall B. Hardy, M.S. Srinivas R. Kadasani, M.S. Michael A. Young, M.E.Sc., P.E. Lawrence A. Twisdale, Jr., Ph.D., P.E. Peter J. Vickery, Ph.D., P.E. Peter F. Skerlj, M.E.Sc., P.E. Jason Lin, Ph.D. Marshall B. Hardy, M.S. Francis M. Lavelle, Ph.D., P.E. Lawrence A. Twisdale, Jr., Ph.D., P.E. 5. Computer Science Srinivas R. Kadasani, M.S. Francis M. Lavelle, Ph.D., P.E. Michael A. Young, M.E.Sc., P.E. Peter J. Vickery, Ph.D., P.E. Richard W. Pearson, B.S. Chris Driscoll, B.S. Lawrence A. Twisdale, Jr., Ph.D., P.E., Civil Engineering, University of Illinois Dr. Lawrence A. Twisdale, Jr., joined ARA in He is a Principal Engineer and Senior Vice President and has more then 30 years of experience directing and performing research and engineering analyses for extreme wind effects and structural response. Dr. Twisdale Applied Research Associates, Inc. 20 May 1, 2004 General Standards

21 has 12 years experience in the application of hurricane modeling relevant to insurance loss analysis and rate making. Dr. Twisdale has been involved in developing methodology and applications to extreme wind risk analysis and wind-borne debris hazard analysis since Dr. Twisdale has performed numerous site-specific wind analyses, including tornado, hurricane, and extratropical cyclones. Dr. Twisdale was Co-Principal Investigator with Dr. Peter Vickery on the NSF research Hurricane Wind Hazards and Design Risk in the United States. This research has developed a physics and meteorology-based numerical windfield model as part of a wind hazard risk assessment methodology. Dr. Twisdale was Principal Investigator on an NSF funded project to test a Predictive Methodology for Building Cladding Performance Using a Sample of Hurricane Bertha Damage Data. He has also led the efforts on the Residential Construction Mitigation Program (RCMP) from in Southeast Florida that involved engineering mitigation analysis of over 2200 houses. He was Principal Investigator on loss mitigation studies in for the State of Florida for both single-family residences and multi-family buildings. Dr. Twisdale is the Co-Principal Investigator for FEMA s HAZUS Wind Loss Estimation Methodology. He was Principal Investigator on several industry funded research projects on the Analysis of Hurricane Windborne Debris Impact Risks for Residential Structures that influenced the SSTD-12 and ASTM Standards. He is a member of the Wind Engineering Research Council, IBHS Wind Committee, and the American Society of Civil Engineers. He has served on several ASCE technical committees on wind effects, structural reliability, and dynamic structure response. Dr. Twisdale has over 70 technical papers in wind engineering, risk analysis, response of structures to extreme wind, and wind-borne debris effects. Dr. Twisdale was an invited author to prepare the chapter on Extreme Wind Risk Assessment in the Probabilistic Structural Mechanics Handbook, published in Peter Vickery, Ph.D., P.E., Engineering Science, University of Western Ontario Dr. Peter Vickery joined Applied Research Associates in Prior to joining ARA, Dr. Vickery completed both his Masters and Doctoral studies at the University of Western Ontario, working with Dr. Alan Davenport, an internationally recognized expert and leader of modern wind engineering technology. Dr. Vickery has over 19 years of experience in wind engineering. He has 12 years experience in the application of hurricane modeling relevant to insurance loss analysis/rate making. Dr. Vickery has published six peer reviewed journal papers related to hurricane risk and additional papers related to wind loads on buildings and other structures. Dr. Vickery serves on the ASCE-7 wind load task committee developing wind loading provisions for use in the United States, and has served on the Board of Directors of the American Association for Wind Engineering. In 1996, Dr. Vickery received the Collingswood Prize from ASCE for the best paper published by a younger member. With Dr. Lawrence Twisdale of Applied Research Associates, Dr. Vickery developed the hurricane missile models used in the wind load damage tools. The hurricane missile model results were instrumental in defining the wind borne debris risk criteria as given in the SSTD-12 missile protection standard and the new ASTM wind borne debris standard. He is a member of the American Society of Civil Engineers and a Licensed Engineer in the state of North Carolina. Dr. Vickery has performed post storm damage surveys following Hurricanes Andrew, Erin, Opal, Fran, Bertha, Bonnie, and Isabel. Following Hurricane Andrew, Dr. Vickery served as an expert witness testifying to the wind speeds produced by the storm. He was responsible for developing the wind load and damage portion of the loss model used for residential structures and played the primary role in the development of ARA s hurricane simulation model. Dr. Vickery was Co-Principal Investigator on ARA s effort to develop the Applied Research Associates, Inc. 21 May 1, 2004 General Standards

22 HAZUS Wind Loss Estimation methodology and software and ARA s Mitigation Grant Feasibility Study for the Hawaii Hurricane Relief Fund. Jason Lin, Ph.D., Aerodynamics Engineering, Nanjing University of Aeronautics & Astronautics Dr. Jason Lin was employed by ARA from 1997 to Prior to joining ARA, he worked 6 years as a Senior Research Engineer and Project Manager at the Boundary Layer Wind Tunnel Laboratory (BLWTL) at the University of Western Ontario, London, Canada, a world famous organization in the field of Wind Engineering. At BLWTL, Dr. Lin worked closely with several of the best known and respected wind engineers in the world: Alan G. Davenport, David Surry and Nick Isyumov. Dr. Lin s work at BLWTL involved wind tunnel investigations, computer modeling and predictions of wind effects on various types of buildings, and computer modeling of storm wind fields and simulations of extreme wind climates. He has authored over 10 publications in professional journals, including several co-authored with Drs. Davenport, Surry (BLWTL) and Dr. Simiu (National Institute of Standards and Technology). Dr. Lin had also been invited by the Swedish National Institute for Building Research to colead a joint research program on building aerodynamics in Sweden in Having been the Principal Engineer of the first wind laboratory sponsored by the Chinese construction industry and conducted wind studies on several of the then tallest buildings in Asia, Dr. Lin was named one of the 10 Most Outstanding Young Researchers in Engineering Sciences by the National Natural Sciences Foundation of China in As a Principal Scientist at ARA, Dr. Lin was responsible for the development of the vulnerability functions, including building vulnerability to wind pressure effects and wind-borne debris impacts. He also contributed to the calculation of insurance losses. He has 10 years experience in the application of hurricane modeling for insurance loss analysis and 21 years experience in wind engineering. Peter F. Skerlj, M.E.Sc., P.E., Engineering Science, University of Western Ontario Mr. Peter Skerlj was employed by ARA from 1997 to January He has over 9 years experience in wind engineering and hurricane modeling relevant to rate making. He has been involved in boundary layer wind tunnel studies including a study of mean external pressure gradients and residual pressures acting on pressure-equalized rainscreen wall systems. He has also been extensively involved in modeling the climatology of wind-driven rain across Canada. He has been involved in a NSF-funded project that developed a new approach for assessing hurricane risk in the United States. This approach has been used to develop the hurricane wind speed maps for ASCE He has analyzed hurricane upper-level pressure and wind velocity data measured from NOAA aircraft for the development of hurricane windfield modeling parameters. He has been involved in a NSF-sponsored project that investigated the correlation between Doppler Radar measurements and the areal extent of strong winds in thunderstorms. He has been involved in a NSF-sponsored project that developed risk consistent hurricane-induced storm surge and wind modeling for the United States coastline and was also involved in a NSFfunded project on hurricane-induced wave modeling. He has been extensively involved in conceptualizing and developing a computer program to predict wind-induced damage to building structures, including prediction of damage to the external building envelope and prediction of water entering the building through failed envelope components. He has performed post storm damage surveys for Hurricane Bonnie (1998). He is a member of the American Society of Civil Engineers and a Licensed Engineer in the state of North Carolina. Applied Research Associates, Inc. 22 May 1, 2004 General Standards

23 Michael Young, M.E.Sc., P.E., Engineering Science, University of Western Ontario Mr. Young was employed by ARA from 1997 to He joined ARA 1997 after earning a Master s degree in Wind Engineering at the University of Western Ontario s Boundary Layer Wind Tunnel Laboratory (BLWTL). Mr. Young has done physical wind tunnel modeling of lowrise and high-rise structures while working as a project engineer at BLWTL. He has also been employed as a project engineer at Rowan Williams Davies and Irwin, Inc. (RWDI) doing wind tunnel simulations on buildings in the United States and overseas. Mr. Young has performed post-hurricane damage surveys for Hurricane Bonnie (1998). His responsibilities included development of individual wind risk analysis tools for residential and commercial buildings, validation of existing damage and loss models, and development of software products for insurance portfolio risk assessment. Mr. Young is a member of the American Society or Civil Engineers, and also the Southern Building Code Congress, Inc. Mr. Young has 7 years of experience in the hurricane modeling field and 9 years of experience in wind engineering. Mr. Young is a Licensed Engineer in the state of North Carolina. Chris Driscoll, B.S., Civil Engineering, University of South Florida Mr. Driscoll joined ARA in 1999 as a Staff Engineer bringing 10 years of various experience in residential/commercial construction and computer programming. He earned his B.S. in Civil Engineering from the University of South Florida. He has been working on residential/commercial hurricane inspection and mitigation programs for the Department of Community Affairs, FEMA, and for small municipalities. Mr. Driscoll has evaluated posthurricane wind damage surveys for Erin (1995), Opal (1995), Georges (1998), and Isabel (2003). He has performed building stock analysis using data collected from Florida Panhandle and South Florida building surveys. He is a developer of wind damage assessment computer modeling tools. Mr. Driscoll has 5 years experience in hurricane modeling and 6 years in structural engineering. Srinivas R. Kadasani, M.S., Environmental Engineering, Oklahoma State University Mr. Kadasani joined ARA s wind group in July Mr. Kadasani graduated from Oklahoma State University (OSU) majoring in Environmental Engineering with emphasis in Computer Modeling. As a research assistant at OSU, Mr. Kadasani conducted experimental studies on ground water bio-remediation of TNT and p-dcb contaminated aquifers. As a part of his graduate thesis, he developed a software plume model for TNT and DCB migration through different types of aquifers. His current responsibilities include engineering analysis, terrain modeling, development of ARA s portfolio analysis software, and HAZUS Wind Loss Estimation Model. His areas of expertise include Object-Oriented and Interface-Oriented design, programming, and GIS-based applications. Mr. Kadasani has 5 years experience in the area of hurricane application modeling and software development. Francis M. Lavelle, Ph.D., P.E., Civil Engineering, Rice University Dr. Francis M. Lavelle joined ARA in He is a Principal Engineer with over 13 years of research, development, and project management experience in the areas of extreme loading effects, risk analysis of structures, and software development. Dr. Lavelle s graduate research at Rice University included studies on the dynamic response of locally nonlinear structures, the seismic response of secondary systems, and retrofitting techniques for reducing earthquakeinduced pounding damage in adjacent buildings. Dr. Lavelle is currently leading ARA s software development efforts for ARA s HurLoss model and for the HAZUS Wind Loss Estimation Model. HAZUS is a joint effort between the Federal Emergency Management Applied Research Associates, Inc. 23 May 1, 2004 General Standards

24 Agency (FEMA) and the National Institute of Building Sciences (NIBS) to produce a nationally applicable software program for estimating potential losses from earthquake, flood, and wind hazards. He has been Principal Investigator on a number of government-sponsored research projects, including an $8.0M, 3-year contract involving both structural engineering research and software development. During the same period, he was also responsible for managing ARA s Advanced Modeling and Software Systems (AMSS) group, a group of 15 civil, mechanical, aerospace, and software engineers. Before heading the AMSS group, Dr. Lavelle was responsible for developing reliability-based design safety factors for structural response to extreme loads. Dr. Lavelle is a member of the American Society of Civil Engineers and has been a registered professional engineer in the state of North Carolina since Marshall B. Hardy, M.S., Statistics, University of Kentucky Mr. Marshall B. Hardy joined ARA in 1983 and has over 20 years experience in applied statistical analysis and probabilistic risk assessment. He has a Masters degree in Statistics from the University of Kentucky. He has approximately 6 years experience in support of hurricane modeling relevant to insurance loss analysis and rate making. Mr. Hardy performs multivariate statistical analysis and tests for ARA s wind engineering applications. He has aided in the design of simulations, analyzed loss distributions, and performed multivariate analyses of hurricane damage and loss data sets. He has aided in the development of loss functions. He was a key developer of ARA s TORSCR code that is now the nuclear power industry standard for PRA for extreme wind and tornado-missile hazard. He has applied the TORSCR code and statistically characterized distribution parameters for ten nuclear power plant PRAs. Mr. Hardy has also served as lead statistical analyst on numerous DoD, NSF, and EPA projects. This experience involved statistical modeling of uncertainty in nonlinear elasto-plastic response to wind, or multivariate analysis-of-variance (ANOVA) to identify dominant uncertainties in the predictive methodologies, and cluster analysis for code validation. Mr. Hardy is an expert in the SAS statistical analysis system that will be used in this work and is also an experienced FORTRAN programmer on a number of systems including the DNA Cray at Los Alamos, DEC minicomputers, and PCs. The Air Force published his 1997 paper on sequential design of experiments or simulations. Richard W. Pearson, B.S., Computer Science, North Carolina State University Mr. Richard W. Pearson joined ARA s wind group in July He has 8 years of experience in software development and a background in object oriented programming, analysis, and design. Mr. Pearson graduated from North Carolina State University (NCSU) majoring in Computer Engineering with an emphasis on Software Architecture, Analysis, and Design. As a Software Engineer at Stingray Software, Mr. Pearson contributed to the design and development of the award-winning software tools suite, Objective Studio. During the same period, Mr. Pearson was a member of a research team tasked with identifying potential new markets and products for Stingray Software. Mr. Pearson s current responsibilities include the architecture and development of ARA s portfolio analysis software. He has over 2 years experience in the application of hurricane modeling. B. Identify any new employees or consultants (since the previous submission) working on the model. No new employees or consultants have worked on the model development since our previous submission. Applied Research Associates, Inc. 24 May 1, 2004 General Standards

25 C. Provide visual business workflow documentation connecting all personnel related to model design, testing, execution, and maintenance. The organization of the model and supporting personnel are shown below. Lead individuals are shown in bold. Individuals who are not current ARA employees are shown in italics. Hurricane Simulation Group Vol I Vickery Twisdale Skerlj Individual Risk Group Vickery Driscoll Lin Twisdale Skerlj Young Vol II Portfolio Analysis Group Vol III Lavelle Pearson Kadasani Driscoll Vickery Twisdale Lin Statistics Vickery Hardy Twisdale Lavelle Lin Skerlj Computer Science Lavelle Pearson Kadasani Vickery Driscoll Skerlj Young Loss Studies Lavelle Twisdale Vickery Young D. Provide the names, positions, credentials, and their role in the development of the model of any individuals who are not full-time employees of the modeler. Indicate specifically whether such individuals are associated with the insurance industry, consumer advocacy group, or a government entity as well as their involvement with consulting activities. The following individuals are no longer full-time employees of ARA: Associated with Individual Status Insurance Industry Consumer Advocacy Government Entity Peter J. Vickery, Ph.D., P.E. Part-time 1 No No No Jason Lin, Ph.D., P.E. No longer No No No Peter F. Skerlj, P.E. employed by Yes No No Michael A. Young, P.E. ARA Yes No No 1 Since October 2002, Dr. Vickery has been an employee of both the Boundary Layer Wind Tunnel Laboratory at the University of Western Ontario and ARA. He is returning to full time employment with ARA on April The roles of these individuals in the development of the model are identified in Section G-2.2.A. Dr. Lin, Mr. Skerlj, and Mr. Young were full-time employees of ARA during the time of their involvement in the development of the model. 3. Independent Expert Review A. Provide dates of independent peer reviews that have been performed on the following aspects of the model: 1. Meteorology The wind speed (wind field) model has been anonymously peer reviewed for publication in the Journal of Structural Engineering by experts in the fields of wind engineering and meteorology. Applied Research Associates, Inc. 25 May 1, 2004 General Standards

