Probabilistic Hurricane Storm Surge P Surge Arthur Taylor

Probabilistic Hurricane Storm Surge (P -Surge) Arthur Taylor Meteorological Development Laboratory, National Weather Service January 20, 2008

Introduction The Sea, Lake, and Overland Surges from Hurricanes (SLOSH) model is the NWS’s operational hurricane storm surge model. • The NWS uses composites of its results to predict potential storm surge flooding for evacuation planning • National Hurricane Center (NHC) begins operational SLOSH runs 24 hours beforecast hurricane landfall Probabilistic Storm Surge 2008

Introduction NHC’s operational SLOSH runs are based on a single NHC forecast track and its associated parameters. • When provided accurate input, SLOSH results are within 20% of high water marks. • Track and intensity prediction errors cause large errors in SLOSH forecasts and can overwhelm the SLOSH results. Probabilistic Storm Surge 2008

Hurricane Ivan: A case study Probabilistic Storm Surge 2008

Probabilistic Storm Surge Methodology Use an ensemble of SLOSH runs to create probabilistic storm surge (p-surge) • Intended to be used operationally so it is based on NHC’s official advisory. • P-surge’s ensemble perturbations are determined by statistics of past performance of the advisories. • P-surge uses a representative storm for each portion of the error distribution space rather than a random sampling Probabilistic Storm Surge 2008

Input Parameters for SLOSH A single run of SLOSH requires the following parameters: • Track (Location and Forward Speed) • Pressure • Radius of Maximum Winds (Rmax) Probabilistic Storm Surge 2008

Errors used by P-surge The ensemble is based on distributions of the following: • • Cross track error (impacts Location) Along track error (impacts Forward Speed) Intensity error (impacts Pressure) Rmax error Probabilistic Storm Surge 2008

P-surge Error Distributions The error distributions for cross track, along track, and intensity are determined by: • Calculating the regression of the yearly mean error • Assuming a normal error distribution • Determining the standard deviation (sigma) based on: Probabilistic Storm Surge 2008

Regression of Yearly Mean Error To calculate the yearly mean error: • The forecasts from the advisories were compared with observations, represented by the 0 hour information from the corresponding later advisories. • The errors were averaged by year • Regression curves were calculated and plotted for each forecast hour (12, 24, 36, …) • A mean error value was determined from where the regression curve crossed a chosen year. Probabilistic Storm Surge 2008

Example of 24 -hour Cross Track Error Regression Plot The 2004 error regression value 34. 8 was chosen as the 24 -hour mean cross track error Probabilistic Storm Surge 2008

Rmax Error Distributions For Rmax, we can’t assume a normal distribution since the error is bounded. To calculate the Rmax error distributions: • Group the values in bins according to: • The forecasts from the advisories were matched to the 0 hour estimate, which was treated as an observation • The probability density function (PDF) and cumulative density function (CDF) were plotted for each bin and forecast hour (12, 24, 36, …) • Since we chose to use 3 storm sizes (small 30%, medium 40%, large 30%) we determined the 0. 15, 0. 5, and 0. 85 values of the CDF for each bin and forecast hour. Probabilistic Storm Surge 2008

PDF for Rmax Errors Bin 0 -3

. 85 = small size. 50 = medium size. 15 = large size CDF for Rmax Errors Bin 0 -3

Example: Katrina Advisory 23 Probabilistic Storm Surge 2008

Cross Track Variations To vary the cross track storms, we consider the coverage and the spacing. Chose to cover 90% of the area under the normal distribution. • This was 1. 645 standard deviations to the left and right of the central track Chose to space the storms Rmax apart at the 48 hour forecast. • Storm surge is typically highest one Rmax to the right of the landfall point. So for proper coverage, we wanted the storms within Rmax of each other. Probabilistic Storm Surge 2008

Example: Cross Track Error Probabilistic Storm Surge 2008

Varying the Other Parameters: Size: Small (30%), Medium (40%), Large (30%) Forward Speed: Fast (30%), Medium (40%), Slow (30%) Intensity: Strong (30%), Medium (40%), Weak (30%) Probabilistic Storm Surge 2008

Assigning Weights Cross Track Weight 12. 43% 23. 25% 28. 65% 23. 25% 12. 43% Along Track Slow 30% 3. 729% 6. 975% 8. 595% 6. 975% 3. 729% Along Track Medium 40% 4. 972% 9. 300% 11. 460% 9. 30% Along Track Fast 30% 3. 729% 6. 975% 8. 595% 4. 972% 6. 975% 3. 729% This is repeated for other two dimensions (Rmax weights, Intensity weights) A representative storm is run for each cell in the 4 dimensional (Cross, Along, Rmax, Intensity) error space. Actual number of Cross Track weights depends on Rmax. Probabilistic Storm Surge 2008

Putting it all together 1) Calculate initial SLOSH input from NHC advisory 2) Determine which size distribution to use, based on the size -bin of the storm. Iterate over the size 3) Calculate the cross track spacing, a function of the size. Iterate over the cross tracks, stepping by the spacing and covering 1. 645 standard deviations to left and right 4) Iterate over the along tracks, creating slow, medium and fast storms 5) Iterate over the intensity, creating weak, medium, and strong storms. 6) Assign a weight to the storm (cross track weight * along track weight * intensity weight * size weight) 7) Perform all SLOSH runs Probabilistic Storm Surge 2008

