Storm Surge Modeling in Puerto Rico in Support of Emergency Response, Risk Assessment, Coastal Planning and Climate Change Analysis

Home , Condado Lagoon, Isla Magueyes, Isleta de San Juan

Report prepared for the Caribbean Coastal Ocean Observing System (CariCOOS)/NOAA University of Puerto Rico/Mayagüez, P.R. and Puerto Rico Coastal Zone Management Program Department of Natural and environmental Resources

by Jose Benítez (Ph.D. candidate) and Aurelio Mercado Irizarry (Professor)

Department of Marine Sciences/University of Puerto Rico/Mayaguez July 2015

TABLE OF CONTENTS

Content Page

Sponsors ……………………………………………………………………………………………………………… 3

Methodology ………………………………………………………………………………………………………. 4

Computer Models Used …………………………………………………………………………… 4

Hurricane Wind Model Used ……………………………………………………………………. 4

Hurricane Headings Used …………………………………………………………………………. 6

Computational Unstructured Mesh …………………………………………………………. 13

Domain Decomposition …………………………………………………………………………… 18

Frictional Dissipation ……………………………………………………………………………….. 19

Steric Effects on Sea Level in Puerto Rico …………………………………………………. 25

Astronomical Tide Validation ………………………………………………………………………………. 26

Validation Against Storm Surges ………………………………………………………………………….. 26

Results (Maps of Synthetic Hurricanes) ……………………………………………………………….. 41

Caveats ………………………………………………………………………………………………………………... 57

Acknowledgments ……………………………………………………………………………………………….. 58

References …………………………………………………………………………………………………………… 58

Appendix 1 …………………………………………………………………………………………………………… 59

Appendix 2 ……………………………………………………………………………………………………………. 60

TABLES

Table Page

1 Hurricane statistics used for the five Saffir-Simpson categories 9

2 Manning coefficient classification of the National Land Cover Class shown

In Figure 21 20

3 Manning coefficient classification of the benthic habitats shown in Figure 22 21 3

SPONSORS:

• The Caribbean Coastal Ocean Observing System (CariCOOS), a NOAA sponsored program established at the University of Puerto Rico at Mayaguez, P.R. • The Puerto Rico Coastal Zone Management Program (PRCZMP), a NOAA sponsored program established at the Department of Natural and Environmental Resources of Puerto Rico.

SCOPE OF WORK

Figure 1 – Map of the archipelago of Puerto Rico, including its two island municipalities of Vieques and Culebra (to the east). The uninhabited Isla de Mona to the west was not included in the flood mapping.

• First a note on the nomenclature to be used. Each hurricane simulation outputs spatial and temporal information about the ocean’s and wind waves’ response. One of these is the maximum sea surface elevations at every computational node irrespective of the moment in time when it is produced. This is called the Maximum Envelope Of Water, or MEOW. There is something similar for Hs, the significant wave height. In addition, when parallel hurricane tracks are run with a given constant heading, we will then have a collection of MEOWs, and based on them we compute the Maximum of the MEOWs, or MOM. This MOM then gives, for the sea surface heights, the maximum storm surge elevation at each computational node irrespective of the hurricane track(s) which generated it (see Figures 8 to 11). Henceforth, each set of (constant) hurricane headings will have a set of MEOWs for each track in the set, and one associated MOM showing the maximum storm surge elevation irrespective of the individual hurricane track(s) that produced it. In this study, as will be discussed below, four different headings were chosen, so for each hurricane category there will be four MOMs. Next we combine all of the MOMs into a SuperMOM, which will show the maximum storm surge 4

elevations at each computational node for that given category irrespective of the track(s) and hurricane heading(s) that produced it. So the principal output will be MEOWs, MOMs, and SuperMOMs. • The SuperMOM for each hurricane category will include results of all chosen tracks and storm headings (to be mentioned below), with the exception of a heading from the southwest to the northeast, which has been included due to Hurricanes Lenny and Omar. That heading has been chosen since it will be a worst case scenario for the west coast of the island, but since no hurricane has ever made landfall on the west coast, it is not included in the SuperMOM, but the results are there just in case they are ever needed. And, besides, they could be useful for coastal planning along the west coast of Puerto Rico if one wonders what could be expected from a hurricane making landfall along the west coast. • The mandate on the part of the CariCOOS program was to develop a storm surge atlas for Puerto Rico (Figure 1) based on present sea level conditions. The atlas is prepared by running multiple hurricanes along different tracks, with different headings, this being repeated for each of the five Saffir-Simpson hurricane categories. It is very important to highlight that by storm surge it is implied the stillwater elevation produced by the passing hurricane due to pressure setup (inverted barometer effect), wind setup (piling up of water on the coast due to onshore winds), and wave setup (due to the accumulation of water when wind waves break in the near shore). The high frequency wind-forced waves riding on top of the stillwater and capable of producing wave runup are not included and would require another type of model. Coastal flooding due to wave runup is a very important component in the overall coastal flooding problem. But its inclusion is beyond the scope of this work. • The mandate on the part of the PRCZMP was to repeat all of the above but now for the scenarios of 0.5 and 1.0 meters of sea level rise, which is simply given to the model as an input parameter • Images and figures of the inland flooding are to be presented in three formats: kmz, shapefiles, and png. • Finally, as mentioned in the section titled Caveats at the end, flooding maps near nearshore inland water bodies, and rivers, are not reliable due to the fact that for the lack of information about them, the National Geophysical Data Center (NGDC – know known as the National Centers for Environmental Information - NCEI) decided to put a “lid” over those water bodies in the Digital Elevation Model (DEM) for Puerto Rico. This “lid” lies at an elevation up to 0.5 m above Mean High Water (MHW), which is the reference vertical datum.

METHODOLOGY

Computer Models Used:

• We used version 50.99 of the tightly-coupled hydrodynamic and wind wave models ADCIRC+SWAN (called by some PADSWAN, the P standing for the parallel version), both running in the same unstructured mesh. This allows the computation of the three storm surge components: pressure, wind, and wave setups. Originally the model was run on a 48 nodes workstation. Later on we used a 448 nodes Linux cluster.