26 The wind speed frequency model was anonymously reviewed at the same time the wind speed model was reviewed, also for publication in the Journal of Structural Engineering. The meteorological components have also been peer reviewed by the HAZUS Wind Committee. 2. Vulnerability The vulnerability function parameters have not been reviewed by personnel outside of ARA, except that the HAZUS Wind Committee has reviewed methodology and procedures used to develop the vulnerability functions. 3. Actuarial Science The actuarial components and resulting annualized loss costs have been reviewed by a consulting actuarial firm. 4. Statistics None, except through review of publications. None. 5. Computer Science B. Provide documentation of independent peer reviews of the Standards or Disclosures. Identify any unresolved or outstanding issues as a result of these reviews. The journal articles describing the windfield and climatological modeling were peer reviewed following the standard process for publication in ASCE s refereed journals. The fact that the articles were published indicates that there were no unresolved or outstanding issues. The actuarial review was performed by Douglas J. Collins, FCAS, MAAA, a Consulting Actuary with the firm of Tillinghast Towers Perrin, in February There were no unresolved or outstanding issues as a result of this review (see Appendix A). The meteorological review was performed by Dr. Steven Businger, a Professor of Meteorology at the University of Hawaii, in February There were no unresolved or outstanding issues as a result of this review (see Appendix B). C. Describe the nature of any on-going or functional relationship the company has with any of the persons performing the independent peer reviews. None. The actuarial and meteorological reviews of the model were paid for by our firm. D. Describe any review by an independent organization, such as Standard and Poor s, Moody s, etc. No reviews have been performed by such organizations. 1. Provide a completed Form G-1, Expert Certification. See Form G-1. Applied Research Associates, Inc. 26 May 1, 2004 General Standards

27 G-3 Risk Location A. ZIP Codes used in the model shall be updated at least every 24 months using information originating from the United States Postal Service. The United States Postal Service issue date of the updated information shall be reasonable. B. ZIP Code centroids, when used in the model, shall be based on population data. C. ZIP Code information purchased by the modeler shall be verified by the modeler for accuracy and appropriateness. ZIP Code centroids used in the model are the population centroids, and are updated at least every 24 months. The issue date of the current ZIP Code data set is October Maps showing the zip code boundaries and the associated centroids will be available to the professional team. 1. List the current ZIP Code databases used by the model and the components of the model to which they relate. Provide the effective (official United States Postal Service) date corresponding to the ZIP Code databases. The release date of the ZIP Code database used by the model is October 1, The effective date of the current United States Postal Service ZIP Code data set is August Describe in detail how invalid ZIP Codes are handled. If ZIP Codes are unidentified, we first attempt to map the zip code to one in our historical databases. If this is not successful, we use public domain information on historical ZIP Codes in an attempt to map the ZIP Code. If a policy with an older ZIP Code is found, it is mapped to the centroid of its new ZIP Code in the most current database. Finally, if ZIP Codes cannot be reassigned, they are removed from the analysis and reported as missing ZIP Codes. Applied Research Associates, Inc. 27 May 1, 2004 General Standards

28 G-4 Units of Measurement A. All units of measurement for model inputs and outputs shall be clearly identified. B. All model outputs of length, wind speed, and pressure shall be in units of statute miles, statute miles per hour, and millibars, respectively. C. Wind inputs to the damage function shall be in units consistent with currently used wind measurement units and/or shall be converted using standard meteorological/engineering conversion factors. All units of measure for model inputs and outputs are clearly identified. The conversion factors used in the hurricane model are: 1.0 mph = m/s 1.0 km = statute miles 1. All conversion factors or techniques shall be disclosed. ARA s windfield model is valid for any averaging time and height above ground. The windfield model has been validated using both peak gust wind speeds and wind speeds averaged over ten minutes. In the validation studies, all measured wind speed data were adjusted (when needed) to a height of 10m above ground (in local terrain conditions) before wind speed comparisons were performed. The basic output of the model, for use in loss estimation, is the peak gust windspeed. Applied Research Associates, Inc. 28 May 1, 2004 General Standards

29 G-5 Independence of Model Components The meteorology, vulnerability, and actuarial components of the model shall each be theoretically sound without compensation for potential bias from the other two components. Relationships within the model among the meteorological, vulnerability, and actuarial components shall be reasonable. All of the components of ARA s hurricane model, including the wind speed, climatology, damage, and loss models have been individually developed and validated. The hurricane wind field model used in ARA s hurricane model has been validated through comparisons of modeled and observed wind speed data using information collected from over 15 landfalling storms. Details describing the ARA wind field model, and the efforts taken to validate the model are given in Vickery, et al. (2000a). Example hurricane wind field validation plots are given in Figures 4 and 5. The damage and loss models used to produce vulnerability functions are based on firstprinciple approaches: engineering load and resistance analysis for physical damage estimation, and repair and reconstruction cost models for building loss estimation. These components have been independently validated. The actuarial components are theoretically sound and have also been validated separately without potential bias or compensation to the meteorology and vulnerability components. The relationships among the meteorological, vulnerability, and actuarial components of the model are reasonable. Hurricane Elena, Pensacola NAS Hurricane Hugo, Charlotte Airport Wind Direction Wind Direction :00 4:00 8:00 12:00 16:00 Time UTC, 2 September :00 4:00 8:00 12:00 Time UTC, 22 September Ten Minute Wind Speed (mph) :00 4:00 8:00 12:00 16:00 Ten Minute Wind Speed (mph) :00 4:00 8:00 12:00 Time UTC, 2 September 1985 Time UTC, 22 September Gust Wind Speed (mph) Gust Wind Speed (mph) :00 4:00 8:00 12:00 16:00 Time UTC, 2 September :00 4:00 8:00 12:00 Time UTC, 22 September 1989 Figure 4. Comparison of Modeled and Observed Wind Speeds at Inland Locations Applied Research Associates, Inc. 29 May 1, 2004 General Standards

30 Hurricane Hugo, Columbia Airport Hurricane Hugo, Shaw AFB Wind Direction Wind Direction :00 4:00 8:00 12:00 Time UTC, 22 September :00 4:00 8:00 12:00 Time UTC, 22 September Ten Minute Wind Speed (mph) Ten Minute Wind Speed (mph) :00 4:00 8:00 12:00 0:00 4:00 8:00 12:00 Time UTC, 22 September 1989 Time UTC, 22 September Gust Wind Speed (mph) Gust Wind Speed (mph) :00 4:00 8:00 12:00 0:00 4:00 8:00 12:00 Time UTC, 22 September 1989 Time UTC, 22 September 1989 Hurricane Fran, RDU Airport Hurricane Frederic, Mobile Wind Direction :00 0:00 4:00 8:00 12:00 Time UTC, 5 6 September 1996 Wind Direction :00 0:00 4:00 8:00 12:00 Time UTC, September Ten Minute Wind Speed (mph) :00 0:00 4:00 8:00 12:00 Time UTC, 5 6 September Ten-Minute Wind Speed (mph) :00 0:00 4:00 8:00 12:00 Time UTC, September Gust Wind Speed (mph) Gust Wind Speed (mph) :00 0:00 4:00 8:00 12: :00 0:00 4:00 8:00 12:00 Time UTC, 5 6 September 1996 Time UTC, September 1979 Figure 4 (concluded). Comparison of Modeled and Observed Wind Speeds at Inland Locations Applied Research Associates, Inc. 30 May 1, 2004 General Standards

31 Hurricane Fran, Kure Beach Hurricane Bertha, Kure Beach Wind Direction Wind Direction :00 16:00 20:00 0:00 4:00 8:00 12:00 0 0:00 4:00 8:00 12:00 16:00 20:00 0:00 4:00 8:00 Time UTC, 5 6 September 1996 Time UTC, July Ten Minute Wind Speed (mph) :00 16:00 20:00 0:00 4:00 8:00 12:00 Ten Minute Wind Speed (mph) :00 4:00 8:00 12:00 16:00 20:00 0:00 4:00 8:00 Time UTC, 5 6 September 1996 Time UTC, July Gust Wind Speed (mph) Gust Wind Speed (mph) :00 16:00 20:00 0:00 4:00 8:00 12:00 0:00 4:00 8:00 12:00 16:00 20:00 0:00 4:00 8:00 Time UTC, 5 6 September 1996 Time UTC, July 1996 Hurricane Elena, Dauphin Island Sea Lab Hurricane Hugo, Myrtle Beach AFB Wind Direction Wind Direction :00 4:00 8:00 12:00 16:00 Time UTC, 2 September :00 0:00 4:00 8:00 12:00 Time UTC, September Ten Minute Wind Speed (mph) Ten Minute Wind Speed (mph) :00 4:00 8:00 12:00 16:00 20:00 0:00 4:00 8:00 12:00 Time UTC, 2 September 1985 Time UTC, September Gust Wind Speed (mph) Gust Wind Speed (mph) :00 4:00 8:00 12:00 16:00 Time UTC, 2 September :00 0:00 4:00 8:00 12:00 Time UTC, September 1989 Figure 5. Comparison of Modeled and Observed Wind Speeds at Coastal Locations Applied Research Associates, Inc. 31 May 1, 2004 General Standards

32 Form G-1: Expert Certification In accordance with I.A. of the Process for Determining the Acceptability of a Computer Simulation Model, the following credentialed experts in the areas of the discipline indicated below do hereby certify that they have reviewed the model for compliance with the Standards and, according to their professional standards and code of ethical conduct, do hereby certify that the model complies with the 2003 Standards adopted by the Florida Commission on Hurricane Loss Projection Methodology and is ready to be reviewed by the Professional Team. NOTE: A facsimile or any properly reproduced signature on the same or identical forms will be acceptable to meet this requirement. Updated signatures are required following modifications to the model. METEOROLOGY: Steven Businger, Ph.D. Professor of Meteorology, University of Hawaii Print Name, Title, and Degree or Credentials ENGINEERING: See Page 33 Signature Peter J. Vickery, Ph.D., P.E. Principal Engineer, Applied Research Associates Print Name, Title, and Degree or Credentials Date ACTUARIAL SCIENCE: See Page 34 Signature Douglas J. Collins, FCAS, MAAA Consulting Actuary, Tillinghast Towers Perrin Print Name, Title, and Degree or Credentials Date STATISTICS: See Page 35 Signature Marshall B. Hardy, M.S. Staff Scientist, Applied Research Associates, Inc. Print Name, Title, and Degree or Credentials Date COMPUTER SCIENCE: See Page 36 Signature Brian Grant, M.S. Sr. Computer Scientist, Applied Research Associates, Inc. Print Name, Title, and Degree or Credentials Date See Page 36 Signature Date Applied Research Associates, Inc. 32 May 1, 2004 General Standards

33 Applied Research Associates, Inc. 33 May 1, 2004 General Standards

34 Applied Research Associates, Inc. 34 May 1, 2004 General Standards

35 Applied Research Associates, Inc. 35 May 1, 2004 General Standards

36 Applied Research Associates, Inc. 36 May 1, 2004 General Standards

37 METEOROLOGICAL STANDARDS M-1 Official Hurricane Set* (*Significant Revision) For landfall frequency analyses, the modeler shall use the latest updated Official Storm Set. Updates to HURDAT approved by the Tropical Prediction Center/National Hurricane Center are acceptable modifications to the storm set. The storm set used by ARA to define the hurricane climatology includes all storms given in the HURDAT database. The storm database encompasses the period 1886 through The storm set used by ARA includes all storms defined in the official event set. 1. Describe any deviation from the Official Storm Set. The primary sources of information used to develop the statistical models for describing hurricane risk in the United States are: (i) the HURDAT database, used for developing the models describing storm tracks, heading, occurrence rates and central pressure, (ii) the data given in the publication NWS-38, used for developing the models describing central pressure and radius to maximum winds, and (iii) a database of upper level aircraft measurements provided by the Hurricane Research Division for developing statistical models defining the Holland Profile parameter, B. The development of our hurricane model used all storms in the HURDAT database whether they made landfall in Florida or not, and thus our data set inherently includes all storms in the Hurricane Commission s storm set. Applied Research Associates, Inc. 37 May 1, 2004 Meteorological Standards

38 M-2 Hurricane Characteristics* (*Significant Revision) Methods for depicting all modeled hurricane characteristics, including but not limited to wind speed, radial distributions of wind and pressure, minimum central pressure, radius of maximum winds, strike probabilities, tracks, and the time variant wind fields, shall be based on information documented by currently accepted scientific literature or modeler information accepted by the Commission. The wind speeds associated with a modeled hurricane are estimated using ARA s peer reviewed hurricane wind field model, as described in Vickery, et al. (2000a,b). The following paragraphs summarize key elements. Using ARA s storm track model, the number of storms to be simulated in any one year is obtained by sampling from a negative binomial distribution having a mean value of 8.78 storms/year and a standard deviation of 3.74 storms/year. The starting position, date, time, heading, and translation speed of all tropical storms, as given in the HURDAT database, are sampled and used to initiate the simulation. Using the historical starting positions of the storms (i.e., date and location) ensures that the climatology associated with any seasonal preferences for the point of storm initiation is retained. Given the initial storm heading, speed and intensity, the simulation model estimates the new position and speed of the storm based on the changes in the translation speed and storm heading over the current six-hour period. The changes in the translation speed, c, and storm heading, θ, between times i and i+1 are obtained from, lnc = a1+ a2ψ + a3λ + a4lnci + a5θ i + ε (1a) θ = b + b ψ + b λ + b c + b θ + b θ + ε (1b) i 5 i 6 i 1 where a 1, a 2, etc., are constants, ψ and λ are the storm latitude and longitude, respectively; c i is the storm translation speed at time step i; θ i is the storm heading at time step i; θ i-1 is the heading of the storm at time step i-1; and ε is a random error term. The coefficients a 1, a 2, etc., have been developed using 5-degree by 5-degree grids over the entire Atlantic basin. A different set of coefficients for easterly and westerly headed storms is used. As the simulated storm moves into a different 5-degree by 5-degree square, the coefficients used to define the changes in heading and speed change accordingly. The central pressure of a storm is modeled through the use of a relative intensity parameter, which is coupled to the sea surface temperature. Modeling hurricanes using this relative intensity concept was first used in single point simulations by Darling (1991). Note that while the actual central pressure of a hurricane is a function of more than the sea surface temperature (i.e., wind shear aloft, storm age, depth of warm water, etc.), the modeling approach is an improvement over traditional simulation techniques in that the derived central pressures are bounded by physical constraints, thus eliminating the need to artificially truncate the central pressure distribution. The relative intensity approach is based on the efficiency of a tropical cyclone relative to a Carnot cycle heat engine and the details of the approach given in Darling (1991). To compute the Applied Research Associates, Inc. 38 May 1, 2004 Meteorological Standards