Product 1: Probability of exceeding X feet To calculate the probability of exceeding X feet, we look at the maximum each cell in each SLOSH run attained. • If that value exceeds X, we add the weight associated with that SLOSH run to the total. • Otherwise we don’t increase the total. • The total weight is considered the probability of exceeding X feet. Example: 5 storms have weights of 0. 1, 0. 2, 0. 4, 0. 2, 0. 1, and the first 2 exceeded X feet in a given cell. The probability of exceeding X feet in that cell is: • 0. 1 + 0. 2 = 30% Probabilistic Storm Surge 2008

Katrina Adv 23: Probability >= 5 feet of storm surge Probabilistic Storm Surge 2008

Product 2: Height exceeded by X percent of the ensemble storms. Determine what height to choose in a cell so that there is a specified probability of exceeding it. • For each cell, sort the heights of each SLOSH run. • From the tallest height downward, add up the weights associated with each SLOSH run until the given probability is exceeded. • The answer is the height associated with the last weight added. Example: 5 storms have surge values of 3, 6, 5, 2, 4 feet and respective weights of. 1, . 2, . 4, . 2, . 1. • • • Make ordered pairs of the numbers: (3, . 1), (6, . 2), (5, . 4), (2, . 2), (4, . 1) Sort by surge height: (6, . 2), (5, . 4), (4, . 1), (3, . 1), (2, . 2) Height exceeded by 60% of storms = 4 (. 6 <. 2 +. 4 +. 1) Probabilistic Storm Surge 2008

Katrina Adv 23: 10% of ensemble storms exceed this height Probabilistic Storm Surge 2008

Is it Statistically Reliable? If we forecast 20% chance of storm surge exceeding 5 feet, does surge exceed 5 feet 20% of the time? • Create forecasts for various projections and thresholds • Get a matching storm surge observation Problem: Insufficient observations • Observations are made where there has been surge, so there is a bias toward higher values. • Storm surge observations contaminated by waves and astronomical tide issues. • Number of hurricanes making landfall is relatively small. Result: 340 observations for 11 Storms from 1998 -2005 Probabilistic Storm Surge 2008

Point Observations 11 Storms (340 Observations): • Dennis 05, Katrina 05, Wilma 05, Charley 04, Frances 04, Ivan 04, Jeanne 04, Isabel 03, Lili 02, Floyd 99, Georges 98 STORM Katrina 05 Ivan 04 Isabel 03 Lili 02 Floyd 99 Georges 98 Dennis 05 Wilma 05 Charley 04 Jeanne 04 Frances 04 OBS % OF TOTAL OBS 99 29. 12% 50 14. 71% 44 12. 94% 40 11. 76% 37 10. 88% 32 9. 41% 25 7. 35% 5 1. 47% 4 1. 18% 3. 88% 1. 29% Probabilistic Storm Surge 2008 OF THE 340 OBSERVATIONS, 2. 35% 16. 18% 35. 00% 61. 18% (8/340) ARE (55/340) ARE (119/340)ARE (208/340)ARE < 2 < 5 < 7 < 10 FEET

>5 ft Forecasts (Point) 12 hr 24 hr 36 hr 48 hr

>7 ft Forecasts (Point) 12 hr 24 hr 36 hr 48 hr

> 10 ft Forecasts (Point) 12 hr 24 hr 36 hr 48 hr

Gridded Analysis In order to deal with the paucity of observations, we wanted to use an analysis field as observations. Used SLOSH hindcast runs. • NHC used best historical information for input • Given accurate input, model results are within 20% of high water marks. Advantage: • Observation at every grid point (on the order of 106) • Observations are made where there is little surge. Disadvantage: • Used same model in analysis as we did in p-surge method. Probabilistic Storm Surge 2008

>5 ft Forecasts (Gridded) 12 hr 24 hr 36 hr 48 hr

>7 ft Forecasts (Gridded) 12 hr 24 hr 36 hr 48 hr

>10 ft Forecasts (Gridded) 12 hr 24 hr 36 hr 48 hr

Where can you access our product? http: //www. weather. gov/mdl/psurge When is it available? • Beginning when the NHC issues a hurricane watch or warning for the continental US • Available approx. 1 -2 hours after the advisory release time. Probabilistic Storm Surge 2008

Current Development • We were “experimental” in 2007, and plan on becoming “operational” in 2008. • We have added the data to the NDGD (National Digital Guidance Database), and are now working on delivering the data to AWIPS. • We are developing more training material. • We are updating the error statistics used in our calculations based on the 2007 storm season, and will continue to investigate the reliability diagrams. Probabilistic Storm Surge 2008

Future Development We would like to: • Include probability over a time range, both incremental and cumulative. • Allow interaction with the data in a manner similar to the SLOSH Display program. • Investigate its applicability to Tropical storms. • Add gridded astronomical tides to forecast probabilistic total water levels. Probabilistic Storm Surge 2008