Hurricane Wind Model Used

• For the production runs used in the preparation of the maps use was made of the internal hurricane winds model available in ADCIRC, version 50.99 Stable. In this version the Holland B parameter is an output of the model. The model is the so-called asymmetric model in which, every six hours, the model reads 1-minute averaged winds at 10 m height at the four quadrants of the storm, given at a certain specified distance from the storm center. With this information the radius of maximum winds (RMW) is computed. In our case, we specified maximum winds corresponding to the chosen hurricane category and at a distance equal to a chosen radius of maximum winds. Based on the given input of central pressure, maximum winds, and radii, the hurricane wind model outputs Holland’s B value. This was made via the use of the fort.22 formatted input files, which are shown in Appendix 2, together with other fort.** files needed by ADCIRC/SWAN.

• For the validation of the models, re-analyzed winds (Automated Tropical Cyclone Forecasting – ATCF - system's best track) were used for hurricanes Georges (1998), Omar (2008), Earl (2010), and Irene (2011). The information was obtained from the following links:

• GEORGES 1998 aal071998.dat http://ftp.nhc.noaa.gov/atcf/archive/1998/

• OMAR 2008 aal152008.dat http://ftp.nhc.noaa.gov/atcf/archive/2008/ • EARL 2010 aal072010.dat http://ftp.nhc.noaa.gov/atcf/archive/2010/

• IRENE 2011 aal092011.dat http://ftp.nhc.noaa.gov/atcf/archive/2011/

• The information in the links was also supplied in the fort.22 format. • Table 1 shows the actual hurricane parameters used for each category. The central pressures are dictated by the range assigned to each Saffir-Simpson category. There is some latitude in choosing the central pressure for each category. In this study what was done was to take approximately the value falling at position 3/4 starting from the highest pressure defining the hurricane category. For example, category 2 is defined by the limits 979 to 965 mb, encompassing 15 mb. 75% of 15 is 11.25, and counting down to the eleventh position from 979 gives 969 mb, and this is the value used. This implies that for category 2 hurricanes 75% of the pressures defining its range fall above 969 mb (less intense), with approximately 25% within the category 2 pressure interval being more intense than the value of 969. The same for the maximum wind speeds (Vmax). Category 5 is open-ended. Knowing that in 1928 we were struck by a category 5 hurricane (called San Felipe in Puerto Rico and the 1928 Okeechobee hurricane elsewhere), with 1-minute sustained winds topping 160 mph (the San Juan cup anemometer reported sustained winds of 160 mph before failing; since the anemometer was 30 miles north of the storm center, winds near the landfall point were unofficially estimated as high as 200 mph - https://en.wikipedia.org/wiki/1928_Okeechobee_hurricane), and lowest central pressure 6

estimated as 929 mb, it was decided to assign a central pressure of 900 mb, and maximum winds of 150 kn (173 mph) for the simulated category 5 hurricanes. ≤ • The hurricane forward speed, Vf, is a free variable that is assigned based on typical values for the region. The value of 10 kn is such a value. Later on tests can be made with different Vf values. The radius of maximum winds, RMW, is somewhat more problematic since typically it is not included in NOAA’s historical archives, and it shows no trend, maybe with the exception that one expects stronger hurricanes to be more compact. The values chosen were decided upon using some guidance from Hsu and Yan (1998) and Mercado (1990). • Another important factor is that the wind drag coefficient used is the Powell method 2006; (itself based on Garrat, 1977) (http://www.caseydietrich.com/2010/08/01/wind-drag-based-on- storm-sectors/), it being capped at 0.003

Hurricane Headings Used:

Figures 2 - 6 show all tropical storms (Categories 1 to 5) which reside in the historical tracks depository of NOAA, starting in 1842, and passing within 75 nm of the center of the island. The figures show: • Puerto Rico has been attacked by hurricanes of all categories, including a direct hit by a category 5, in 1928. • Since due to practical considerations the computer simulations cannot cover all of the range of different historical angles of attack, and limiting the number of hurricane headings to three (because of time limitations based on the computer resources we have), it was decided to use the headings of 270° (translation directly from the east – the angle measures the heading relative to North in a clockwise sense), 290°, and 330°. To summarize, we will be using four headings of 270°, 290°, and 330° (and 60°; see below). These three headings resemble the ones shown in Figure 9.4 of Elsner and Birol-Kara, 1999, with headings of approximately 270°, 305°, 315°, and 330°.

Figure 2 – Historical tracks of hurricanes of category 1 passing within 75 nm of the center of Puerto Rico, according to the NOAA tool at http://coast.noaa.gov/hurricanes/#., and covering the period 1842 up to 2014. 7

Figure 3 - Historical tracks of hurricanes of category 2 passing within 75 nm of the center of Puerto Rico, according to the NOAA tool at http://coast.noaa.gov/hurricanes/#., and covering the period 1842 up to 2014.

Figure 4 – Historical tracks of hurricanes of category 3 passing within 75 nm of the center of Puerto Rico, according to the NOAA tool at http://coast.noaa.gov/hurricanes/#., and covering the period 1842 up to 2014.

Figure 5 – Historical tracks of hurricanes of category 4 passing within 75 nm of the center of Puerto Rico, according to the NOAA tool at http://coast.noaa.gov/hurricanes/#., and covering the period 1842 up to 2014.

Figure 6 – Historical tracks of hurricanes of category 5 passing within 75 nm of the center of Puerto Rico, according to the NOAA tool at http://coast.noaa.gov/hurricanes/#., and covering the period 1842 up to 2014. 9

Figure 7 – Tracks for “wrong way” hurricanes, moving with a component to the east. Here we show hurricanes Lenny (1999 – category 4) and Omar (2008 – category 2). The center of 75 nm radius circle is at the eastern tip of the island of Vieques.

•If we displace the center of the circle to the eastern tip of the island municipality of Vieques (see Figures 1 and 7) we can observe that there have been at least two “wrong way” hurricanes since 1842, these being Lenny (1999 - a category 4), and Omar (2008 – a category 2). For this reason it was decided to add and additional heading of 60°, since these will be the worst case for the west coast of the island. As mentioned above, this heading is not included in the SuperMOM for each hurricane category.

Table 1 – Hurricane statistics used for the five Saffir-Simpson categories.

Cat Central pressure RMW Vf Vmax Separation (mb) (nm) (kn) (kn) between tracks (nm) 1 980 25 10 78 5 2 969 25 10 92 5 3 950 20 10 108 5 4 926 15 10 131 5 5 900 10 10 150 5

Based on the above discussion, Figures 8-11 show the storm tracks actually used. Figure 12 shows a typical hurricane wind speed radial profile. The spacing between parallel tracks (5 nm) has the objective of covering each coastal point on the island with the chosen winds characteristic of each hurricane intensity based on the internal hurricane winds model included in the ADCIRC hydrodynamic model (this is the model used – more about this below). This was verified by running ADCIRC along each track and storing the maximum wind speeds for each track. Figure 13 shows a filled contour plot of the maximum wind speeds for all tracks with heading of 290°, showing that no gaps in wind speeds with values smaller than the corresponding category exist. In other words, if the spacing between tracks had been too large we would have seen regions of the coastline which did not felt the assigned maximum wind speeds. The same was done for all of the other three headings used in order to verify that no wind gaps appear.