39 relative intensity, I, of a hurricane, we use the mean monthly sea surface temperatures in the Atlantic Basin (given in one-degree squares) at the location of the storm, combined with the central pressure data given in the HURDAT data base (see description in Jarvinen, et al., 1984), an assumed relative humidity of 0.75, and a temperature at the bottom of the stratosphere taken to be equal to 203 o K (Emanuel, 1988). Using the approach given in Darling (1991), every central pressure measurement given in HURDAT is converted to a relative intensity. During the hurricane simulation process, the values of I at each time step are obtained from, ln( Ii+ 1) = c0 + c1ln( Ii) + c2 ln( Ii 1) + c3ln( Ii 2) + c4ts + c5( Ts Ts ) + ε (2) 1 The coefficients c 0, c 1, etc., vary with storm latitude, storm intensity, basin (i.e., Gulf of Mexico or Atlantic Ocean), and heading (i.e., Easterly or Westerly direction). Near the United States coastline, where more continuous pressure data is available, finer, regionally specific values of these coefficients are developed. These regionally specific coefficients take into account changes in the relationships between sea surface temperature and storm intensity that may be influenced by subsurface water temperatures as described, for example, in Chouinard, et al. (1997). These regional coefficients preserve the variations in local hurricane climatology along the coastline, and through small adjustments in the coefficients, the model can be calibrated to match historical landfall rates of hurricanes. In the modeling process, once a simulated storm makes landfall, the reduction in central pressure with time is modeled using the filling models described in Vickery and Twisdale (1995a). If a storm moves back over water, Equation 2 is again used to model the variation in central pressure with time. The radius to maximum winds (R max given in statute miles) is modeled using: 2 ln R max = ϕ p + ε ; r 2 =0.325, ε = (3) where the error term, ε, is normally distributed. Equation (3) is an update of the R max model given in Vickery, et al. (2000b). The updates include the addition of recent intense storms (such as Floyd, 1999, and Mitch, 1998). The modeling of the Holland pressure profile parameter, B, is unchanged from that given in Vickery, et al. (2000b). 1. Identify the hurricane characteristics (e.g., central pressure or radius of maximum winds) that are used in the model. The characteristics of the hurricane that are needed to produce an estimate of wind speed at a site are as follows: (i) Central Pressure Difference (ii) Holland Pressure Profile Parameter (iii) Radius to Maximum Winds (iv) Storm Translation Speed (v) Storm Track (vi) Latitude and Longitude of Site (vii) Surface Roughness at the Site (viii) Distance of the Site from the Coast. i+ i Applied Research Associates, Inc. 39 May 1, 2004 Meteorological Standards

40 2. Describe the dependencies among variables in the wind field component and how they are represented in the model. The storm central pressure is modeled as a function of sea surface temperature. The storm track, which defines storm heading, translation speed, and distance to site, is a function of latitude and longitude. The radius to maximum winds and the Holland pressure profile parameter are a function of central pressure and latitude, as described in Vickery, et al. (2000b). 3. Describe the process for converting gradient winds to surface winds including the treatment of the associated uncertainties. Explain how the wind speeds generated in the wind field model were converted from sustained to gust and identify the averaging time. ARA s windfield model is valid for any averaging time and height above ground. The windfield model has been validated using both peak gust wind speeds and wind speeds averaged over ten minutes. In the validation studies, all measured wind speed data were adjusted (when needed) to a height of 10 m above ground (in local terrain conditions) before wind speed comparisons were performed. The basic output of the model, for use in loss estimation, is the peak gust windspeed. 4. Describe how the asymmetric nature of hurricanes is considered in the model. Asymmetries are modeled in the hurricane wind field as described in Vickery, et al., (2000) and in our response to Standard M Describe the stochastic hurricane tracks and discuss their appropriateness. Describe the historical data used as the basis for the model s hurricane tracks. The modeling of the hurricane tracks in our model is described in detail in Vickery, et al. (2000b). The approach models a storm track beginning with its initial development over the ocean and ending with its final dissipation over land or in the open ocean. The approach has been validated as described in Vickery, et al. (2000b) through comparisons of simulated and observed key storm statistics along the coast of the United States. The development of the storm track model used the historical data given in the HURDAT database, encompassing the period 1886 through Describe how the coastline is segmented (or partitioned) in determining the parameters for hurricane frequency used in the model. Provide the hurricane frequency distribution by intensity for each segment. The model does not use a coastline segment approach to define hurricane parameters. The modeling process is described in detail in Vickery, et al. (2000). 7. For hurricane characteristics modeled as random variables, describe the probability distributions. The hurricane simulation model generates a time series of storms in the Atlantic Basin simulating a period of 300,000 years. The storm generation and track modeling technique used to generate this time series of storms is described in the answer to the general description of the model and in Vickery, et al. (2000b). Applied Research Associates, Inc. 40 May 1, 2004 Meteorological Standards

41 M-3 Landfall Intensity Models shall use maximum one-minute sustained 10-meter wind speed when defining hurricane landfall intensity. This applies both to the Official Storm Set used to develop landfall strike probabilities as a function of coastal location and to the modeled winds in each hurricane which causes damage. The associated maximum one-minute sustained 10-meter wind speed shall be within the range of wind speeds (in statute miles per hour) categorized by the Saffir-Simpson scale. Saffir-Simpson Hurricane Scale (for displayed parameters): Category Winds (mph) Central Pressure (mb) Damage > 980 Minimal Moderate Extensive Extreme 5 Over 155 < 920 Catastrophic The storm intensity at the time of landfall (or any other time), as defined by the Saffir- Simpson scale, can be computed using either the one minute sustained wind speed or the central pressure. All comparisons of storm intensity presented herein are based on the one minute sustained wind speed at a height of 10 meters. 1. Define an event in the model. Describe how the model handles events with multiple landfalls and by-passing hurricanes. An event is defined as: (i) any simulated hurricane that makes landfall in Florida, or (ii) a bypassing hurricane that produces peak gust wind speeds on land of at least 50 mph anywhere in Florida. The effect of changing the definition of an event on estimates of average annual loss will be shown to the professional team. The computation of loss occurs when the peak gust wind speed equals or exceeds 50 mph. ARA s hurricane model simulates the entire track of a hurricane or tropical storm, and thus storms with multiple landfalls are inherently included in the modeling process. Storms making multiple landfalls are defined as a single event. 2. Provide the upper limit of wind speeds (maximum one-minute average wind at 10- meters height) per hurricane category (defined by the Saffir-Simpson scale wind speed) that the model produces. The maximum wind speeds produced by the model in each of the five categories are shown below. The maximum wind speeds of category 1 through 4 storms are based upon the Saffir- Simpson scale, and the maximum sustained wind speed associated with the category 5 storm has been derived from the maximum peak gust wind speed produced in the simulation of Florida hurricanes. Applied Research Associates, Inc. 41 May 1, 2004 Meteorological Standards

42 Category Maximum Sustained Wind Speed (mph) >155 Applied Research Associates, Inc. 42 May 1, 2004 Meteorological Standards

43 M-4 Hurricane Probabilities A. Modeled probability distributions for hurricane intensity, eye diameter, forward speed, radii for maximum winds, and radii for hurricane force winds shall be consistent with historical hurricanes in the Atlantic basin. B. Modeled hurricane probabilities shall reasonably reflect the historical record through 2002 for category 1 to 5 hurricanes and shall be consistent with those observed for each coastal segment of Florida and neighboring states (Alabama, Georgia, and Mississippi). The modeled probability distributions of hurricane strength, forward speed, radii to maximum winds are consistent with that derived from historical storms in the Atlantic Basin. The data used to derive the statistical models include the publication NWS-38, the HURDAT data base, and annual updates to the HURDAT database available from the National Hurricane Center/Tropical Prediction Center web site. Additional information on recent storms has been obtained from the Hurricane Research Division web site as well as detailed analyses of storms as published in AMS Journals including, Monthly Weather Review, Weather and Forecasting, etc. The resulting statistical models for radii to maximum winds, etc., used in the ARA model are an update of the models published in accepted scientific literature in Vickery and Twisdale (1995b) and Vickery, et al. (2000b). No database exists describing historical records of radii of hurricane force winds. 1. Describe the source documents and any additional research that was performed to develop the model s variable functions or databases. Describe all such information, including a description of the historical database(s), for the model s hurricane wind speeds and hurricane frequencies. The primary sources of information used to develop the statistical models for describing hurricane risk in the United States are: (i) the HURDAT database, used for developing the models describing storm tracks, heading, occurrence rates and central pressure; (ii) the data given in the publication NWS-38, used for developing the models describing central pressure and radius to maximum winds; (iii) a database of upper level aircraft measurements provided by the Hurricane Research Division for developing statistical models defining the Holland Profile parameter, B; and (iv) Navy database of monthly mean sea surface temperatures. The development of our hurricane model used all storms in the HURDAT database whether they made landfall in Florida or not, and thus our data set inherently includes all storms in the Hurricane Commissions storm set. 2. List any assumptions used in creating any of these databases. No assumptions were made in the use or development of the above databases. With respect to hurricane intensity modeling, which is modeled as function of sea surface temperature, we have developed the model with the assumption that the outflow temperature is constant having a value of 203 degrees Kelvin and a constant relative humidity of Applied Research Associates, Inc. 43 May 1, 2004 Meteorological Standards

44 3. Provide vertical bar graphs depicting distributions of hurricane frequencies by category by region (Figure 9). Modeled probabilities of landfalling hurricanes by region are consistent with the limited observed (or historical) data. Figure 6 presents comparisons of modeled and observed landfall counts of hurricanes as a function of Saffir-Simpson category, as defined by wind speed. Data are shown as a function of region and for Florida as a whole. The agreement between the modeled and historical rates of landfalling storms as a function of intensity and region is reasonable, with the differences between historical and modeled results falling well within the range of uncertainty associated with the limited number of historical observations of landfalling hurricanes. Landfalling storms depicted for the four Florida regions in Figure 6 include both entering and exiting storms, as defined in the Official Storm Set. The graphs for Mississippi and Alabama (top left) and Georgia (bottom right) include all entering storms only. The intensities of historical storms and the landfall region have been obtained directly from the information given in the Official Storm Set. Applied Research Associates, Inc. 44 May 10, 2004 Meteorological Standards

45 MISSISSIPPI and ALABAMA - All Entering Storms NORTHWEST FLORIDA - All Entering and Exiting Storms Count Model Data Storm Category Count Model Data Storm Category WEST FLORIDA - All Entering and Exiting Storms SOUTH EAST FLORIDA - All Entering and Exiting Storms Count Model Data Storm Category Count Model Data Storm Category NORTH EAST FLORIDA -All Entering and Exiting Storms GEORGIA - All Entering Storms Count Model Data Storm Category Count Model Data Storm Category Figure 6. Comparison of Modeled and Observed Landfalling Counts of Hurricanes by Category (defined by wind speed) and Region 4. Provide a completed Form M-1, Annual Occurrence Rates. See Form M-1. Applied Research Associates, Inc. 45 May 10, 2004 Meteorological Standards

46 M-5 Land Friction and Weakening* A. *The magnitude of land friction coefficients shall be consistent with currently accepted scientific literature, consistent with geographic surface roughness, and implemented with appropriate geographic information system data. (*Significant Revision) B. The hurricane overland weakening rate methodology used by the model shall be reasonable in comparison to historical records. The effect of land friction is treated in the ARA hurricane wind field model through the use of widely accepted wind engineering boundary layer models. The wind speeds near the ground are reduced using the approach described in Vickery, et al. (2000a). The largest, and most rapid, portion of the reduction in wind speeds associated with a hurricane making landfall is produced by the ground friction, which is modeled using the ESDU (1983) boundary layer models. The approach used to model the effects of ground friction in the ARA model has been validated through comparisons of measured and modeled wind speeds from hurricanes taken offshore, at the coast, and inland from the coast. The database used to derive the ground roughness is based on the Florida Water Management District s Land Use Land Cover database. ARA s hurricane model uses the filling rates developed by Vickery and Twisdale (1995a) to describe the rate of weakening of a storm (as defined by the increase in central pressure) upon making landfall. The use of this peer reviewed filling model, coupled with the ESDU model used to model the reduction in wind speed associated with friction, produce reductions in wind speed within 20% of the Kaplan/DeMaria model. 1. Describe the decay rates or hurricane degradation assumptions used by the model after the hurricane makes landfall. Describe how far inland hurricane force winds are estimated for different category events (as defined by wind speed in the Saffir-Simpson scale). Describe any variations in the decay rate. Once a simulated storm makes landfall, the central pressure difference, p, is decreased as the storm fills (or weakens). The filling of the storm is modeled using the approach described in Vickery and Twisdale (1995a), where the central pressure difference at any time after landfall, p(t), is given as: p( t) = p0 exp( at) where p o is the central pressure difference at the time of landfall and a is a filling parameter, which is a function of the storm intensity at the time of landfall as well as the location (or region) of landfall, and t is the time in hours since the center of the storm crossed land. Three different representations of the filling parameter, a, are used, one for the Gulf of Mexico, one for the Florida Peninsula, and one for the Atlantic Coast. In performing studies for locations in Florida, only the first two filling model regions (different representations of the filling parameter, a) are applicable. In addition to the filling associated with the increase in the storm central pressure (or Applied Research Associates, Inc. 46 May 1, 2004 Meteorological Standards

47 reduction in p), the wind speed at ground level is immediately decreased at the coastline because of frictional effects. This immediate decrease in wind speed is followed by a gradual decrease in wind speed at the surface associated with a change in the boundary layer characteristics of the storm in the region of the eyewall. The reduction in wind speeds associated with changes in the hurricane wind field following landfall is described in Vickery, et al. (2000a). Additional changes in the characteristics of the hurricane following landfall include a decrease in the value of the Holland profile parameter, B, and an increase in the radius to maximum winds, R max, with both changes being brought about because both B and R max are correlated with central pressure and/or latitude, both of which continue to change after landfall. How far inland hurricane force winds penetrate, given the landfall of a storm of different intensities, will vary significantly with the storm characteristics. These storm characteristics include translation speed, the Holland B parameter, R max, p o, orientation of the storm with respect to the coastline, air-sea temperature difference, and the location of landfall. In the example results presented in Table 1, the following assumptions have been made: (i) The initial wind speed represents the average value of the storm category computed for over water conditions (i.e., one minute average wind speed computed at a height of 10 m above the water level). (ii) The value of central pressure, p o, used to arrive at the above noted wind speeds are computed using the median value of R max associated with p o for a latitude of 28 degrees N, and the mean value of the Holland profile parameter, B, associated with the given values of R max and p o. (iii) The overland wind speeds used to define the hurricane intensity are based on the modeled one minute average wind speeds at a height of 10 m above ground in open terrain. (iv) The air-sea temperature difference at the time of land fall is zero. (v) The storm is moving at a speed of 15 mph (a representative value for the Florida area). Table 1. Inland Distance of Hurricane Force Winds Category Distance of Hurricane Force Winds Inland (miles) South Florida Filling Model Gulf of Mexico Filling Model Identify other variables that affect the wind speed estimation (e.g., surface roughness, topography, etc.). Given a hurricane with fixed values of central pressure, heading, position, etc., and a site located on land, the variable which has the greatest impact on wind speed is the local ground roughness. Other variables having minor effects include distance from the coast, distance from the center of the storm, and the air-sea temperature difference. Applied Research Associates, Inc. 47 May 1, 2004 Meteorological Standards

48 3. Provide the collection and publication dates of the land use and land cover data used in the model. The database used for determining the surface roughness was obtained from the five Florida Water Management Districts (FWMD) that serve Florida, as noted in Figure 7. The FWMD Land-Use Land Cover databases from these agencies are considered superior sources for Florida terrain information due to higher spatial and land use resolution than the USGS LULC data. In addition, the FWMD data are based on more recent imagery. Given the LULC information, the aerodynamic surface roughness length, z o, associated with a given LULC category has been assigned a value, based on wind engineering expertise and judgment, coupled with sample aerial photography. The FWMD LULC data sets are based on the National Aerial Photography Program s infrared imagery collected in The processed data sets were published by the districts in 1997 and SRWMD NWFWMD SJRWMD SWFWMD SFWMD Figure 7. The Five Florida Water Management Districts 4. Provide a graphic representation of the modeled degradation rates for Florida storms over time compared to the Kaplan-DeMaria decay rate. Include curves for +/- 20% of the Kaplan-DeMaria values. Comparisons of degradation rates with Kaplan-DeMaria are given in Figure 8. Applied Research Associates, Inc. 48 May 1, 2004 Meteorological Standards

49 Sustained Wind Speed (mph) Hurricane Erin - Wind Speed Reduction with Time -20% Kaplan-DeMaria Mean 20% Erin Simulation Time After Land Fall (hrs) Sustained Wind Speed (mph) Hurricane Opal - Wind Speed Reduction with Time -20% Kaplan-DeMaria Mean 20% Opal Simulation Time After Land Fall (hrs) Sustained Wind Speed (mph) Hurricane Andrew - Wind Speed Reduction with Time -20% Kaplan-DeMaria Mean 20% Andrew Simulation Time After Land Fall (hrs) Figure 8. Example Comparisons of Modeled Degradation Rates to Kaplan-Demaria Decay Rates Applied Research Associates, Inc. 49 May 1, 2004 Meteorological Standards

