1 / 35

Probabilistic Hurricane Storm Surge (P-Surge)

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.

Télécharger la présentation

Probabilistic Hurricane Storm Surge (P-Surge)

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.


Presentation Transcript

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

  2. 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 before forecast hurricane landfall

  3. 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.

  4. Hurricane Ivan: A case study

  5. 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

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

  7. 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

  8. 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:

  9. 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.

  10. 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

  11. 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.

  12. PDF for Rmax Errors Bin 0-3

  13. .85 = small size .50 = medium size .15 = large size CDF for Rmax Errors Bin 0-3

  14. Example: Katrina Advisory 23

  15. 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.

  16. Example: Cross Track Error

  17. 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%)

  18. Assigning Weights • 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.

  19. Putting it all together • Calculate initial SLOSH input from NHC advisory • Determine which size distribution to use, based on the size-bin of the storm. Iterate over the size • 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 • Iterate over the along tracks, creating slow, medium and fast storms • Iterate over the intensity, creating weak, medium, and strong storms. • Assign a weight to the storm (cross track weight * along track weight * intensity weight * size weight) • Perform all SLOSH runs

  20. 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%

  21. Katrina Adv 23: Probability >= 5 feet of storm surge

  22. 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)

  23. Katrina Adv 23: 10% of ensemble storms exceed this height

  24. 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

  25. 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 OBS % OF TOTAL OBS Katrina 05 99 29.12% Ivan 04 50 14.71% Isabel 03 44 12.94% Lili 02 40 11.76% Floyd 99 37 10.88% Georges 98 32 9.41% Dennis 05 25 7.35% Wilma 05 5 1.47% Charley 04 4 1.18% Jeanne 04 3 .88% Frances 04 1 .29% OF THE 340 OBSERVATIONS, 2.35% (8/340) ARE < 2 FEET 16.18% (55/340) ARE < 5 FEET 35.00% (119/340)ARE < 7 FEET 61.18% (208/340)ARE < 10 FEET

  26. >5 ft Forecasts (Point) 12hr 24hr 36hr 48hr

  27. >7 ft Forecasts (Point) 12hr 24hr 36hr 48hr

  28. > 10 ft Forecasts (Point) 12hr 24hr 36hr 48hr

  29. 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.

  30. >5 ft Forecasts (Gridded) 12hr 24hr 36hr 48hr

  31. >7 ft Forecasts (Gridded) 12hr 24hr 36hr 48hr

  32. >10 ft Forecasts (Gridded) 12hr 24hr 36hr 48hr

  33. 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.

  34. 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.

  35. 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.

More Related