MOM of 270° 14 tracks

Figure 8 – Collection of tracks with a heading of 270° (clockwise from North). Fourteen tracks with a separation of 5 nm were found to be sufficient for this heading. 11

MOM of 290° 19 tracks

Figure 9 – Collection of tracks with a heading of 290° (clockwise from North). Nineteen tracks with a separation of 5 nm were found to be sufficient for this heading.

MOM of 330° 28 tracks

Figure 10 – Collection of tracks with a heading of 330° (clockwise from North). Twenty eight tracks with a separation of 5 nm were found to be sufficient for this heading. 12

MOM of 60° 21 tracks

Figure 11 – Collection of tracks with a heading of 60° (clockwise from North). Twentyone tracks with a separation of 5 nm was found to be sufficient for this heading.

Figure 12- Typical (normalized) radial wind speed profile obtained from the internal hurricane wind model. This case corresponds to RMW = 10 nm for the category 5 hurricanes.

Figure 13- Painted contour plot of maximum wind speed obtained after running all of the 19 tracks shown in Figure 9. The figure shows that all computational nodes felt hurricane wind speeds very close to the nominal value corresponding to the assigned hurricane category. In this case the nominal value was 150 knt.

COMPUTATIONAL UNSTRUCTURED MESH

• Figure 14 shows the grid spacing of the computational unstructured mesh used (consisting of triangles). Figure 15 shows a zoom for the San Juan metropolitan area, showing the very high resolution afforded inside San Juan Bay. The same holds all around the island. Note that the computational mesh extends inland up to a chosen elevation above MHW, in this case 10 m. • The mesh has 860,000 nodes and 1,692,087 elements. • The input topographic and bathymetric information used to assign the elevation (by “elevations” it is meant both topography and bathymetry) at each computational node was obtained from the Digital Elevation Model (DEM) prepared by NOOA’s National Geophysical Data Center (NGDC) for tsunami flood mapping (Taylor et al., 2008). Figure 16 shows the seven DEMs prepared by NGDC, consisting of six coastal ones of 1/3 arc seconds (approximately 10 m) resolution, and one of large area coverage of 1 arc seconds (approximately 30 m) resolution. For the regions not covered by the NGDC DEMs used was made of General Bathymetric Charts of the Ocean (GEBCO) data (http://www.gebco.net/). Elevations in these DEMs are given relative to Mean High Water (MHW), a standard procedure when preparing these types of maps for emergency management, where it is assumed that the storm surge arrives at MHW (credible worst case scenario). In any case, the difference between MHW and Mean Sea Level (MSL) is small (being 0.132 m; Taylor et al., 2008). Figure 17 shows a filled depth contour plot of the mesh elevations. Figure 18 shows a color-coded plot of the grid spacing inside the island.

Figure 14 – GRID SPACING. Computational unstructured mesh (Finite Elements) used. It consists of small triangles that offer very high resolution where required, and smaller resolution otherwise. This mesh was cut out from a much bigger mesh prepared by Dr. Juan Gonzalez while a student at the University of Notre Dame. The painted contour plot shows the spatially variable grid spacing.

Figure 15 – Zoom of computational mesh in the San Juan metropolitan area showing the very high resolution inside San Juan Bay. The higher resolution inside the San Antonio Channel (south of the Isleta de San Juan), is due to the need to better resolve the narrow channel connecting San Antonio with the Condado lagoon. The blue line is the MHW shoreline. The areas not painted is because they lie above 10 m above MHW. 15

Figure 16 – Source and coverage of datasets used in compiling the Puerto Rico DEMs (boundaries in red). From NGDC (Taylor et al., 2008). 16

Figure 17- GRID ELEVATIONS. Painted contour plot of mesh elevation (i.e., both topography and bathymetry) for the whole computational domain. All of the mesh was based on a much larger (in geographical coverage) mesh obtained from Juan Gonzalez, University of Notre Dame. Bathymetry is assigned positive values.

Figure 18- Zoom close to Puerto Rico of the elevation mesh. 18

DOMAIN DECOMPOSITION

The complexity and size of the models, and size and resolution of the computational area, requires the use of parallelizing methodologies so that the models can be run using multiple CPUs on a reasonable length of wall clock time. The technique used by the coupled ADCIRC+SWAN is called Domain Decomposition, in which a program is run prior to execution of both models and, based on the number of CPUs available, it breaks the computational domain into exactly the same number of regions as CPUs, assigning to each CPU its own sub-domain of execution. In our case, since we used a cluster of 448 CPUs, then the computational mesh was sub-divided into 448 pieces. Figure 19 shows a color-coded plot of the decomposition done for the whole mesh, while Figure 20 shows a zoom close to Puerto Rico.

Figure 19- Sub-domains of the computational mesh into which the mesh has been divided based on the technique of Domain Decomposition used by ADCIRC+SWAN. 19

Figure 20 – Zoom of Figure 19 near Puerto Rico.

FRICTIONAL DISSIPATION

Land Cover for Puerto Rico

Bottom friction is parameterized using a Manning’s n formulation, with spatially-variable values based on land classification. Manning’s n roughness coefficients, or simply Manning coefficients, are spatially assigned to the mesh nodes which lie inland using a data set of land cover for Puerto Rico. The effects of land cover on surge water flow are considered by introducing various Manning coefficients based on the National Land Cover Database (NLCD – 2001 edition). A modified table of Manning coefficients, Table 2, corresponding to different land cover classes proposed by Mattocks and Forbes (2008), was employed in this study. Note that the land categories described as Developed High Intensity, Developed Medium Intensity, and Developed Low Intensity (not shown in Table 2) are reduced to Impervious. Figure 21 shows USGS Puerto Rico Land Cover Classification as of 2001 (http://www.mrlc.gov/nlcd01_data.php ).