50 M-6 Logical Relationships of Hurricane Characteristics A. The radius of maximum winds shall reflect historical hurricane characteristics. B. The magnitude of asymmetry shall increase as the translation speed increases, all other factors held constant. C. The wind speed shall decrease with increasing surface roughness (friction), all other factors held constant. The hurricane wind field characteristics, including the radial distribution of wind speeds, storm symmetry characteristics, and reduction in wind speed as a function of surface friction, are consistent with accepted scientific principles. The radial distribution of wind speeds has been validated thorough comparisons of model and observed wind speeds compared throughout the entire passage of multiple storms and at multiple anemometer sites. The effect of land friction is modeled using the ESDU terrain roughness model, and the effect of storm symmetry is treated using the approach described in Vickery, et al. (2000a). 1. Provide a completed Form M-2, Radius of Maximum Winds. See Form M Provide a completed Form M-3, Maps of Maximum Winds. See Form M-3. Applied Research Associates, Inc. 50 May 1, 2004 Meteorological Standards

51 Form M-1: Annual Occurrence Rates Provide annual occurrence rates for landfall from the data set that the model generates by hurricane category (defined by wind speed in the Saffir-Simpson scale) for the entire state of Florida and selected regions as defined in Figure 9. List the annual occurrence rate (probability of an event in a given year) per hurricane category. The historical frequencies below have been derived from the Commission s Official Storm Set. If hurricanes are used in addition to the Official Storm Set as specified in Standard M-1, then the historical frequencies should be modified. Modeled Annual Occurrence Rates Entire State Region A NW Florida Region B SW Florida Category Historical Modeled Historical Modeled Historical Modeled Region C SE Florida Region D NE Florida By-Passing Storms Category Historical Modeled Historical Modeled Historical Modeled *Round to 2 decimal places Note: Number of Hurricanes does not include By-Passing Storms Applied Research Associates, Inc. 51 May 1, 2004 Meteorological Standards

52 Figure 9. Region Definitions Applied Research Associates, Inc. 52 May 1, 2004 Meteorological Standards

53 Form M-2: Radius of Maximum Winds Provide ranges for radius of maximum winds used by the model (viz., the stochastic storm set) for the central pressures provided in the table below. Provide a graphical representation of the Rmax (x-axis) versus Central Pressure (y-axis). Central Pressure (mbar) Median Mean Std Dev 5th 95th The ranges provided are the results of simulations performed using the statistical models for R max and the Holland pressure profile parameter described earlier. For each central pressure value given in the following table (far field pressure = 1013 mbar in all cases), 1000 simulations were performed for a storm located at a latitude of 27 degrees North, with the translation speed sampled for a location in the mid point of the state. From each of the 1000 simulations for each central pressure value, unique values of R max were produced, yielding a total of 1000 estimates of R max for each value of central pressure. The median, mean, standard deviation, and the 5 th and 95 th percentile values are provided. A graphical representation of the data is provided in Figure 10. Applied Research Associates, Inc. 53 May 1, 2004 Meteorological Standards

54 CP( mbar) R max (mi) Figure 10. Range of Simulated R max vs. Central Pressure (values shown are the 5 th percentile, mean, and 95 th percentile) Applied Research Associates, Inc. 54 May 1, 2004 Meteorological Standards

55 Form M-3: Maps of Maximum Winds Provide a color map of the maximum winds at the ZIP Code level for the modeled version of the Official Storm Set. Provide a color map of the maximum winds at the ZIP Code level for a 100-year return period from the stochastic storm set. Maximum winds in these maps are defined as the maximum one-minute sustained winds as recorded at each location. Figure 11 shows the maximum wind speeds (one minute sustained at a height of 10 m in open terrain, at the ZIP-Code centroid) for the historical storm set. The historical storms were modeled such that the maximum sustained wind speed (10 m above ground, over water) at the time of landfall matches the maximum wind speed as given in the official storm set. If central pressure data is given in HURDAT, the central pressure as a function of time after landfall uses the HURDAT data. If central pressure data is not given in HURDAT, the central pressure after landfall is modeled using the filling models given in Vickery and Twisdale (1995a). The radius to maximum winds is modeled using information obtained from NWS-38 or the Hurricane Research Division of NOAA. If no radius to maximum wind data is available, then R max is modeled using the regression model used in the stochastic storm modeling. Figure 12 presents the maximum sustained windspeeds for a 100 year return period from the full 300,000 year simulation. Maximum Sustained (mph) <= Figure 11. Maximum One Minute Sustained Wind Speeds for the Historical Storm Set Applied Research Associates, Inc. 55 May 1, 2004 Meteorological Standards

56 100year Sustained Wind Speed <= / Figure Year Return Period Sustained Wind Speeds at a Height of 10 m Above Ground Applied Research Associates, Inc. 56 May 1, 2004 Meteorological Standards

57 VULNERABILITY STANDARDS V-1 Derivation of Vulnerability Functions* A. Development of the vulnerability functions is to be based on a combination of the following: (1) historical data, (2) tests, (3) structural calculations, (4) expert opinion, or (5) site inspections. Any development of the vulnerability functions based on structural calculations or expert opinion shall be supported by tests, site inspections, or historical data. B. The method of derivation of the vulnerability functions shall be theoretically sound. C. Any modification factors/functions to the vulnerability functions or structural characteristics and their corresponding effects shall be clearly defined and be theoretically sound. D. Construction type and construction characteristics shall be used in the derivation and application of vulnerability functions. E. In the derivation and application of vulnerability functions, assumptions concerning building code revisions and building code enforcement shall be reasonable and be theoretically sound. (*Significant Revision) F. Vulnerability functions shall be separately derived for building structures, mobile homes, appurtenant structures, contents, and additional living expense. G. The minimum wind speed that generates damage shall be reasonable. A. ARA s vulnerability functions include elements of each of the following: (1) historical data, (2) tests, (3) structural calculations, (4) expert judgment, and (5) site inspections. The ARA personnel responsible for the development of the damage and loss models have extensive experience in wind load modeling, structural analysis, post-hurricane damage surveys, and meteorology. B. ARA's vulnerability functions have been developed for residential buildings (including mobile homes) using a theoretically sound load and resistance modeling approach, coupled with empirically-derived elements. For each structure, the model estimates physical damage to the building. This physical damage is used in turn to estimate the loss to the building and contents separately. The building damage state is used in conjunction with a restoration model to estimate the costs associated with additional living expenses. C. No modification factors/functions are applied to the vulnerability functions or structural characteristics. Applied Research Associates, Inc. 57 May 1, 2004 Vulnerability Standards

58 D. The assumptions related to construction types and construction characteristics are based on multiple datasets and engineering judgment. These data and assumptions will be discussed with the professional team. An extensive set of loss functions has been developed for modeling the effects of the new Florida Building Code. However, because the new code has been in effect for only one year, the new curves are currently given zero weight in the default building stock model. While ARA s building performance modeling approach allows treatment of certain building code and enforcement issues, the loss projection model does not require separate inputs on building codes/enforcement. E. Building code enforcement is not considered per se in the model. Specific construction characteristics are treated by evaluating the effects through the building performance model. The building performance model is run with specific building class inputs to generate a loss function for that class (e.g., hip, hurricane straps, no shutters, etc.). If required, quality of construction is treated through the application of resistance factors to the nominal resistance of various building components. Validations have been performed through comparisons of modeled and observed physical damage states. F. ARA s vulnerability functions separately compute damages for building structures, mobile homes, appurtenant structures, contents, and additional living expense. Our current version of the appurtenant structures vulnerability function is modeled as a ratio of the building vulnerability. This simplistic function is used since: (1) the true value and type of appurtenant structure is usually unknown, (2) the contribution of the appurtenant coverage to the AAL is small, and thus errors introduced through this approximation is small, and (3) insurer documentation of appurtenant structure loss is usually poor. Example comparisons of modeled and observed appurtenant structure losses for those cases where reliable data are available will be shown to the professional team. G. ARA s vulnerability functions produce damage for windspeeds above and below the hurricane threshold of 74 mph. The minimum peak gust windspeed that produces damage is about 50 mph. 1. Provide a flow chart documenting the process by which the vulnerability functions are derived and implemented. Figure 13 shows the flow chart describing the approach used to develop the damage and loss functions. Applied Research Associates, Inc. 58 May 1, 2004 Vulnerability Standards

59 Figure 13. Flow Chart for Development of Building Vulnerability Functions 2. Describe the nature and extent of actual insurance claims data used to develop the model s vulnerability functions. Describe in detail what is included, such as, number of policies, number of insurers, and number of units of dollar exposure, separated into personal lines, commercial, and mobile home. ARA has used insurance loss data from Hurricane Andrew (3 insurers), Hurricane Hugo (2 insurers), Hurricane Georges (1 insurer), Hurricane Fran (1 insurer), Hurricane Erin (1 insurer), Hurricane Bertha (1 insurer), Hurricane Bonnie (1 insurer), Hurricane Earl (1 insurer), and Hurricane Opal (1 insurer). In all 12 cases, personal lines data were available. Mobile home data were available for one storm-insurer combination. No commercial data associated with these records have been examined. Due to non-disclosure agreements with the insurance companies, additional details cannot be disclosed. 3. Summarize site inspections, including the source, and a brief description of the resulting use of these data in development, validation, or verification of vulnerability functions. ARA has used information on building characteristics collected as a part of the Residential Construction Mitigation Program to determine distribution of the wind resistive characteristics of houses constructed in Florida. The details of the data collection and its use in the model are described in the March 2002 Florida Department of Community Affairs report entitled Development of Loss Relativities for Wind Resistive Features of Residential Structures. 4. List the primary documents or the research results used in the development of the model s vulnerability functions. ARA engineers and scientists with significant experience in wind engineering, building performance, and post storm damage surveys developed the vulnerability functions. Examples of some of the reports used directly in the development of the ARA damage and loss models include Stathopoulos (1979), Meecham (1988), FEMA (1992), Ho (1992), Crandall, et al. Applied Research Associates, Inc. 59 May 1, 2004 Vulnerability Standards

60 (1993), Cunningham (1993), Sparks, et al. (1994), Monroe (1996), Reed, et al. (1996, 1997), Uematsu and Isyumov (1998), and Twisdale, et al. (1996, 1998). ARA engineers have performed post storm damage surveys following Hurricanes Andrew, Erin, Opal, Bertha, Fran and Bonnie. ARA engineers have also participated on FEMA Building Performance Assessment Teams (BPATs). In part because of our proven expertise in damage model development and validation, ARA was selected by a panel of wind engineering and meteorology experts to develop FEMA s HAZUS model for wind loss estimation. 5. Describe the number of categories of the different vulnerability functions. Specifically, include descriptions of the structure types, lines of business, and coverages in which a unique vulnerability function is used. The number of categories of different vulnerability functions used in a loss projection study depends on the objective of the study. For a basic analysis, wall construction is a common way to analyze and report results since insurers have wall construction classes. On the other hand, to develop a classification for wind vulnerability, many key building variables may be evaluated. In summary, the vulnerability functions may be based on either fine grained or coarse grained representations of the construction parameters. Examples of the number of categories or building classes considered in loss projection studies will be reviewed with the professional team. The basis for differentiation is the building performance model, which uses engineering analysis, empirical data, and judgment. The building categories used in the model are built up from detailed engineering load and resistance models that take into account building shape, roof shape, roof cover, garage doors, roof-wall connection, sheathing attachment, etc. For each building, damage and loss are estimated for the building, appurtenances, contents, and loss of use. Once the losses have been generated, fast running vulnerability (loss) functions are developed for the different coverages, etc., for each class. In this manner, the appropriate building vulnerability information is captured in the respective vulnerability functions used in the loss projection. 6. Identify the one-minute average sustained wind speed at which the model begins to estimate damage. The damage model is initiated when the peak gust wind speed at a height of 10 m in open terrain exceeds 50 mph. This peak gust value corresponds to a sustained wind speed (one minute average wind speed at a height of 10 m in open terrain) of about 40 mph. 7. Describe how the duration of wind speeds at a particular location over the life of a hurricane is considered. Duration of wind speeds is modeled in the load and resistance model via the cumulative damage algorithm illustrated in Figure 13. The fast-running damage functions represent the expected damage as a function of the peak gust wind speed averaged over many storms of varying durations. 8. Provide a completed Form V-1, One Hypothetical Event. See Form V-1. Applied Research Associates, Inc. 60 May 1, 2004 Vulnerability Standards

61 V-2 Mitigation Measures* (*Significant Revision due to Form V-2) A. Modeling of mitigation measures to improve a building s wind resistance and the corresponding effects on vulnerability shall be theoretically sound. These measures shall include fixtures or construction techniques that enhance: Roof strength Roof covering performance Roof-to-wall strength Wall-to-floor-to-foundation strength Opening protection Window, door, and skylight strength. B. Application of mitigation measures shall be reasonable both individually and in combination. ARA s vulnerability model was originally developed to treat the wind resistive characteristics of buildings, including fixtures or construction techniques that reduce losses. The methods used for estimating the effects of individual and multiple mitigation measures are reasonable. Details of the model will be disclosed to the professional team. The percentage changes in zero deductible personal residential non-mitigated damage for each of the requested mitigation measures are provided in Form V Provide a completed Form V-2, Mitigation Measures Range of Changes in Damage. See Form V-2. Applied Research Associates, Inc. 61 May 1, 2004 Vulnerability Standards

62 Form V-1: One Hypothetical Event Wind speeds for 336 ZIP Codes are provided in the file named FormV1Input03.xls. The wind speeds and ZIP Codes represent a hypothetical hurricane track. The modeler is instructed to model the sample exposure data provided in the file named FormsExposureInput03.xls against these wind speeds at the specified ZIP Codes and provide the damage ratios summarized by wind speed (mph) and construction type. The wind speeds provided are one-minute sustained 10-meter wind speeds. The sample exposure data provided consists of three structures (one of each construction type wood frame, masonry, and mobile home) individually placed at the population centroid of each of the ZIP Codes provided. Each ZIP Code is subjected to a specific wind speed. For completing Part A, Estimated Damage for each individual wind speed range is the sum of loss to all buildings in the ZIP Codes subjected to that individual wind speed range. Subject Exposure is all exposures in the ZIP Codes subjected to that individual wind speed range. For completing Part B, Estimated Damage is the sum of the loss to all buildings of a specific type (wood frame, masonry, or mobile home) in all of the wind speed ranges. Subject Exposure is all exposures of that specific type in all of the ZIP Codes. One base structure for each of the construction types should be placed at the population center of the ZIP Codes. Base Frame Structure: One story Unbraced gable end roof Normal shingles (55mph) ½ plywood deck 6d nails, deck to roof members Toe nail truss to wall anchor Wood framed exterior walls Nails for wall/floor/foundation connections No shutters Standard glass windows No door covers No skylight covers Constructed in 1980 Base Masonry Structure: One story Unbraced gable end roof Normal shingles (55mph) ½ plywood deck 6d nails, deck to roof members Toe nail truss to wall anchor Masonry exterior walls No vertical wall reinforcing No shutters Standard glass windows No door covers No skylight covers Constructed in 1980 Base Mobile Home Structure: Tie downs Single unit If additional assumptions are necessary to complete this Form (for example, regarding duration), the modeler should provide the reasons why the assumptions were necessary as well as a detailed description of how they were included. Provide a plot of the Form V-1 Part A data. Applied Research Associates, Inc. 62 May 1, 2004 Vulnerability Standards

63 Form V-1: One Hypothetical Event Part A Wind Speed (mph) Estimated Damage/ Subject Exposure % % % % % % % % % % % % % Part B Construction Type Estimated Damage/ Subject Exposure Wood Frame 1.31% Masonry 1.26% Mobile Home 3.33% A plot of the Part A data is provided in Figure 14. Applied Research Associates, Inc. 63 May 1, 2004 Vulnerability Standards