Benthic Zones for Puerto Rico

The term benthic refers to anything associated with or occurring on the bottom of a permanent body of water. In order to assign Manning's coefficients to those nodes which lie inside the Puerto Rico benthic zones in the near shore, a NOAA data set containing information on the region's coral reefs, seagrass beds, mangrove forests, and other important habitats within the benthic zones was used as shown in Figure 22. The NOAA's National Ocean Service (2001) acquired aerial photographs for the nearshore waters of Puerto Rico, thus, creating a benthic habitat map. In Table 3, a Manning coefficient is assigned to each one of the different benthic habitat listed. These values for Manning coefficient were derived from the United Stated Geological Survey (USGS), among other reference sources for hydraulic information.

Finally, Figure 23 shows a color-coded figure of the assigned Manning coefficients. This was carried out by a program that reads the information in the tables, computes the Manning coefficient and assigns it to the nearest computational node. It should be stated that these Manning values are also used by the SWAN wave model.

Table 2 – Manning coefficient classification of the National Land Cover Class shown in Figure 21.

NLCD Land Cover Class Manning Coefficient Unclassified 0.025

Impervious 0.050

Open Space Developed 0.020

Cultivated Land 0.037

Pasture / Hay 0.033

Grassland / Herbaceous 0.034

Deciduous Forest 0.100

Evergreen Forest 0.110

Scrub / Shrub 0.050

Palustrine Forested Wetland 0.100

Palustrine Scrub Shrub Wetland 0.048

Palustrine Emergent Wetland 0.045

Estuarine Forested Wetland 0.100

Estuarine Scrub Shrub Wetland 0.048

Unconsolidated Shore 0.040

Barren Land (Rock/Sand/Clay) 0.090

Open Water 0.020

Palustrine Aquatic Bed 0.015

Table 3 - Manning coefficient classification of the Benthic Habitats shown in Figure 22.

Benthic Habitat Manning Coefficient Sand 0.0260 Mud 0.0200 Linear Reef 0.0488 Spur and Groove Reef 0.0488 Patch Reef (Individual) 0.0488 Patch Reef(Aggregated) 0.0488 Scattered Coral/Rock in Unconsolidated Sediment 0.0260 Colonized Pavement 0.0900 Colonized Bedrock 0.0900

Colonized Pavement with Sand Channels 0.0900

Reef Rubble 0.0500

Uncolonized Pavement 0.0900

Uncolonized Bedrock 0.0900

Land 0.0240 Mangrove 0.0450 Artificial 0.0488 Unknown 0.0260

Seagrass -- Continuous 0.0150

Seagrass -- Patchy(70-<90%) 0.0150

Seagrass -- Patchy(50-<70%) 0.0150

Seagrass -- Patchy(30-<50%) 0.0150

Seagrass -- Patchy(10-<30%) 0.0150

Macroalgae -- Patchy(50-<90%) 0.0150

Macroalgae – Patchy(10-<50%) 0.0150

Figure 21 – Information in the National Land Cover Database for Puerto Rico, as of 2001- http://www.mrlc.gov/nlcd01_data.php. 23

Figure 22 – Information about the benthic types near Puerto Rico, as of 2001 - http://ccma.nos.noaa.gov/ecosystems /coralreef/ usvi_pr_mapping.aspx. 24

Figure 23 – Distribution of Manning coefficients for inland and nearshore Puerto Rico. 25

STERIC EFFECT ON SEA LEVEL IN PUERTO RICO

Summer heating of surface waters raises sea level (steric effect), and this should be considered in this study. Figures 24 and 25 show the steric effect on sea level at the Magueyes Island and San Juan Bay tidal stations. For the maps prepared as part of this project (the production runs) a constant value of 0.08 m was added to the starting sea level in order to account for this effect. For the validations against historical hurricanes the value used was the one corresponding to the day of hurricane passage determined from the NOAA page.

Figure 24 – Annual steric effect at Magueyes Island (NOAA), western part of the south coast of Puerto Rico. In 2012 and 2014 high values of 0.16 m above MSL have been registered.

Figure 25 – Annual steric effect at San Juan Bay (NOAA). In 2012 and 2014 high values of 0.19 and 0.17 m, respectively, above MSL were registered.

ASTRONOMICAL TIDE VALIDATION

Before embarking in production runs necessary to prepare the storm surge maps, it is recommended to try to validate the numerical model, including the computational mesh. After all, the comparison of model results with real life observations is what gives credibility to the storm surge maps. One of the ways of validation is by comparing NOOA’s predicted tidal signals against the model output. In this case the model is run with just tidal forcing along the mesh boundaries. There is no wind input, eliminating in this way the errors and/or uncertainties due to the wind model. The NOAA predictions are based on tidal harmonic constants for different tidal stations that NOAA has computed over the years. If the bathymetry has gross errors this can be seen in phase differences between the NOAA predictions and the model output, since the tide propagation phase velocity is proportional to the square root of the water depth. For this purpose ADCIRC is only forced by supplied tidal harmonics at its open boundaries. Table 4 shows the eight harmonics used. Figure 26 shows the location of tidal stations in Puerto Rico. In this report we will show results for San Juan, Magueyes Island, Esperanza – Vieques, and Culebra. The ADCIRC model is run with just the forcing from the eight tidal harmonics, and the results shown are after a spin up of 30 days. The comparisons are shown in Figures 27 – 30. Several more tidal validations were made, but are not shown. It can be seen the good agreement between NOAA tidal predictions and model output.

Table 4: Tidal harmonics used to force the model Harmonic K1 O1 P1 Q1 N2 M2 S2 K2 Nodal 1.08834 1.14298 1.00000 1.14298 0.97338 0.97338 1.00000 1.23198 Factor Equilibrium 249.14 265.42 116.37 264.67 156.06 156.81 0.00 318.83 Argument (°) Name Luni-solar Principal Principal Major Major Principal Principal Luni-solar declinational lunar solar lunar lunar lunar solar declinational elliptical elliptical Period 23.93 26.87 24.07 26.87 12.66 12.42 12.00 11.97 (hrs)

Figure 26 – Map of Puerto Rico showing locations of tide gauges in the island.

Figure 27 - San Juan Bay tidal validation. Comparison of model output and NOAA predicition.

Figure 28 - Magueyes Island tidal validation. Comparison of model output and NOAA predicition.

Figure 29 - Esperanza, Vieques Island tidal validation. Comparison of model output and NOAA prediction.

Figure 30 - Culebra Island tidal validation. Comparison of model output and NOAA predicition.

VALIDATION AGAINST STORM SURGES

Data from four hurricanes is available for model validation: George (1998), Omar (2008), Earl (2010), and Irene (2011). Figure 31 shows a plot of these hurricanes. The following table shows the hurricane name and whether there was validation data from NOAA tide gauges, and from offshore wave buoys. Figure 32 shows the location of offshore wave buoys from which wave information could be obtained for validation purposes. Figure 33 shows the location of buoys closer to shore, of which only the CariCOOS buoys 41053 (at San Juan Bay entrance), and 42085 (near Caja de Muertos island, Ponce) are used.

Table 5. Hurricanes and NOAA tide gauges and offshore wave buoys used for validation HURRICANE NAME NOAA TIDE GAUGE OFFSHORE WAVE DATA BUOYS Hugo (1989) Yes (San Juan, Isla Magueyes) No George (1998) Yes (San Juan, Isla Magueyes, Culebra, No Esperanza-Vieques) Omar (2008) Yes (San Juan, Isla Magueyes) Yes (#41043, #42059) Earl (2010) Yes (San Juan, Isla Magueyes, Culebra, Yes (#41043, #42059) Esperanza-Vieques) Irene (2011) Yes (San Juan, Isla Magueyes, Culebra, Yes (#41043, #41115, #41053, Esperanza-Vieques) #42059)

Figure 31 - Hurricane tracks of the five hurricanes used for storm surge model validation.

Figure 32 - Location of offshore wave buoys used for validation purposes. #41053 is just offshore of the entrance to San Juan Bay (see Figure 28).

Figure 33 – Locations of instrumentation in, and near, Puerto Rico.

GEORGES 1998:

For Hurricane Georges (1998) – see Figure 34 - the only tide gauges available in Puerto Rico were San Juan Bay and Isla Magueyes. Figures 35 and 36 show the model validation for Georges at San Juan Bay and Isla Magueyes, respectively. No wave gauges were available for Georges.

Figure 33 - Track of Hurricane Georges (1998). The four filled circles along the track correspond to the first four lines in the table at the left side. From http://coast.noaa.gov/hurricanes/. Georges (1998) made landfall on September 21 at 06:00 PM and exited approximately in the very early hours of the 22nd .

Figure 34 - San Juan Bay. Georges (1998) made landfall on September 21 at 06:00 PM and exited approximately in the very early hours of the 22nd . 33

Figure 35 – Magueyes Island. Validation for Hurricane Georges (1998) based on the NOAA tide gauge station.

OMAR 2008:

Figure 37 shows the Omar (2008) trajectory. Figures 38 to 41 show the model validation for Hurricane Omar (2008), showing literally no storm surge signal. Figure 42 shows validation of the Significant Wave Height for Omar at Buoy Station 42059 in the eastern Caribbean. It should be emphasized that although the modeling did include the contribution of waves to the overall storm surge, there was no especial effort to simulate deep water waves. The SWAN wave model was run with all its input parameters in default mode.

Figure 36 -Track of Hurricane Omar (2008). The two filled circles along the track correspond to the first two lines in the table at the left side. From http://coast.noaa.gov/hurricanes/. Omar (2008) came closest to Puerto Rico between October 15, 2008, 08:00 PM (LST) and October 16, 02:00 AM (LST).

Figure 37 – Culebra tide gauge. Omar (2008) came closest to Puerto Rico between October 15, 2008, 08:00 PM (LST) and October 16, 02:00 AM (LST). 35

Figure 38 – Magueyes Island tide gauge. Omar (2008) came closest to Puerto Rico between October 15, 2008, 08:00 PM (LST) and October 16, 02:00 AM (LST).

Figure 39 – San Juan tide gauge. Omar (2008) came closest to Puerto Rico between October 15, 2008, 08:00 PM (LST) and October 16, 02:00 AM (LST). 36

Figure 40 – Esperanza, Vieques Island tide gauge. Omar (2008) came closest to Puerto Rico between October 15, 2008, 08:00 PM (LST) and October 16, 02:00 AM (LST).

Figure 41 – Observed versus simulated Significant Wave Height at Buoy Station 42059 in the eastern Caribbean. SWAN was run with all its parameters in default mode.

EARL 2010:

Figure 43 shows Earl’s trajectory. Figures 44 to 47 show validations for Hurricane Earl (2010). Figure 48 shows observed versus simulated Significant Wave Height at Buoy Station 41043 at Northeast Puerto Rico for Earl.

Figure 42 - Track of Hurricane Earl (2010). Earl came closest to the island on August 30, at 08:00 PM (approximately the third filled circle in the figure. From http://coast.noaa.gov/hurricanes/.

Figure 43 - Culebra tide gauge. Earl (2010) came closest to the island on August 30, at 08:00 PM. 38

Figure 44 – Magueyes Island tide gauge. Earl (2010) came closest to the island on August 30, at 08:00 PM.

Figure 45 – San Juan tide gauge. Earl (2010) came closest to the island on August 30, at 08:00 PM. 39

Figure 46 – Esperanza, Vieques, tide gauge. Earl (2010) came closest to the island on August 30, at 08:00 PM.

Figure 47 - Observed versus simulated Significant Wave Height at Buoy Station 41043 at Northeast Puerto Rico. SWAN was run with all its parameters in default mode.

IRENE 2011:

Figure 49 shows Irene’s (2011) trajectory. Figures 50 – 53 show storm surge validations for Irene. 40

Figure 48 - Track of Hurricane Irene (2011). The three dots along the track correspond to the first three lines in the table at the left side. From http://coast.noaa.gov/hurricanes/. Irene approximately made landfall during the first couple of hours of August 22, and left the island during the early morning of the same day.

Figure 49 – Culebra tide gauge. Irene (2011) approximately made landfall during the first couple of hours of August 22, and left the island during the early morning of the same day. 41

Figure 50 – Magueyes Island tide gauge. Irene (2011) approximately made landfall during the first couple of hours of August 22, and left the island during the early morning of the same day.

Figure 51 – San Juan tide gauge. Irene (2011) approximately made landfall during the first couple of hours of August 22, and left the island during the early morning of the same day. 42

Figure 52 – Esperanza, Vieques Island, tide gauge. Irene (2011) approximately made landfall during the first couple of hours of August 22, and left the island during the early morning of the same day.

Although no quantitative comparisons have been made, the figures show that storm surge model predictions match well with the observations. But the significant wave height predictions, although in phase with the observations in that the time occurrence of the peaks match, the model predictions tend to be much larger than the measurements. It should be mentioned that no fine-tuning of SWAN was made. It was run with its parameters in default mode.