64 80% Total Loss / Subject Exposure 70% 60% 50% 40% 30% 20% 10% 0% Sustained Wind Speed (mph) Figure 14. Losses vs. Wind Speed for Form V-1, Part A Applied Research Associates, Inc. 64 May 1, 2004 Vulnerability Standards

65 Form V-2: Mitigation Measures Range of Changes in Damage Provide the percentage change in the zero deductible personal residential non-mitigated damage due to each mitigation measure listed in Form V-2. These mitigation measures are the minimum required to be documented. Adding measures to this list is encouraged. If additional assumptions are necessary to complete this Form (for example, regarding duration), the modeler should provide the reasons why the assumptions were necessary as well as a detailed description of how they were included. Provide this Form on CD-ROM in both Excel and PDF format. The file name should include the abbreviated name of the modeler, the Standards year, and the Form name. Base structures for frame and masonry are as defined in Form V-1. Base structures are $100,000 fully insured structures with a zero deductible policy. Required ZIP Codes: 32226, Duval County ( mph wind range) 32308, Leon County ( mph wind range) 32617, Marion County ( mph wind range) 33140, Miami-Dade County ( mph wind range) 34110, Collier County ( mph wind range) Place the base structure at the population centroid for the five required ZIP Codes. Wind speeds (one-minute sustained 10-meter) start at 50 mph and vary at intervals of 25 mph or less up to the maximum wind speed indicated in the range above. Individual mitigation measures should be added one at a time to each base structure. The change to the damage for each mitigation measure should be determined as a percentage with the maximum and minimum value from the required ZIP Codes. The minimum and maximum percentage reductions in expected ground-up building losses for mitigated frame and masonry structures are provided in Form V-2. The reductions in losses were computed for the five ZIP Codes specified above and their associated wind speed ranges (in 25 mph intervals). The ranges represent the smallest and largest reductions in ground-up building losses for the 24 cases considered. In general, the mitigation measures were applied one-at-a-time to the base structure (i.e., the combined effects of multiple mitigation measures are not shown in Form V-2). Exceptions were made for door covers, which were applied in conjunction with engineered window protection, and skylight covers, which were applied in conjunction with engineered window and door protection. It is unlikely that door and/or skylight protection would be applied to a structure without also applying window protection. The model produces the same estimates of building damage for owners, renters, or condo unit owners. Applied Research Associates, Inc. 65 May 1, 2004 Vulnerability Standards

66 In some instances, the largest percentage reductions occurred at the lower end of the wind speed range, where small reductions in loss due to mitigation were divided by small losses for the base for the base structure. For example, the largest percentage changes in damage for normal shingles occurred in the 75 mph wind speed case, where the expected losses for the base structure are only 1 to 2%. Applied Research Associates, Inc. 66 May 1, 2004 Vulnerability Standards

67 Form V-2: Mitigation Measures Range of Changes in Damage MITIGATION OWNERS RENTERS CONDO UNIT OWNERS FRAME MASONRY FRAME MASONRY FRAME MASONRY MEASURES LOW HIGH LOW HIGH LOW HIGH LOW HIGH LOW HIGH LOW HIGH ROOF STRENGTH UNBRACED GABLE ENDS BRACED GABLE ENDS HIP ROOF ROOF COVERING ROOF-TO-WALL STRENGTH NORMAL SHINGLE (55 mph) RATED SHINGLE (110 mph) MEMBRANE NAILING 6d OF DECK 8d 6/ / TOE NAILS CLIPS STRAPS Double Wrap Straps WALL-FLOOR- FOUNDATION STRENGTH NAILS TIES/CLIPS STRAPS OPENING PROTECTION SHUTTERS NONE PLYWOOD STEEL ENGINEERED W NDOW, DOOR, & SKYLIGHT STRENGTH WINDOWS STD GLASS LAMINATED IMPACT GLASS NO DOOR COVER DOOR COVER NO SKYLIGHT COVER SKYLIGHT COVER Applied Research Associates, Inc. 67 May 1, 2004 Vulnerability Standards

68 Actuarial Standards A-1 Underwriting Assumptions A. When used in the modeling process or for verification purposes, adjustments, edits, inclusions, or deletions to insurance company input data used by the modeler shall be based upon accepted actuarial, underwriting, and statistical procedures. B. For loss cost estimates derived from or validated with historical insured hurricane losses, the assumptions in the derivations concerning (1) construction characteristics, (2) policy provisions, (3) claim payment practices, and (4) relevant underwriting practices underlying those losses, as well as any actuarial modifications, shall be reasonable and appropriate. The amount and quality of insurance information on historical losses varies significantly. The assumptions involved in the comparisons of historical insured hurricane losses to model estimated losses are documented as part of each study. Any adjustments are based upon accepted actuarial, underwriting, and statistical procedures. Sensitivity analyses are often used to assess how certain assumptions affect the estimated losses to ensure the reasonableness of comparisons. 1. Describe how the model treats the definition of an event from an insurance policy perspective. Although ARA s hurricane model is able to compute the time of landfall, or time of loss, this information is not currently used, thus a single storm which makes multiple landfalls is treated as a single event. 2. Identify the assumptions used to develop loss costs for unknown residential construction types. The unknown vulnerability curve varies with region in Florida. The vulnerability curve is a composite that reproduces the proportion of each construction type which is prevalent in a given region. The unknown vulnerability curves do not include mobile homes. 3. Describe how the modeled loss costs take into consideration storm surge and flood damage to the infrastructure. The impact of storm surge and flood damage is treated in losses associated with ALE. Storm surge and flood damage to the infrastructure does not impact building, content, or appurtenant structure losses. The only input required for storm surge and flood damage to the infrastructure is the ALE limit. Applied Research Associates, Inc. 68 May 1, 2004 Actuarial Standards

69 4. Describe the assumptions included in model development and validation concerning insurance company claim payment practices. ARA s loss model is developed using a cost restoration model to estimate the repair costs given physical damage and thus the major assumption within the model are the threshold values used to determine if components (e.g. roof cover) are completely replaced rather than repaired. Details of these assumptions will be provided to the Professional Team. 5. Identify depreciation assumptions and describe the methods and assumptions used to reduce insured losses on account of depreciation. Provide a sample calculation for determining the amount of depreciation and the actual cash value (ACV) losses. The loss costs estimates do not include any assumptions with respect to depreciation. The validation of the model with actual insurance data indicates that this assumption is valid. 6. Identify property value assumptions and describe the methods and assumptions used to determine the true property value and associated losses. Provide a sample calculation for determining the property value and guaranteed replacement cost losses. The loss model assumes that the insured value is equal to the replacement or actual value of the insured property and contents. Unless specified with separate documentation on replacement value, no provision is made in the loss costs analysis for cases where the replacement value exceeds the insured value, and thus no estimate of the value of the building being different from the insured value is computed. 7. Describe how loss adjustment expenses are considered within the loss cost estimates. Loss adjustment expenses are not considered. Applied Research Associates, Inc. 69 May 1, 2004 Actuarial Standards

70 A-2 Loss Cost Projections A. Loss cost projections produced by hurricane loss projection models shall not include expenses, risk load, investment income, premium reserves, taxes, assessments, or profit margin. B. Loss cost projections shall not make a prospective provision for economic inflation. C. Loss cost projections shall not explicitly include demand surge. A. Loss costs projections include only the direct costs associated with rebuilding/replacing the damaged structures, contents, appurtenant structures, and the costs associated with additional living expenses. Additional costs such as expenses, risk load, investment income, premium reserves, taxes, assessments, or profit, are not included in the loss costs projections. B. Loss cost projections do not make prospective provisions for economic inflation. C. Loss cost projections do not explicitly treat demand surge. A separate analysis would be required with increased labor and material costs in order to produce loss functions that might be representative of demand surge. Separate studies to treat demand surge would be documented in the analysis report. 1. Describe the method or methods used to estimate annual loss costs needed for ratemaking. Identify any source documents used and research performed. Using a straightforward simulation of N years of storms, expected annual losses are computed simply as the sum of all losses, net of policy conditions, divided by N. Loss costs for a given territory are computed by dividing the average annual loss by the appropriate exposure base. 2. Explain how the model treats the issue of demand surge. Loss cost projections do not explicitly treat demand surge. A separate analysis would be required with increased labor and material costs in order to produce loss functions that might be representative of demand surge. Separate studies to treat demand surge would be documented in the analysis report. 3. Identify the highest level of resolution for which loss costs can be provided. Identify the resolution used for the reported output ranges. Loss costs can be produced at the coverage, policy, site, ZIP Code, county, state, and portfolio levels. The highest resolution is for a site and an individual building located at a latitude-longitude point. The resolution for the reported output ranges in this document is ZIP Codes. Applied Research Associates, Inc. 70 May 1, 2004 Actuarial Standards

71 The model explicitly considers the location and terrain roughness of each area, such as beach/coastal, inland location, etc. For each simulated storm, the effects of terrain and location relative to the storm, distance from coast, and time since landfall are treated. Applied Research Associates, Inc. 71 May 1, 2004 Actuarial Standards

72 A-3 User Inputs All modifications, adjustments, assumptions, defaults, and treatments of missing values used in the model shall be actuarially sound and included with the model output. Modifications/Adjustments: Out-of-date ZIP Codes are mapped to the current ZIP Code set used in the model. Records with ZIP Codes that cannot be mapped by the model must be revised by the user or omitted from the analysis. No other modifications are made to the user inputs. Assumptions/Defaults: Building stock models associated with construction type, year built, and occupancy are used to infer loss functions for risks with unknown or incompletely specified construction characteristics (e.g., wood frame ). Details will be shown to the Professional Team. Missing Values: Records with missing values are reported in an error log file where the user is prompted to fill in the missing values. If no values can be input, the record is removed from the analysis. 1. Describe the methods used to distinguish among policy form types (e.g., homeowners, dwelling property, mobile home, renters, condo unit owners). Loss costs are produced separately by the model for structure, contents, and loss of use subject to the applicable homeowner, mobile home, renters, or condo unit owner policy limits. Aside from differences in the primary coverage and coverage limits, no further distinctions are made among policy form types in the model. Mobile homes are identified through the indicated structure type. 2. Disclose, in a model output report, the specific type of input that is required to use the model or model output in a personal residential property insurance rate filing. Such input includes, but is not limited to, optional features of the model, type of data to be supplied by the model user and needed to derive loss estimates from the model, and any variables that a model user is authorized to set in implementing the model. Include the model name and version number on the model output report. A blank study disclosure summary form is shown Figure 15. Model input requirements are documented in the HurLoss User Manual, which will be made available to the Professional Team. 3. Provide a copy of the input form used by a model user to provide input criteria to be used in the model. The modeler should demonstrate that the input form relates directly to the model output. Include the model name and version number on the input form. All items included in the input and output forms submitted to the Commission should be clearly labeled and clearly defined. Applied Research Associates, Inc. 72 May 1, 2004 Actuarial Standards

73 A completed study disclosure summary form for the Output Ranges analysis (Forms S-1A and S-1B) summarizing the input criteria and modeling assumptions is shown in Figure Describe actions performed to ensure the validity of insurer data used for model inputs or validation/verification. Aggregate exposures by county, coverage, and line of business reported in the model output are compared to control totals provided by the insurer. Out-of-date ZIP Codes are mapped to the current ZIP Code set used in the model. Records with ZIP Codes that cannot be mapped by the model must be revised by the user or omitted from the analysis. The fields in each record are checked for valid ranges (known construction types, nonnegative coverage limits, valid locations, etc.). Records with invalid or missing fields are reported to the user and must be revised by the user or omitted from the analysis. Applied Research Associates, Inc. 73 May 1, 2004 Actuarial Standards

74 HURLOSS STUDY DISCLOSURE SUMMARY Input/Output Element Disclosure/Discussion Model Version: HURLOSS 3.1 Model Accepted by FCHLPM? Yes. Accepted under 2002 Standards on 5/29/2003. Model Options Used: Company: Purpose of Study: Period of Analysis: ARA Analysts: Company Supplied Data: Company Loss Data Analyzed: Lines of Business: Coverages: Deductibles: Company Exposure Analyzed? Loss aggregation: Exposure locations: Insurance to value: Demand Surge Costs Included? No, demand surge costs are not a standard output of the model. Figure 15. Blank Study Disclosure Summary Form Applied Research Associates, Inc. 74 May 1, 2004 Actuarial Standards

75 HURLOSS STUDY DISCLOSURE SUMMARY Input/Output Element Disclosure/Discussion Model Version: HURLOSS 3.2 Model Accepted by FCHLPM? Approval pending; submitted for review under 2003 Standards on February 28, Model Options Used: None. Company: FCHLPM Purpose of Study: Development of county and statewide output ranges Period of Analysis: January-February 2004 ARA Analysts: Company Supplied Data: Company Loss Data Analyzed: Lines of Business: Coverages: F. Lavelle, P. Vickery Coverage A, B, C, D exposure weights by ZIP Code along with definitions of types of business, lines of business, construction types, deductible groups, and file layout. None Owners, Renters, Condo Unit Owners, and Mobile Home Owners A, B, C, D Deductibles: $0, $500, $1000, $2500, 1%, 2%, 5% (Percentages based on primary coverage limit) Company Exposure Analyzed? Not applicable. Loss aggregation: Exposure locations: Insurance to value: Demand Surge Costs Included? Results aggregated to county and state levels. ZIP Codes Replacement value assumed to be equal to insured limit. No, demand surge costs are not a standard output of the model. Figure 16. Study Disclosure Form for Forms S-1A and S-1B Applied Research Associates, Inc. 75 May 1, 2004 Actuarial Standards

76 A-4 Logical Relationship to Risk A. Loss costs shall not exhibit an illogical relation to risk, nor shall loss costs exhibit a significant change when the underlying risk does not change significantly. B. Loss costs produced by the model shall be positive and non-zero for all valid Florida ZIP Codes. C. Loss costs cannot increase as friction or roughness increase, all other factors held constant. D. Loss costs cannot increase as the quality of construction type, materials and workmanship increases, all other factors held constant. E. Loss costs cannot increase as the presence of fixtures or construction techniques designed for hazard mitigation increases, all other factors held constant. F. Loss costs cannot increase as the quality of building codes and enforcement increases, all other factors held constant. G. Loss costs shall decrease as deductibles increase, all other factors held constant. H. The relationship of loss costs for individual coverages (A, B, C, D) shall be consistent with the coverages provided. A. The loss costs produced by the ARA model show no illogical relations with respect to risk, nor do loss costs exhibit a significant change when the underlying risk does not change significantly. B. Loss costs produced by the model are positive and non-zero for all Florida ZIP Codes. C. Loss costs decrease as the surface roughness increases, all other factors held constant. D. Loss costs decrease as quality of construction increase. E. Loss costs decrease as wind-resistive fixtures and/or construction techniques are applied. F. The model does not explicitly treat building code enforcement. To the extent that building code enforcement is quantified in specific structural or building parameter terms, the model shows that loss costs decreases for stronger/higher quality construction, all other factors held constant. G. Loss costs decrease with increasing deductibles, all other factors held constant. Applied Research Associates, Inc. 76 May 1, 2004 Actuarial Standards