RESULTS (MAPS OF SYNTHETIC HURRICANES)

A total of 82 tracks were run for each hurricane category. For the five categories this comes out a total of 410 hurricanes per sea level scenario. And since three sea level scenarios were evaluated (present, 0.5 and 1.0 m above present), the total number of events simulated comes out to 1230. For each event we output a kmz, a png, and three shapefile-related files (*.dbf, *.shp. and *.shx). For each category there are three sea level scenarios. For each sea level scenario there are 5 categories. For each category there are 4 headings. For each heading there is a MOM. For each category there is a SuperMOM (ensemble of all MOMs). All of the processed files (kmz, png, and shapefiles) were submitted to both the PRCZMP and CariCOOS. We kept a copy at the Physical Oceanography Laboratory of the Department of Marine Sciences, UPRM. We will show in this report the png images for the SuperMOMs for sea surface elevation for each sea level scenario. These are shown in Figures 54 to 68 (these are the png images). The data is also available as KMZ and shapefiles. For each category results are shown for sea level rise scenarios of 0.0, 0.5, and 1.0 m above the MHW vertical datum of the DEM used. Read the caption at the top of the figures for details:

• SLR – sea level rise scenario simulated (meters) • dP – hurricane central pressure (mb) 43

• [270°, 290°, 330°, 330°] – the headings simulated in this SuperMOM

• Vf = hurricane forward speed (knots)

• Vmax – maximum wind speed somewhere in the hurricane (knots)

CATEGORY 1; SLR = 0 m

Figure 53 45

CATEGORY 1; SLR = 0.5 m

Figure 54 46

CATEGORY 1; SLR = 1 m

Figure 55 47

CATEGORY 2; SLR = 0 m

Figure 56 48

CATEGORY 2; SLR = 0.5 m

Figure 57 49

CATEGORY 2; SLR = 1 m

Figure 58 50

CATEGORY 3; SLR = 0 m

Figure 59 51

C ATEGORY 3; SLR =0. 5 m

Figure 60 52

CATEGORY 3; SLR =1 m

Figure 61 53

CATEGORY 4; SLR =0 m

Figure 62 54

CATEGOR Y 4; SLR =0.5 m

Figure 63 55

CATEGORY 4; SLR =1 m

Figure 64 56

CATEGORY 5; SLR =0 m

Figure 65 57

CATEGORY 5; SLR =0.5 m

Figure 66 58

CATEGORY 5; SLR =1 m

Figure 67 59

CAVEATS

It is important to highlight the caveats involved in the preparation of hazard maps like these since it is important for the users to know what’s missing, especially if it might lead to an underestimation of the hazard. These are independent of model limitations and of input bathymetry and topography potential errors.

• It should be emphasized that the following coastal flood maps show what is called the stillwater elevations. This is what a tide gauge would measure, filtering the high-frequency (periods of, say, 20 seconds or smaller) waves. • Related with the above caveat, the maps do not show the flooding due to wave runup (the high- frequency waves propagating inland on top of the stillwater elevation and breaking nearshore, and inland, like a bore or a sheet of seawater. It is known that wave runup can make a significant contribution to coastal flooding by seawater, increasing even more the flooded areas shown in the maps. In islands like Puerto Rico runup and overtopping can easily be the major cause of coastal flooding by seawater. • The MOMs and SuperMOM for each category show the potential flooding from a set of 61 different hurricanes of that given category. They do not show the potential flooding from any particular hurricane. For that one has to go to the maps for each individual hurricane. • The effect of infragravity waves is not included. These are low-frequency (periods of a few minutes) waves, some coastally-trapped, that can temporarily increase the stillwater elevations when the wave crest is passing by. Where they occur they can be an important factor for allowing high-frequency waves to penetrate deeper inland, and increase the overtopping, and thus, inland flooding. • Based on the above three bullets, these maps are expected to show the minimum areas expected to be flooded by seawater. • No rainfall flooding, nor ponding due to rainfall, is included. Nor is the effect of flash floods moving water downslope to the coast. • The stillwater elevations are given relative to Mean High Water. That is, it is assumed that the storm surge occurs at high astronomical tide. In Puerto Rico this is not much of an issue since the tidal range is small. • These maps show potentially floodable areas under the scenario of a given category hurricane passing through the most critical trajectory for any given coastal site. That is why several hurricanes are simulated in parallel tracks, trying to be sure that all locations along the coast suffer the consequences of the worst track for that location. Any different hurricane track than the critical track will tend to show, in general, smaller flooding depths for that site. • The maps are based on the best available topographic and bathymetric data as obtained in the early 2000’s, and used for tsunami flood mapping for NOAA. The DEM resolution is 1/3 arc seconds (approximately 10 meters), and it tries to be bare-earth. That is, vegetation and infrastructure have mostly been eliminated. Information about the actual topographic roughness conditions at a given location is supplied by the use of assigned Manning coefficients to each computational node. Since the topographic and bathymetric data was obtained by the 60

use of the LiDAR technology, in places with turbid waters the signal is not capable of reaching the bottom. As a consequence, rivers are represented as an almost flat surface upriver from its river mouth. And the surface of this lid lies 0.1 to 0.5 m above Mean High Water (it is dry land). The same happens in inland water bodies like coastal lagoons (Lagunas San Jose and Torrecillas, part of the San Juan Bay Estuary). Appendix 1 further discusses this issue. • The dynamic interaction between river discharge and storm surge is not included. This could increase the storm surge and serve as an obstacle for the river discharge, thus increasing the river flooding. • The storm surge propagation upriver is not reliable due to the lack of riverine bathymetric data which is replaced by a lid. It is not known what the consequences of this lid are. • Coastal morphological changes (due to bottom erosion by waves and currents) during the storm are not included. Nor are any changes that have happened between the dates of topographic and bathymetric acquisition and the present. • Land use information and sea bottom features that are included as a computational attributes, and both necessary for friction parameterization, are based on data that might not be up to date. It will be up to a year that an update of that information will be available.

ACKNOWLEDGMENTS

We acknowledge the funding support of the Caribbean Coastal Ocean Observing System (CariCOOS), especially its director, Prof. Julio Morell. Also, of the Puerto Rico Coastal Zone Management Program, especially of its director, Mr. Ernesto Diaz. Additionally, of Mr. Harry Justiniano, IT of the Physical Oceanography Laboratory, and Dr. Juan Gonzalez, now a PhD candidate at the University of Notre Dame.

REFERENCES

Elsner, J. B., and A. Birol Kara, 1999. Hurricanes of the North Atlantic. Oxford University Press.

Garratt, J. R. 1977. “Review of drag coefficients over oceans and continents.” Monthly Weather Review, 105, 915-929.

Hsu, S. A. and Z. Yan, 1998. A note on the radius of maximum wind for hurricanes. J. Coastal Res., 14(2): 667-668.

Mattocks, C. and C. Forbes, 2008. A real-time, event-triggered storm surge forecasting system for the state of North Carolina. Ocean Modelling, V25(3-4): 95-119.

Mercado, A., 1990. Flood Insurance Study for the Commonwealth of Puerto Rico and the U.S. Virgin Islands. Vols. 1 to 5. Submitted to the Federal Emergency Management Agency, Washington, D.C.

Powell, M. D. 2006. “Final Report to the National Oceanic and Atmospheric Administration (NOAA) Joint Hurricane Testbed (JHT) Program.” 26 pp. 61

Taylor, L. A., B. W. Eakins, K. S> Carignan, R. R. Warnken, T. Sazonova, and D. C. Schoolcraft, 2008. Digital Eelevation Models of Puerto Rico: Procedures, Data Sources and Analysis. NOAA Technical Memorandum NESDIS NGDC-13.

APPENDIX 1

THE PROBLEM WITH THE NEARSHORE INLAND WATER BODIES

Figure 69 is an example of the problem with nearshore inland water bodies for which no elevation information is available. For example, at the time of the preparation of the San Juan DEM, NGDC had no information about the bathymetry for the San Juan Bay Estuary, with the exception of San Juan Bay. The figure shows contours of 0 m elevation (corresponding to the MHW shoreline, as mentioned before) in red, and contours of -1 (purple), -0.5 (blue), 0.5 (green), and 1.0 (black) m elevations. Recall that negative values correspond to bathymetry. Figure 70 also shows the same problem for the Rio Grande de Loiza.

Figure 68 – Shaded relief plot of the San Juan 10 meter resolution DEM, showing that, with the exception of San Juan Bay, the rest of the lagoons in the San Juan Bay Estuary are covered by a flat “lid” lying up to 0.5 m above Mean Sea Level (MSL). The red curve is the 0 m elevation isobaths, and the blue line is the 0.25 m elevation isobath. The lagoons with no information are shown as flat areas.

Note that, as expected, Caño Martin Peña is shown partly filled in. This can be seen by noticing that there is no red contour all along its path. Recall that red is 0.0 m elevation, and that green and black are 0.5 and 1.0 m, respectively, elevations. There are no purple and blues. 62

Also notice that there is no red (0.0 m) contour around San Jose, Torrecillas, and Canal Suarez. The same with the colors purple (-1.0 m) and blue (-0.5 m). Actually you can see that San Jose, Suarez Canal, and Torrecillas are outlined by the green (0.5 m) and black (1.0) contours. All elevations inside those water bodies have been assigned values above MHW.

As a consequence, model output results are not to be trusted near inland water bodies that have been artificially filled up.

Figure 69 – Rio Grande de Loiza 10 m resolution DEM. The river outline is shown as a flat area due to a lack of data.

APPENDIX 2

WIND FORCING

ADCIRC-SWAN (hereafter called ADSWAN) can use winds from several sources, and it also has an internal hurricane wind model. In this study the re-analyzed ATCF (Automated Tropical Cyclone Forecasting) winds were used to force the model for the validation exercises for hurricanes Georges (1998), Omar (2008), Earl (2010), and Irene (2011). By using re-analyzed winds supplied by the National Hurricane Center we try to use the best winds available in order to compare model results with observations. In theory any discrepancies between observation and model results should then be due to model setup (i.e., bathymetry) and/or model deficiencies. But it is known that even the re-analyzed winds are not perfect, and that should be taken into consideration. 63

For production runs (the ones to be used for the preparation of the coastal inundation maps), the internal hurricane winds model was used.

The description that follows is based on the use of version 49 of ADCIRC, which has already been updated now by version 50. Based on version 49, in both cases (validation and production runs), the input meteorological data was given in the NWS = 9 format, which was later changed to the NWS = 19 format by running the supplied aswip.f Fortran program. NWS is one the many input parameters that have to be supplied to ADCIRC. It is supplied in the Model Parameter and Periodic Boundary Condition File (fort.15). Setting NWS = 9 tells the model that the meteorological data will be supplied as a single meteorological input file. In version 50, NWS = 9 is no longer used and has been replaced by NWS = 19. Here we present the ADCIRC manual notes for NWS = 9:

Notes for NWS = 9 Hurricane parameters are read in from the Single File Meteorological Forcing Input File. It is assumed that the first entry in the Single File Meteorological Forcing Input File corresponds to the beginning of the model run (e.g., the cold start time). Wind velocity and atmospheric pressure are calculated at exact finite element mesh node locations and directly coupled to ADCIRC at every time step using the asymmetric hurricane vortex formulation (Mattocks et al, 2006; Mattocks and Forbes, 2008) based on the Holland gradient wind model. The input file is fixed width (not comma separated values or csv) and is assumed to correspond to the ATCF Best Track/Objective Aid/Wind Radii Format. Historical tracks, real-time hindcast tracks and real-time forecast tracks may be found in this format. This option uses the radii at specific wind speeds (34, 50, 64, 100 knots) reported in the four quadrants (NE, SE, SW, NW) of the storm to calculate the radius of maximum winds as a function of the azimuthal angle. Garret's formula is used to compute wind stress from the wind velocity. In order to use the NWS=9 option, the file needs to be in best track format. The forecast period (column #6) needs to be edited to reflect the time of the forecast/nowcast for each track location (each line) in hours from the start of the simulation (0, 6, 12, 18, etc). The original data in that column depends on what type of best track format data is being used. The original data might have 0 or other numbers in that column. See: http://www.nrlmry.navy.mil/atcf_web/docs/database/new/abrdeck.html It is suggested that users change the "BEST" tech type to "ASYM" in column 5 in the fort.22 file to denote that the file has been modified to accommodate the asymmetric wind formulation (the simulation time in hours in the 6th column has been added, etc.) so it will not get confused in the future with a best track file.

The way it goes is as follows. For running a validation attempt an ATCF file is downloaded and the relevant information is manually written as shown in the table below (which is written in the NWS = 9 format required by the fort.22 files).