77 H. The relationship of loss costs for individual coverages (A, B, C, D) is consistent with the coverages provided. 1. State whether the model produces the same loss costs if it runs the same information more than once (i.e., not changing the seed of the random number generator). The model produces the same loss costs each time it is run with the same input information. 2. Demonstrate that loss cost relationships by type of coverage (buildings, appurtenant structures, contents, additional living expenses) are consistent with actual insurance data. Loss cost relationships by coverage type are compared to actual data as given in Form S Demonstrate that loss cost relationships by construction type or vulnerability function (frame, masonry, and mobile home) are consistent with actual insurance data. Loss cost relationships by construction type are compared to actual data as given in Form S Demonstrate that loss cost relationships among coverages, territories, and regions are consistent and reasonable. Loss cost relationships among coverages, territories, and regions are consistent and reasonable. The hurricane risk map for Florida, derived using our peer-reviewed hurricane risk model, yields 50 and 100 year return period wind speeds along the United States coastline similar to results of previous studies such as Georgiou (1985). A comparison of the loss costs for wood, masonry, and mobile homes, shown in Form S-4, shows the predicted loss costs vary with the hurricane risk, with a maximum in South Florida, where the hurricane risk is highest, to a minimum away from the coast, in North Florida where the risk is lowest. Note that the loss costs include the effects of local terrain, while the wind speed map is given for open terrain. 5. Explain any anomalies or special circumstances that might preclude any of the above conditions from occurring. None. 6. Provide a completed Form A-1, 30 Hypothetical Events. See Form A Provide a completed Form A-2, Loss Costs. See Form A-2. Applied Research Associates, Inc. 77 May 1, 2004 Actuarial Standards

78 A-5 Deductibles and Policy Limits A. The methods used in the development of mathematical distributions to reflect the effects of deductibles and policy limits shall be actuarially sound. B. The relationship among the modeled deductible loss costs shall be reasonable. Differences in these relationships from those previously found acceptable shall be reasonable. The model produces statistical distribution of losses that are treated mathematically to reflect deductibles and coinsurance. The details of this approach and validation will be presented to the professional team during their visit. The relationships among the modeled deductible loss costs are reasonable, as demonstrated by our results for Form A-2, Form S-1A, and Form S-1B. 1. Describe the methods used in the model to treat deductibles (both flat and percentage), policy limits, replacement costs, and insurance-to-value when estimating loss costs. ARA has developed fast-running loss functions that enable direct computations for deductibles within the simulations. Since loss estimates are performed on a policy-by-policy basis, the actual loss experienced by the building is simply equal to the ground-up loss minus the deductible. Deductibles that are expressed as a percentage are converted to dollar equivalent and then applied. Unless otherwise indicated and documented, the insured value of the structure is assumed to be equal to the replacement value of the structure. In the estimation of loss costs, the modeled losses are not allowed to exceed the policy limits without separate documentation and justification. 2. Provide an example of how insurer loss (loss net of deductibles) is calculated. Discuss data or documentation used to confirm or validate the method used by the model. Table 2 is representative of a typical one-story house. Table 2. Example of Insurer Loss Calculation Building Policy Mean Loss Average Loss Average Loss Net Value Limit Deductible Ratio (Zero Deductible) of Deductible $100, $90, $ % $ $ For any given windspeed, the damage is comprised of a wide range of losses ranging between 0 and 100%. To compute the mean loss for a given windspeed, each possible outcome of ground-up loss derived from the building performance model is used, and the deductible is subtracted from this total loss to compute the net loss. The mean loss, including the effect of deductible, is then obtained by averaging each of the computed losses. The example paid losses given above change with the characteristics of the building. Applied Research Associates, Inc. 78 May 1, 2004 Actuarial Standards

79 A-6 Contents A. The methods used in the development of contents loss costs shall be actuarially sound. B. The relationship between the modeled building and contents loss costs shall be reasonable, based on the relationship between historical building and contents losses. Differences in the relationship of building and contents loss costs from those previously found acceptable shall be reasonable. The ARA model produces direct estimates of damage to contents. The model has been validated through comparisons with actual loss data. 1. Describe the methods used in the model to calculate loss costs for contents coverage. The model used to estimate the vulnerability of contents is based on the physical damage and the resulting possibility of wind and water entering the building following damage. Thus, while the damage to contents is a function of the damage to the building, the model is constructed in such a way that damage to contents does not occur until sufficient physical damage to the building has occurred to allow wind and/or water to enter the building causing damage to the contents. The content model has been validated/calibrated separately from the building vulnerability model. 2. Demonstrate that loss cost relationships between building and contents coverages are reasonable. Figure 17 shows a comparison of modeled and observed content loss as a function of the loss to the building. Content Damage Ratio Observed Model Building Damage Ratio Figure 17. Comparison of Modeled and Observed Mean Content Damage vs. Mean Building Damage Applied Research Associates, Inc. 79 May 1, 2004 Actuarial Standards

80 A-7 Additional Living Expenses (ALE) A. The methods used in the development of Additional Living Expense (ALE) loss costs shall be actuarially sound. B. ALE loss cost derivations shall consider the estimated time required to repair or replace the property. C. The relationship between the modeled building and ALE loss costs shall be reasonable, based on the relationship between historical building and ALE losses. Differences in the relationship of building and ALE loss costs from those previously found acceptable shall be reasonable. ARA s Additional Living Expense model includes factors that are hurricane related, including the time it takes to repair/reconstruct the house and storm surge/wave damage to infrastructure. The model has been calibrated using insurance loss data. Additional living expenses are estimated using a model which estimates the time required to rebuild a damage structure and includes a component for damage to infrastructure due to storm surge and waves. The model does not initiate the computation for additional living expenses associated with wind induced damage until the physical damage sustained to the building is significant enough such that the building is unlivable. ALE losses associated with storm surge and wave damage to the infrastructure can occur when there is no damage to the structure. 1. Describe the methods used to develop loss cost for additional living expense coverage. State whether the model considers both direct and indirect loss to the building. For example, direct loss is for expenses paid to house policyholders in an apartment while their home is being repaired. Indirect loss is for expenses incurred for loss of power (e.g., food spoilage). An additional living expense model was developed using a time restoration model that is used to estimate the time homeowners are unable to live in the damaged structure. The model allows for ALE losses to be incurred due to infrastructure damage caused by storm surge and waves. The model has been calibrated through comparisons of actual insurance losses. 2. State the minimum threshold at which ALE loss is calculated (e.g., loss is estimated for building damage greater than 20% or only for category 3, 4, 5 events). Provide documentation of validation test results to verify the approach used. Figure 18 shows a ZIP Code comparison of modeled and actual ALE costs. Since the model allows for ALE losses to be incurred due to infrastructure damage caused by storm surge and waves, there is no minimum threshold of building damage required for ALE losses. Applied Research Associates, Inc. 80 May 1, 2004 Actuarial Standards

81 ALE Damage Ratio Observed Model Building Damage Ratio Figure 18. Comparison of Modeled and Observed Mean ALE Damage vs. Mean Building Damage Applied Research Associates, Inc. 81 May 1, 2004 Actuarial Standards

82 Form A-1: 30 Hypothetical Events Thirty hypothetical events have been specified by the Commission consisting of five hurricanes, one for each hurricane category 1-5, at six different landfall locations; Jacksonville, Ft. Pierce, Miami, Ft. Myers, Tampa/St. Petersburg, and Panama City. Provide the maximum estimated one-minute sustained 10-meter wind speed over land associated with the events as well as the estimated loss by coverage type. Modeled estimated one-minute average wind speeds should be consistent with central pressure inputs. A description of the events is contained in the file named FormA1Input03.xls. Provide this information on CD-ROM in both an Excel and a PDF format. The file name should include the abbreviated name of the modeler, the Standards year, and the Form name. Complete Form A-1 using the specified file layout: Estimated losses are requested in total and by coverage type for the 30 hypothetical events. No. Field Name Description INPUT 1. Event ID Event identification Category Saffir-Simpson Hurricane Category Central Pressure Measured in millibars 4. Radius of Maximum Winds Measured in statute miles 5. Forward Speed Measured in miles per hour 6. Landfall Latitude and longitude of event at landfall location 7. Location General area of landfall 8. Direction Measured in degrees, assuming 0 degrees is north 9. Radius of Hurricane Force Winds Measured in statute miles OUTPUT 10. Maximum Estimated Wind Speed 11. Total Estimated Loss Maximum estimated one minute average wind speed over land for this event Total estimated loss summarized for building, appurtenant structures, contents and additional living expense 12. Estimated Building Loss Total estimated loss for building 13. Estimated App. Structure Loss Total estimated loss for appurtenant structures 14. Estimated Contents Loss Total estimated loss for contents 15. Estimated ALE Loss Total estimated loss for additional living expense The requested data are provided on the CD-ROM in the files ARA03FormA1.xls and ARA03FormA1.pdf. Applied Research Associates, Inc. 82 May 1, 2004 Actuarial Standards

83 Form A-2: Loss Costs Provide loss costs for each construction type for each ZIP Code in the sample data set named FormA2Input03.xls. The following is a description of the requested file layout. Follow the instructions on Form A-2 below. Note that fields 1-9 are the exposure fields from the sample data set. Fields are for the loss costs (net of deductibles). Provide the expected annual loss costs by construction type and coverage for each ZIP Code in the sample data. There are 1,479 ZIP Codes and 3 construction types; therefore, the completed file should have 4,437 records in total. If there are ZIP Codes in the sample data set that the model does not recognize as valid, provide a list of such ZIP Codes and either a) the new ZIP Code to which the original one was mapped, or b) an indication that the insured values from this ZIP Code were not modeled. Loss cost data should be provided for all ZIP Codes given in the sample data set. That is, if no losses were modeled, the record should still be included in the completed file with loss cost of zero, and, if a ZIP Code was mapped to a new one, the resulting loss costs should be reported with the original ZIP Code. Provide the results on CD-ROM in both Excel and PDF format using the following file layout. The file name should include the abbreviated name of the modeler, the Standards year, and the Form name. No. Field Name Description Exposure Fields from Sample Data Set 1 Analysis Date Date of Analysis YYYY/MM/DD 2 County Code FIPS County Code 3 ZIP Code 5-digit ZIP Code 4 Construction Type Use the following: 1 = Wood Frame, 2 = Masonry, 3 = Mobile Home 5 Deductible 1% (of the Building Value) policy deductible for each record (i.e., 0.01*$100,000) 6 Building Value $100,000 for each record 7 Appurtenant Structures Value $10,000 for each record 8 Contents Value $50,000 for each record 9 Additional Living Expense Value $20,000 for each record Loss Costs (net of deductibles) 10 Building Loss Cost* Estimated expected annual loss cost for building divided by the building value modeled for each record ($100,000) 11 Appurtenant Structures Loss Cost* Estimated expected annual loss cost for appurtenant structures divided by the appurtenant structures value modeled for each record ($10,000) 12 Contents Loss Cost* Estimated expected annual loss cost for contents divided by the contents value modeled for each record ($50,000) 13 Additional Living Expense Loss Cost* Estimated expected annual loss cost for additional living expense divided by the additional living expense value modeled for each record ($20,000) *Round all loss costs to 6 decimal places Applied Research Associates, Inc. 83 May 1, 2004 Actuarial Standards

84 All deductibles are a percentage of the Building Value and are policy-level deductibles; however, for reporting purposes, the policy deductible should be pro-rated to the individual coverage losses in proportion to the loss. Example Assume that a model analyzing Wood Frame properties in ZIP Code (Miami-Dade County) estimated the following: Field Name Value Analysis Date 1999/11/15 County Code Miami-Dade County = 86 ZIP Code Construction Type Wood Frame = 1 Deductible 1% = 0.01*$100,000 = $1,000 Building Value $100,000 Appurtenant Structures Value $10,000 Contents Value $50,000 Additional Living Expense Value $20,000 Building Loss* $10,000 Appurtenant Structures Loss* $1,000 Contents Loss* $2,500 Additional Living Expense Loss* $500 *Represents 1 st dollar losses (i.e., prior to application of deductibles) The $1,000 policy deductible would be applied as follows: Deductible 1% = 0.01*$100,000=$1,000 Building Loss $10,000-[($10,000 $14,000)x$1,000]=$9, Appurtenant Structures Loss $1,000-[($1,000 $14,000)x$1,000]=$ Contents Loss $2,500-[($2,500 $14,000)x$1,000]=$2, Additional Living Expense Loss $500-[($500 $14,000)x$1,000]=$ The reported Form A-2 data are shown below: Field Name Value Analysis Date 1999/11/15 County Code Miami-Dade County = 86 ZIP Code Construction Type Wood Frame = 1 Deductible 1% = 0.01 Building Value $100,000 Appurtenant Structures Value $10,000 Contents Value $50,000 Additional Living Expense Value $20,000 Building Loss Cost $9, $100,000 = Appurtenant Structures Loss Cost $ $10,000 = Contents Loss Cost $2, $50,000 = Additional Living Expense Loss Cost $ $20,000 = Based on the above information, the data should be reported in the following format: 1999/11/15,86,33102,1,0.01,100000,10000,50000,20000, , , , Applied Research Associates, Inc. 84 May 1, 2004 Actuarial Standards

85 The requested data are provided on the CD-ROM in the files ARA03FormA2_ Revised2004May01.xls and ARA03FormA2_Revised2004May01.pdf. Applied Research Associates, Inc. 85 May 1, 2004 Actuarial Standards

86 Statistical Standards S-1 Use of Historical Data A. The use of historical data in developing the model shall be supported by rigorous methods published in currently accepted scientific literature. B. Modeled and historical results shall reflect agreement using currently accepted scientific and statistical methods. The use of historical data in developing the hurricane model has been demonstrated to be reasonable through publications in the scientific literature. Goodness of fit tests, comparing modeled to historical data, have been performed using t tests, F tests, Chi-squared tests, and the Kolmogorov-Smirnoff test, for all historical data used to define the hurricane hazard. Example comparisons are given in Figure 19 for the distribution of storm central pressure deficit for all tropical cyclones passing within 155 miles of milepost 1450 (approximately Miami). Figure 20 shows a comparison of the modeled and historical distributions of storm heading of all tropical storms passing within 155 miles of milepost Results of all of the statistical tests performed will be available to the professional team during their on-site visit. 24 Milepost 1450 K-S Test:Pass C-S Test 1:Pass C-S Test 2:Pass Observed Model Count Central Pressure Deficit - mbar (Minimum within 155 miles of Milepost) Figure 19. Comparison of Historical and Modeled Distributions of the Central Pressure Deficit of all Tropical Storms Passing Within 155 miles of Milepost 1450 Applied Research Associates, Inc. 86 May 1, 2004 Statistical Standards

87 25 20 Milepost 1450 K-S Test:Pass C-S Test 1:Pass C-S Test 2:Pass Observed Model Count Heading (Degrees, Clockwise from North) Figure 20. Comparison of Historical and Modeled Distributions of Storm Heading of all Tropical Storms Passing Within 155 miles of Milepost Identify the form of the probability distributions used for each function or variable, if applicable. Identify statistical techniques used for the estimates and the specific goodnessof-fit tests applied. Describe whether the p-values associated with the fitted distributions provide a reasonable agreement with the historical data. The hurricane track modeling approach used does not sample from predefined distributions of central pressure, heading, etc. The model predicts the parameters (position and central pressure) of a storm at the next time step based on its position, speed and heading at the current time step and up to two prior time steps. The resulting distributions of translation speed, heading, distance of closest approach are log-normal, bi-normal, and either uniform or linear, respectively. The distribution of the central pressure is approximately Weibull. The radius to maximum winds is modeled using a log-normal distribution, dependent on the central pressure difference and latitude, and the Holland profile parameter is modeled as being normally distributed about the regression line with a mean linearly dependent on central pressure and latitude. All distributions have been evaluated through standard statistical tests using a p-level associated with 95% confidence level. 2. Provide the source and the number of years of the historical data set used to develop probability distributions for specific hurricane characteristics. If any modifications have been made to the data set, describe them in detail and their appropriateness. The HURDAT data set encompassing the period represents the prime source of historical information used to develop frequency distributions for storms affecting Florida and surrounding areas. The HURDAT database has been supplemented with information given in NWS-38 (Ho, et al.,1984). The prime source of information used to develop models for the radii to maximum winds is NWS-38 supplemented with data on more recent storms as given in the scientific literature. Applied Research Associates, Inc. 87 May 1, 2004 Statistical Standards