TABLE 2.1

001 2014090100 ASYM 000 156934N 661656W 150 0900 150 0010 0010 0010 0010 1013 010 330 010 SYNTHET, D 001 2014090101 ASYM 001 158388N 662519W 150 0900 150 0010 0010 0010 0010 1013 010 330 010 SYNTHET, D 001 2014090102 ASYM 002 159842N 663384W 150 0900 150 0010 0010 0010 0010 1013 010 330 010 SYNTHET, D 001 2014090103 ASYM 003 161296N 664251W 150 0900 150 0010 0010 0010 0010 1013 010 330 010 SYNTHET, D 001 2014090104 ASYM 004 162749N 665118W 150 0900 150 0010 0010 0010 0010 1013 010 330 010 SYNTHET, D 001 2014090105 ASYM 005 164202N 665987W 150 0900 150 0010 0010 0010 0010 1013 010 330 010 SYNTHET, D 001 2014090106 ASYM 006 165654N 666858W 150 0900 150 0010 0010 0010 0010 1013 010 330 010 SYNTHET, D 001 2014090107 ASYM 007 167106N 667729W 150 0900 150 0010 0010 0010 0010 1013 010 330 010 SYNTHET, D 001 2014090108 ASYM 008 168558N 668603W 150 0900 150 0010 0010 0010 0010 1013 010 330 010 SYNTHET, D 001 2014090109 ASYM 009 170009N 669477W 150 0900 150 0010 0010 0010 0010 1013 010 330 010 SYNTHET, D 64

001 2014090110 ASYM 010 171460N 670353W 150 0900 150 0010 0010 0010 0010 1013 010 330 010 SYNTHET, D 001 2014090111 ASYM 011 172910N 671231W 150 0900 150 0010 0010 0010 0010 1013 010 330 010 SYNTHET, D 001 2014090112 ASYM 012 174360N 672110W 150 0900 150 0010 0010 0010 0010 1013 010 330 010 SYNTHET, D 001 2014090113 ASYM 013 175810N 672991W 150 0900 150 0010 0010 0010 0010 1013 010 330 010 SYNTHET, D 001 2014090114 ASYM 014 177259N 673873W 150 0900 150 0010 0010 0010 0010 1013 010 330 010 SYNTHET, D 001 2014090115 ASYM 015 178708N 674756W 150 0900 150 0010 0010 0010 0010 1013 010 330 010 SYNTHET, D 001 2014090116 ASYM 016 180156N 675641W 150 0900 150 0010 0010 0010 0010 1013 010 330 010 SYNTHET, D 001 2014090117 ASYM 017 181604N 676528W 150 0900 150 0010 0010 0010 0010 1013 010 330 010 SYNTHET, D 001 2014090118 ASYM 018 183051N 677416W 150 0900 150 0010 0010 0010 0010 1013 010 330 010 SYNTHET, D 001 2014090119 ASYM 019 184498N 678306W 150 0900 150 0010 0010 0010 0010 1013 010 330 010 SYNTHET, D 001 2014090120 ASYM 020 185945N 679197W 150 0900 150 0010 0010 0010 0010 1013 010 330 010 SYNTHET, D

The following information describes the columns in the table above.

The NWS=9 option requires the following variables in the fort.22 file in a best track format: 1) Hurricane number (column 1) 2) Date (column 2) 3) Type of vortex (column 3) 4) Forecast time in hours (column 4); enter the time in hours in each record starting at 0 5) Latitude of the eye (column 5) 6) Longitude of the eye (column 6) 7) Maximum sustained wind speed in knots (column 7) 8) Minimum sea level pressure in MB (column 8) 9) Wind intensity in knots of the radii defined in the record (34, 50, 64 or 100 knots) (column 9) 10) Radius of specified wind intensity for quadrants 1, 2, 3, 4 in NM (columns 10, 11, 12, 13); ≠ 0 11) ) Background pressure in MB (column 14); a standard value of 1013 can be used 12) The radius of maximum winds (column 15) 13) Hurricane heading (column 16) 14) Translation speed (column 17) 15) Descriptive material (columns 18 and 19)

Next the above formatted file is fed to ASWIP.F and the table is re-written in the following format (NWS = 19)

TABLE 2.2

Required columns: 6 7 8 9 10 12 14 15 16 17 18

AL, 13, 2003090800, , ASYM, 0, 158N, 397W, 80, 976, HU, 34, NEQ, 80, 80, 80, 80, 1013, 220, 25, 0, 0, ... AL, 13, 2003090806, , ASYM, 6, 165N, 409W, 95, 966, HU, 34, NEQ, 90, 90, 90, 90, 1013, 220, 25, 0, 0, ... AL, 13, 2003090812, , ASYM, 12, 171N, 420W, 110, 952, HU, 34, NEQ, 90, 90, 90, 90, 1013, 220, 25, 0, 0, ... AL, 13, 2003090818, , ASYM, 18, 176N, 431W, 110, 952, HU, 34, NEQ, 120, 100, 100, 120, 1013, 220, 25, 0, 0, ... AL, 13, 2003090900, , ASYM, 24, 182N, 441W, 115, 948, HU, 34, NEQ, 120, 100, 75, 120, 1013, 200, 25, 0, 0, ...

The information below describes the NWS = 19 formatted file:

• Forecast time in hours (column 6); enter the time in hours in each record starting at 0 • Latitude of the eye (column 7) • Longitude of the eye (column 8) • Maximum sustained wind speed in knots (column 9) • Minimum sea level pressure in mb (column 10) • Wind intensity in knots of the radii defined in the record (34, 50, 64 or 100 knots) (column 12) • Radius of specified wind intensity for quadrants 1, 2, 3, 4 in NM (columns 14, 15, 16, 17); ? 0 • Background pressure in mb (column 18); a standard value of 1013 can be used 65

• Rmax as reported in the ATCF BEST TRACK file in column 20 • Storm Name in Column 28 ATCF file format • Time Record number in column 29. There can be multiple lines for a given time record depending on the number of isotachs reported in the ATCF File • number of isotachs reported in the ATCF file for the corresponding Time record. • Columns 31-34 indicate the selection of radii for that particular isotach. 0 indicates do not use this radius, and 1 indicates use this radius and corresponding wind speed. • Columns 35-38 are the designated Rmax values computed for each of the quadrants selected for each particular isotach. • Column 39 is the Holland B parameter computed using the formulas outlines in the Holland paper, and implemented using the aswip program.

When running a production run, for which there is no ATCF file to prepare a table like Table 2.1, a Matlab script was made to produce a Table 2.1 (in format NWS = 9) for each hurricane category and heading angle, and the rest is processed as already described here.