88 3. Describe the nature and results of the tests performed to validate the wind speeds generated. Wind speed validation studies have been performed using data obtained from Hurricanes Frederic (1979), Alicia (1983), Elena (1985), Hugo (1989), Bob (1991), Andrew (1992), Emily (1993), Erin (1995), Opal (1995), Bertha (1996), Fran (1996), Bonnie (1997), and Georges (1998). In all cases, quantitative comparisons of modeled and observed estimates of wind speeds are only made if the measured records of wind speeds are continuous (or contain daily maxima), the height of the anemometer is known, the averaging times of both the gust and long term average is known and the terrain surrounding the anemometer is known. Comparisons for all valid records are given in Vickery, et al. (2000) for storms up to and including those that occurred in Provide the date of loss of the insurance company data available for validation and verification of the model. Insurance company data from the following hurricanes have been used to validate the model: Hurricane Hugo (1989) Hurricane Andrew (1992) Hurricane Erin (1995) Hurricane Bertha (1996) Hurricane Bonnie (1998) Hurricane Earl (1998) 5. Provide an assessment of uncertainty using confidence intervals or other accepted scientific characterizations of uncertainty. The major modeling uncertainties include hurricane, terrain, building stock, and vulnerability. An example of our analysis of hurricane modeling uncertainty is provided in Twisdale, Vickery and Hardy (1993), where it is shown that the uncertainty in wind speed as a function of return period is relatively large (CoV~10%). Using this result as an example, the mean estimated 100 year return period peak gust wind speed in the Miami region is about 145 mph, with the 95% confidence values ranging between about 116 mph and 175 mph. The uncertainties in the predicted wind speeds at a given location are amplified when propagated through the model to obtain predictions of loss costs. The magnitude of this amplification decreases with increasing uncertainty in the underlying distribution of wind speed and varies with location. In Alachua County, for example, a 1% uncertainty in the underlying distribution of wind speeds propagates through to a 7% uncertainty in loss costs, whereas as 10% uncertainty (expressed as a CoV) in wind speed propagates through to a 62% CoV in a loss costs. 6. Provide a completed Form S-10, Probability of Hurricanes per Year. See Form S-10. Applied Research Associates, Inc. 88 May 10, 2004 Statistical Standards

89 7. Provide a completed Form S-11, Probable Maximum Loss. See Form S-11. Applied Research Associates, Inc. 89 May 1, 2004 Statistical Standards

90 S-2 Sensitivity Analysis for Model Output The modeler shall have assessed the sensitivity of temporal and spatial outputs with respect to the simultaneous variation of input variables using currently accepted scientific and statistical methods and have taken appropriate action. The model has been tested and sensitivities estimated for cases of simultaneous variations of input parameters. The sensitivity study results will be presented to the professional team. 1. Provide a detailed explanation of the sensitivity analyses that were performed on the model. One-by-one sensitivity studies have been performed examining the effects of terrain, building characteristics, and wind speed. Sensitivity studies have also been performed examining hurricane climate modeling. A model-independent analytic study has been performed that shows the sensitivity of loss cost estimation for a point location. A conference paper (Twisdale, et al., 1993) has been published on uncertainties in hurricane wind risk estimation for a single location. Additional sensitivity studies have been performed in completing Form F for the FCHLPM in previous years. 2. Provide a description of the statistical methods used to perform the sensitivity analysis. No detailed statistical analyses have been performed using the results obtained from the sensitivity studies noted above examining terrain, wind speed and building characteristics. The Form F sensitivity studies were examined to determine the sensitivity of the model (with respect to wind speed estimates) associated with changes in radius to maximum winds, translation speed, central pressure and the Holland pressure profile parameter. 3. Identify the most sensitive aspect of the model and the basis for making this determination. Provide a full discussion of the degree to which these sensitivities affect output results and illustrate with an example. The single most important element in loss estimation is the wind speed since the loads and, hence, physical damage to a building is proportional to the square of the wind speed. Thus, the estimation of losses is sensitive to the hurricane wind climate and the wind model. Loss results are particularly sensitive to the occurrence rate and area of the more intense hurricanes (Saffir- Simpson Category 3 or higher). Given a location (i.e., the hurricane wind climate is held fixed), another key variable is surface roughness and the manner in which the boundary layer is modeled for the sea-land transition. Loss predictions are, or course, sensitive to the building construction parameters and, hence, the vulnerability functions used. These observations of sensitivity are based on numerous detailed sensitivity analyses, theoretical considerations, as well as field observations. Figure 21 shows an example of the distribution of the average annual loss for a house located in Alachua County associated with an assumed distribution describing the variation in the extreme wind speed distribution. The coefficient of variation associated with the input wind speed model was 1%, with this propagating to yield a 7% CoV associated with the prediction of Applied Research Associates, Inc. 90 May 1, 2004 Statistical Standards

91 the AAL. Increasing the coefficient of variation of the hurricane hazard curve to 10%, yields a CoV in the AAL of 62% and increases the mean value of the AAL by 13%. Additional examples showing the impact of the different parameters used in the model on loss cost estimates will be provided to the professional team during their visit AAL = 1.6 $/1000 CoV = Relative Contr bution AAL $/1000 Figure 21. Distribution of AAL Associated with a 1% CoV in the Wind Speed Model 4. Describe other aspects of the model that may have a significant impact on the sensitivities in output results. The sensitivity studies have shown that the estimation of the average annual loss is most sensitive to the estimation of wind speed (i.e. the hurricane hazard curve). In following the development of the model for estimating the hurricane wind speed hazard, this leads to the fact that the modeling of the Holland pressure profile parameter and the modeling of central pressure are other parameters that have a significant impact on the sensitivities of model outputs. An additional item that the model may be sensitive to is relative humidity, which is held constant in the development of the hurricane intensity. A new sensitivity study examining the sensitivity of the results to the number of simulated years was performed by simulating a new 100,000 year storm set with a new random seed. This study showed that the estimates of total loss costs could change by up to 8% in the low loss cost regions of Florida. 5. Describe actions taken in light of the sensitivity analyses performed. At the current time, no changes have been made to the model as a result of the sensitivity analysis. In light of the sensitivity in the estimation of loss costs noted in 4, we have increased our stochastic storm set to include 300,000 years of simulated hurricanes. 6. Provide a completed Form S-12, Hypothetical Events for Sensitivity and Uncertainty Analysis (requirement for new modeling companies which have not previously provided the Commission with this analysis). See Form S-12. Applied Research Associates, Inc. 91 May 1, 2004 Statistical Standards

92 S-3 Uncertainty Analysis for Model Output The modeler shall have performed an uncertainty analysis on the temporal and spatial outputs of the model using currently accepted scientific and statistical methods and have taken appropriate action. The analysis shall identify and quantify the extent that input variables impact the uncertainty in model output as the input variables are simultaneously varied. Estimates of uncertainty are not a standard output of the model; however, an uncertainty analysis has been performed and the results will be presented to the professional team. 1. Provide a detailed explanation of the uncertainty analyses that were performed on the model. One-by-one uncertainty studies have been performed examining the impact of reasonable estimates in the uncertainties associated with terrain, building characteristics, and wind speed, and examining how these propagate through to uncertainties in loss estimates. Uncertainty studies have also been performed examining hurricane climate modeling. A model-independent analytic study has been performed that shows the sensitivity of loss cost estimation for a point location. A conference paper (Twisdale, et al., 1993) has been published on uncertainties in hurricane wind risk estimation for a single location. 2. Provide a description of the statistical methods used to perform the uncertainty analysis. No detailed statistical analyses have been performed using the results obtained from the uncertainty studies noted above examining terrain, wind speed and building characteristics. The Form F uncertainty studies were examined to compute the expected percentage reduction coefficients associated with each variable considered in the Form F study.. 3. Identify the major contributors to the uncertainty in model outputs and the basis for making this determination. Provide a full discussion of the degree to which these uncertainties affect output results and illustrate with an example. The single most important element in loss estimation is the wind speed since the loads and, hence, physical damage to a building is proportional to the square of the wind speed. Thus, the estimation of losses is sensitive to the hurricane wind climate and the wind model. Loss results are particularly sensitive to the occurrence rate and area of the more intense hurricanes (Saffir- Simpson Category 3 or higher). Given a location (i.e., the hurricane wind climate is held fixed), another key variable is surface roughness and the manner in which the boundary layer is modeled for the sea-land transition. Loss predictions are, or course, sensitive to the building construction parameters and, hence, the vulnerability functions used. These observations of sensitivity are based on numerous detailed sensitivity analyses, theoretical considerations, as well as field observations. An example of the impact of a 1% uncertainty in the wind speed climate (as defined by the CoV) Applied Research Associates, Inc. 92 May 1, 2004 Statistical Standards

93 was presented in Figure 21, as to how this small uncertainty in wind speed propagates through to a large uncertainty in the prediction of average annual loss. The uncertainty studies performed by ARA personnel examining the impact of reasonable estimates in the uncertainties of key input parameters were performed by propagating the uncertainties through to the prediction of losses looking at the effects one at a time and in various combinations. The result of the studies clearly showed that the uncertainties in the of wind speed risk dominates the uncertainties in the losses. Additional uncertainty studies have been performed in completing Form F for the FCHLPM in previous years. 4. Describe other aspects of the model that may have a significant impact on the uncertainties in output results. The uncertainty in the predicted loss costs is driven by the uncertainty in the underlying wind hazard curve, and hence uncertainties in the modeling of central pressure, the Holland pressure profile parameter and the decay of storms as they travel inland. 5. Describe actions taken in light of the uncertainty analyses performed. As of the current time, no changes to the model have been made to the models as a result of the uncertainty analyses. 6. For new modeling companies, which have not previously provided this analysis to the Commission, Form S-12 was disclosed under Standard S-2 and will be used in the verification of Standard S-3. The sensitivity and uncertainty analysis required in Form S-12 was submitted by ARA as Form F under the 2002 Standards. Applied Research Associates, Inc. 93 May 1, 2004 Statistical Standards

94 S-4 County Level Aggregation At the county level of aggregation, the contribution to the error in loss costs estimates induced by the sampling process shall be negligible based upon currently accepted scientific and statistical methods. Figure 22 shows the standard errors in the total ground-up owner frame loss costs induced by the sampling process as a percentage of the county weighted average loss costs. For a 300,000 year simulation, the standard errors associated with the sampling process range from 0.9% in the counties with the most hurricane activity to 2.2% in the counties with the least hurricane activity. These errors are negligible in comparison to the uncertainties in the model. Figure 22. Standard Errors in County Weighted Average Loss Costs Applied Research Associates, Inc. 94 May 1, 2004 Statistical Standards

95 1. Describe the sampling plan used to obtain the average annual loss costs and output ranges. For a direct Monte Carlo simulation, indicate steps taken to determine sample size. For importance sampling design, describe the underpinnings of the design. The sampling plan used to obtain AAL s and loss costs is a direct Monte Carlo simulation with a sample size 300,000 years. The sample size was selected by continuing to increase the number of simulated years and examining the standard error in the estimate of the mean loss costs. The sample set of 300,000 years produced a maximum standard error in the total owner frame ground-up loss costs of 2.2%. Applied Research Associates, Inc. 95 May 1, 2004 Statistical Standards

96 S-5 Replication of Known Hurricane Losses The model shall reasonably replicate incurred losses on a sufficient body of past hurricane events, including the most current data available to the modeler. This Standard applies separately to personal residential and, to the extent data are available, to mobile homes. Personal residential experience may be used to replicate building-only and contents-only losses. The replications shall be produced on an objective body of loss data by county or an appropriate level of geographic detail. Figure 23 presents a comparison of modeled and actual total losses by storm and company for residential coverage. The comparisons indicate reasonable agreement between the observed and modeled losses (r 2 = 0.99 in linear space and r 2 = 0.98 in logarithmic space). Additional, and more detailed, comparisons of modeled and actual incurred losses are given in Form S-6. Additional detail of actual and modeled ZIP Code level losses will be presented to the Professional Team. Comparison of Actual Company Losses to Modeled Losses Actual Loss Model Loss Figure 23. Comparison of Modeled and Observed Losses for Homeowner Policies 1. Describe the nature and results of the analyses performed to validate the loss estimates generated by the model. Figures 23 and 24 present example comparisons of simulated and observed losses from recent hurricanes. Other intermediate results for damage estimation will be shown to the Professional Team. Applied Research Associates, Inc. 96 May 1, 2004 Statistical Standards

97 Actual (%) Model (%) Peak Gust Wind Speed in Open Terrain (mph) Figure 24. Comparison of Modeled and Actual Losses as a Function of Peak Gust Wind Speed in Open Terrain 2. Provide a standardized residual plot of the modeled and historical losses. (y-axis is standardized residuals and x-axis is actual losses.) Provide separate plots for personal residential and, if available, for mobile homes. The loss data given in Figure 23 is plotted in Figure 25 in the form of standardized residuals plotted vs. the actual losses. Applied Research Associates, Inc. 97 May 1, 2004 Statistical Standards

98 Standardized Residual Normalized Paid Loss Figure 25. Standardized Residual vs. Normalized Actual Losses 3. Provide a completed Form S-6, Five Validation Comparisons. See Form S-6. Applied Research Associates, Inc. 98 May 1, 2004 Statistical Standards

99 S-6 Comparison of Estimated Hurricane Loss Costs The difference, due to uncertainty, between historical and modeled annual average statewide loss costs shall be statistically reasonable. The difference between historical and modeled annual average statewide loss costs is statistically reasonable, as demonstrated in Disclosures S-6.3 and S Describe the nature and results of the tests performed to validate the expected loss estimates generated. If a set of simulated hurricanes or simulation trials was used to determine these loss estimates, specify the convergence tests that were used and the results. Specify the number of hurricanes or trials that were used. A direct statistical validation of expected loss costs by region is not possible owing to the limited hurricane loss data at any given region, and thus indirect validation procedures are required. This validation process assumes that if the hurricane climatology, in terms of hurricane intensity, frequency, translation speed, filling and windspeed, is properly modeled, and if the prediction of loss given a hurricane is properly modeled, then the loss cost estimates should be valid. Comparisons of modeled and observed wind speeds were presented in the answer to Standard G-5. These comparisons showed that given information describing a storm, including the central pressure difference, p, the radius to maximum winds, Rmax, translation speed, Holland pressure profile parameter, B, heading and location, the wind field model is able to provide good estimates of wind speed at a site. Figure 26 shows comparisons of the modeled (simulated) and historical (derived from HURDAT) values of storm heading, translation speed, distance of closest approach and occurrence rate along the entire United States coastline. Data are given for all storms passing within 155 miles of a particular milepost, spaced at 50 nm increments along the coast. Figure 27 shows a comparison of the simulated and modeled landfall rate of hurricanes as a function of storm intensity in Florida. This comparison shows the combined effect of intensity and frequency modeling, indicating that the model is performing well in its ability to reproduce the statistics of landfall counts of storms as a function of intensity in Florida. The landfall counts given in Figure 27 represent the data for first entering storms (upper plot), all entering storms (middle plot), and all entering and all exiting storms (lower plot). Figure 28 shows a comparison of the historical and modeled annual rate of hurricane landfalls in Florida. In the comparisons given in Figure 28, each hurricane that makes landfall in Florida is counted as a single event for both the modeled and historical storms. The agreement between the annual rate historical and modeled hurricane landfalls is very good with the comparison of the landfall rate statistics passing both the Chi squared and Kolmogorov-Smirnov tests. Applied Research Associates, Inc. 99 May 10, 2004 Statistical Standards

100 HURDAT (MEAN) SIMULATED (MEAN) 60 Annual Occurrence Rate Heading HURDAT (MEAN) HURDAT (STD DEV) SIMULATED (MEAN) SIMULATED (STD DEV) Milepost Milepost Translation Speed (mph HURDAT (MEAN) HURDAT (STD DEV) SIMULATED (MEAN) SIMULATED (STD DEV) Minimum Approach Distance (miles HURDAT (MEAN) SIMULATED (MEAN) Milepost Milepost Figure 26. Comparison of Simulated and Observed Key Hurricane Statistics along the Gulf and Atlantic Coasts of the United States The ability of the model to produce reasonable estimates of loss as a function of wind speed was shown, for example, in Figure 24. As seen in this example, the model is able to reasonably reproduce the observed losses as a function of the peak wind speed in a storm. Figure 23 showed separate examples of total losses experienced by different companies for various events. Applied Research Associates, Inc. 100 May 1, 2004 Statistical Standards

101 FLORIDA - First Entering Storms Number of Events in 103Years Model Historical Storm Category Number of Events in 103Years FLORIDA - All Entering Storms Model Historical Storm Category FLORIDA - All Entering and Exiting Storms Number of Events in 103 Years Model Historical Storm Category Figure 27. Comparison of Modeled and Observed Landfall Rates of Hurricanes as a Function of Intensity in Florida Applied Research Associates, Inc. 101 May 10, 2004 Statistical Standards

102 Model Historical Probability Number of Landfalling Hurricanes/Year Figure 28. Comparison of Modeled and Observed Annual Number of Landfalling Hurricanes in Florida 2. Identify differences, if any, in how the model produces loss costs for specific historical events versus loss costs for events in the stochastic hurricane set. Loss costs from historic storms can be produced using either the fast running loss functions developed for use in the portfolio model or with the building performance model (which takes into account storm duration, change of wind direction, etc.). The computation of loss costs from historical or stochastic storms is fundamentally the same. The losses are summed by territory, line of business, etc., and divided by the appropriate exposure and the number of years of storms. 3. Provide the annual average zero deductible statewide loss costs produced using the list of hurricanes in the Official Storm Set based on the 1998 Florida Hurricane Catastrophe Fund s (FHCF) aggregate personal residential exposure data, as of November 1, 1999 (hlpm1998.exe). Provide a comparison with the statewide loss costs produced by the model on an average industry basis. Provide the 95% confidence interval on the differences between the mean of the historical and modeled loss. The ARA hurricane model calculated historical average annual loss for the 1998 FHCF aggregate exposure database is $2.33 billion per year. The ARA hurricane model calculated simulated average annual loss for the same exposure database $2.80 billion per year. The differences between the historical and simulated average annual statewide loss costs are statistically reasonable as seen through a t-test performed to examine the equivalence of means. The simulated AAL is well within the 95% confidence interval ($1.12 to $3.54 billion per year) on the historical AAL. Applied Research Associates, Inc. 102 May 1, 2004 Statistical Standards

103 4. Provide the annual average zero deductible statewide loss costs produced using the list of hurricanes in the Official Storm Set based on the 2002 Florida Hurricane Catastrophe Fund s (FHCF) aggregate personal residential exposure data, as of August 1, 2003 (hlpm2002.exe). Provide a comparison with the statewide loss costs produced by the model on an average industry basis. Provide the 95% confidence interval on the differences between the mean of the historical and modeled loss. The ARA hurricane model calculated historical average annual loss for the 2002 FHCF aggregate exposure database is $3.01 billion per year. The ARA hurricane model calculated simulated average annual loss for the same exposure database $3.64 billion per year. The differences between the historical and simulated average annual statewide loss costs are statistically reasonable as seen through a t-test performed to examine the equivalence of means. The simulated AAL is well within the 95% confidence interval ($1.45 to $4.58 billion per year) on the historical AAL. 5. Provide a completed Form S-4, Zero Deductible Loss Costs by ZIP Code. See Form S Provide a completed Form S-5, Average Annual Zero Deductible Statewide Loss Costs. See Form S Provide a completed Form S-7, Official Storm Set Average Annual Zero Deductible Statewide Loss Costs. See Form S Provide a completed Form S-8, Hurricane Andrew Loss Costs. See Form S-8. Applied Research Associates, Inc. 103 May 1, 2004 Statistical Standards

104 S-7 Output Ranges For a model previously found acceptable by the Commission, the differences in the updated output ranges shall be reasonable. As discussed below, the changes in the output ranges produced by the ARA model are reasonable. 1. Provide an explanation of the differences in the output ranges between the prior year and the current year submission. The differences in the output ranges between the prior year and the current year are driven by updates to two data sets used by the model: 1. The hurricane track model has been updated to include the starting locations of storms up through the 2002 hurricane season. 2. The ZIP Code database has been updated to 2003, which has caused changes to the ZIP Code centroids and the ZIP Code surface roughnesses. Significant changes in centroid locations and/or changes in ZIP Code boundaries had a particularly strong upward impact on the loss costs in Liberty and Santa Rosa counties. The above changes have resulted in perturbations of the 100-year return period peak gust, open terrain wind speeds on the order of +/- 2%. The corresponding changes in loss costs can be as much as seven times greater than the change in the 100-year wind speed. 2. Provide justification for changes from the prior submission of greater than ten percent in weighted average loss costs for any county, specifically by county. Counties and policy types having changes greater than ten percent in ground-up weighted average loss costs are highlighted in the table on the following page. The justifications for the changes in these counties are provided in Disclosure S-7.1, above. 3. Provide justification for changes from the prior submission of ten percent or less in the weighted average loss costs for any county, in the aggregate. All other changes in ground-up weighted average loss costs are ten percent or less. The justifications for these changes are also provided in Disclosure S-7.1, above. 4. Provide a completed Form S-1A, Output Ranges using the 1998 Florida Hurricane Catastrophe Fund aggregate exposure data. See Form S-1A. 5. Provide a completed Form S-1B, Output Ranges using the 2002 Florida Hurricane Catastrophe Fund aggregate exposure data. See Form S-1B. 6. Provide a completed Form S-2, Percentage Change in Output Ranges. See Form S Provide a completed Form S-3, Percentage Change in Output Ranges by County. See Form S-3. Applied Research Associates, Inc. 104 May 1, 2004 Statistical Standards

105 Percentage Changes from 2002 to 2003 in Total Ground-Up Loss Costs County Owner Frame Owner Masonry Mobile Home Renter Frame Renter Masonry Condo Frame Condo Masonry ALACHUA BAKER BAY BRADFORD BREVARD BROWARD CALHOUN CHARLOTTE CITRUS CLAY COLLIER COLUMBIA DADE DE SOTO DIXIE DUVAL ESCAMBIA FLAGLER FRANKLIN GADSEN GILCHRIST GLADES GULF HAMILTON HARDEE HENDRY HERNANDO HIGHLANDS HILLSBOROUGH HOLMES INDIAN RIVER JACKSON JEFFERSON LAFAYETTE LAKE LEE LEON LEVY LIBERTY MADISON MANATEE MARION MARTIN MONROE NASSAU OKALOOSA OKEECHOBEE ORANGE OSCEOLA PALM BEACH PASCO PINELLAS POLK PUTNAM SAINT JOHNS SAINT LUCIE SANTA ROSA SARASOTA SEMINOLE SUMNTER SUWANNEE TAYLOR UNION VOLUSIA WAKULLA WALTON WASHINGTON Applied Research Associates, Inc. 105 May 1, 2004 Statistical Standards

106 Form S-1A: Output Ranges Provide output ranges in the format shown in the file named 2003FormS1A.xls. A hard copy of the output range spreadsheets should be included with the submission at the end of the Statistical Standards section. Provide the output ranges on CD-ROM in both Excel and PDF format as specified. The file name should include the abbreviated name of the modeler, the Standards year, and the Form name. Provide loss costs by county. Within each county, loss costs should be shown separately per $1,000 of exposure for personal residential, renters, condo unit owners, and mobile home; for each major deductible option; and by construction type. For each of these categories using ZIP Code centroids, the output range should show the highest loss cost, the lowest loss cost, and the weighted average loss cost based on the 1998 Florida Hurricane Catastrophe Fund (FHCF) aggregate exposure data provided to each modeler in the file named hlpm1998.exe. A file named 99FHCFWts.xls has also been provided for use in determining the weighted average loss costs. Include the statewide range of loss costs (i.e., low, high, and weighted average). For each of the loss costs provided, identify what that loss cost represents by line of business, deductible option, construction type, and coverages included, i.e., structure, contents, appurtenant structures, or additional living expenses as specified. Output ranges should be computed assuming no modifications for a non-mitigated average building. Modelers should indicate if per diem is used in producing loss costs for Coverage D (ALE) in the output ranges. If a per diem rate is used in the submission, a rate of $ per day per policy should be used. If a modeler has loss costs for a ZIP Code for which there is no exposure, then the modeler should give the loss costs zero weight (i.e., assume the exposure in that ZIP Code is zero). Provide a list of those ZIP Codes where this occurs. If the modeler does not have loss costs for a ZIP Code for which there is some exposure, the modeler should not assume such loss costs are zero, but should use only the exposures for which it has loss costs in calculating the weighted average loss costs. Provide a list of the ZIP Codes where this occurs. Loss costs are provided on the included CD-ROM in the files named ARA03FormS1A_ Revised2004May01.xls and ARA03FormS1A_ Revised2004May01.pdf. Differences between the prior year and current year submission are discussed in our response to Standard S-7. Applied Research Associates, Inc. 106 May 1, 2004 Statistical Standards

107 Form S-1B: Output Ranges Provide output ranges in the format shown in the file named 2003FormS1B.xls. A hard copy of the output range spreadsheets should be included with the submission at the end of the Statistical Standards section. Provide the output ranges on CD-ROM in both Excel and PDF format as specified. The file name should include the abbreviated name of the modeler, the Standards year, and the Form name. Provide loss costs by county. Within each county, loss costs should be shown separately per $1,000 of exposure for personal residential, renters, condo unit owners, and mobile home; for each major deductible option; and by construction type. For each of these categories using ZIP Code centroids, the output range should show the highest loss cost, the lowest loss cost, and the weighted average loss cost based on the 2002 Florida Hurricane Catastrophe Fund (FHCF) aggregate exposure data provided to each modeler in the file named hlpm2002.exe. A file named 02FHCFWts.xls has also been provided for use in determining the weighted average loss costs. Include the statewide range of loss costs (i.e., low, high, and weighted average). For each of the loss costs provided, identify what that loss cost represents by line of business, deductible option, construction type, and coverages included, i.e., structure, contents, appurtenant structures, or additional living expenses as specified. Output ranges should be computed assuming no modifications for a non-mitigated average building. Modelers should indicate if per diem is used in producing loss costs for Coverage D (ALE) in the output ranges. If a per diem rate is used in the submission, a rate of $ per day per policy should be used. If a modeler has loss costs for a ZIP Code for which there is no exposure, then the modeler should give the loss costs zero weight (i.e., assume the exposure in that ZIP Code is zero). Provide a list of those ZIP Codes where this occurs. If the modeler does not have loss costs for a ZIP Code for which there is some exposure, the modeler should not assume such loss costs are zero, but should use only the exposures for which it has loss costs in calculating the weighted average loss costs. Provide a list of the ZIP Codes where this occurs. Loss costs are provided on the included CD-ROM in the files named ARA03FormS1B_ Revised2004May01.xls and ARA03FormS1B_Revised2004May01.pdf. Applied Research Associates, Inc. 107 May 1, 2004 Statistical Standards

108 Output Range Specifications Owners Policy Type Coverage A: Structure Amount of Insurance = $100,000 Replacement Cost Included Subject to Coverage A Limit Ordinance or Law Not Included Coverage B: Appurtenant Structures Amount of Insurance = 10% of Coverage A Amount Replacement Cost Included Subject to Coverage B Limit Ordinance or Law Not Included Coverage C: Contents Amount of Insurance = 50% of Coverage A Amount Replacement Cost Included Subject to Coverage C Limit Coverage D: Additional Living Expense Amount of Insurance = 20% of Coverage A Amount Time Limit = 12 Months Per Diem = $150.00/day per policy, if used Loss Costs per $1,000 should be related to the Coverage A Amount. For weighting the Coverage D Loss Costs, use the file named 99FHCFWts.xls for Form S-1A and 02FHCFWts.xls for Form S-1B for distribution for Coverage A. Loss Costs for the various deductibles should be determined based on per occurrence deductibles. Explain any deviations and differences from the prescribed format above. Specify the model name and version number reflecting the release date as a footnote on each page of the output. Applied Research Associates, Inc. 108 May 1, 2004 Statistical Standards

109 Output Range Specifications Renters Policy Type Coverage C: Contents Amount of Insurance = $25,000 Replacement Cost Included Subject to Coverage C Limit Coverage D: Additional Living Expense Amount of Insurance = 40% of Coverage C Amount Time Limit = 12 Months Per Diem = $150.00/day per policy, if used Loss Costs per $1,000 should be related to the Coverage C Amount. For weighting the Coverage D Loss Costs, use the file named 99FHCFWts.xls for Form S-1A and 02FHCFWts.xls for Form S-1B for distribution for Coverage C. Loss Costs for the various deductibles should be determined based on per occurrence deductibles. For weighting the Coverage C Loss Costs, use the file named 99FHCFWts.xls for Form S-1A and 02FHCFWts.xls for Form S-1B for distribution for Coverage C. Explain any deviations and differences from the prescribed format above. Specify the model name and version number reflecting the release date as a footnote on each page of the output. Applied Research Associates, Inc. 109 May 1, 2004 Statistical Standards

110 Output Range Specifications Condo Unit Owners Policy Type Coverage A: Structure Amount of Insurance = 10% of Coverage C Amount Replacement Cost Included Subject to Coverage A Limit Coverage C: Contents Amount of Insurance = $50,000 Replacement Cost Included Subject to Coverage C Limit Coverage D: Additional Living Expense Amount of Insurance = 40% of Coverage C Amount Time Limit = 12 Months Per Diem = $150.00/day per policy, if used Loss Costs per $1,000 should be related to the Coverage C Amount. For weighting the Coverage D Loss Costs, use the file named 99FHCFWts.xls for Form S-1A and 02FHCFWts.xls for Form S-1B for distribution for Coverage C. Loss Costs for the various deductibles should be determined based on per occurrence deductibles. For weighting the Coverage C Loss Costs, use the file named 99FHCFWts.xls for Form S-1A and 02FHCFWts.xls for Form S-1B for distribution for Coverage C. Explain any deviations and differences from the prescribed format above. Specify the model name and version number reflecting the release date as a footnote on each page of the output. Applied Research Associates, Inc. 110 May 1, 2004 Statistical Standards

111 Output Range Specifications Mobile Home Owners Policy Type Coverage A: Structure Amount of Insurance = $50,000 Replacement Cost Included Subject to Coverage A Limit Coverage B: Appurtenant Structures Amount of Insurance = 10% of Coverage A Amount Replacement Cost Included Subject to Coverage B Limit Coverage C: Contents Amount of Insurance = 50% of Coverage A Amount Replacement Cost Included Subject to Coverage C Limit Coverage D: Additional Living Expense Amount of Insurance = 20% of Coverage A Amount Time Limit = 12 Months Per Diem = $150.00/day per policy, if used Loss Costs per $1,000 should be related to the Coverage A Amount For weighting the Coverage D Loss Costs, use the file named 99FHCFWts.xls for Form S-1A and 02FHCFWts.xls for Form S-1B for distribution for Coverage A. Loss Costs for the various deductibles should be determined based on per occurrence deductibles. Explain any deviations and differences from the prescribed format above. Specify the model name and version number reflecting the release date as a footnote on each page of the output. Applied Research Associates, Inc. 111 May 1, 2004 Statistical Standards

112 Form S-2: Percentage Change In Output Ranges Provide the percentage change in the weighted average loss costs using the 1998 Florida Hurricane Catastrophe Fund s (FHCF) aggregate personal residential exposure data, as of November 1, 1999 only, from the output ranges from the prior year submission for the following: statewide (overall percentage change), by region, as defined in Figure 29 North, Central and South, by coastal and inland counties, as defined in Figure 30. Provide this Form on CD-ROM in both an Excel and a PDF format. The file name should include the abbreviated name of the modeler, the Standards year, and the Form name. Figure 29. State of Florida by North/Central/South Counties Applied Research Associates, Inc. 112 May 1, 2004 Statistical Standards