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Incorporating Ensemble Information into National Hurricane Center Wind Speed Probability Products

Incorporating Ensemble Information into National Hurricane Center Wind Speed Probability Products. Mark DeMaria NOAA/NESDIS/RAMMB NCAR-CSU Collaborative Tropical Cyclone Meeting Boulder, CO 10 February 2010. Outline. Monte Carlo TC wind speed probability model

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Incorporating Ensemble Information into National Hurricane Center Wind Speed Probability Products

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  1. Incorporating Ensemble Information into National Hurricane Center Wind Speed Probability Products Mark DeMaria NOAA/NESDIS/RAMMB NCAR-CSU Collaborative Tropical Cyclone Meeting Boulder, CO 10 February 2010

  2. Outline • Monte Carlo TC wind speed probability model • Improvement of the MC model using track model ensemble spread • Utilizing the MC model as a prototype for ensemble forecast products

  3. The Monte Carlo Wind Probability Model • Include uncertainty in TC track, intensity and structure forecasts • Replaced NHC’s strike probability program in 2006 • Interaction of track, intensity structure errors, especially near land, made fitting error distributions to analytic distributions inaccurate • Monte Carlo approach useful for situations with complicated geometry, but well-defined interaction rules • Originally developed for scattering problems • Text and graphical products • Versions for Atlantic and east/central/western N. Pacific

  4. Monte Carlo Wind Probability Model • Estimates probability of 34, 50 and 64 kt wind to 5 days • 1000 track realizations from random sampling NHC track error distributions • Intensity of realizations from random sampling NHC intensity error distributions • Special treatment near land • Wind radii of realizations from radii CLIPER model and its radii error distributions • Serial correlation of errors included • Probability at a point from counting number of realizations passing within the wind radii of interest

  5. Special Treatment Near Land • Forecast position over water, but “realization” over land • Replace NHC intensity with Kaplan and DeMaria inland decay model forecast • Forecast position over land, but realization over water • Replace NHC intensity with “persistence” forecast from last NHC point over water • Inland intensity check (added in 2008) • Limit final intensity (forecast + random perturbation) to max observed as a function inland • Kill storms off if max wind drops below 20 kt

  6. Serial Correlation of Errors • Initial formulation Et+12 = Et+12 random • Revised formulation Et+12 = a + bEt + Et+12 random No serial correlation With serial correlation First 10 track realizations for Hurricane Ike 07 Sept 2008 12 UTC

  7. Convergence of the MC model • Theoretical analysis of MC methods • Empirical test • Run with varying N • Compare to run with N=500,000 • N=1000 in operational model 64 kt wind probability error as a function of the number of realizations N for Hurricane Ike (2008) case

  8. MC Probability Example Hurricane Bill 20 Aug 2009 00 UTC 1000 Track Realizations 34 kt 0-120 h Cumulative Probabilities

  9. Forecast Dependent Track Errors • Use GPCE input as a measure of track uncertainty • GPCE = Goerss Predicted Consensus Error • Divide NHC track errors into three groups based on GPCE values • Low, Medium and High • Use probability distribution for real time GPCE value tercile • Different forecast times can use different distributions • Relies on relationship between NHC track errors and GPCE value

  10. Goerss Predicted Consensus Error (GPCE) • Predicts error of CONU track forecast • Consensus of GFDI, AVNI, NGPI, UKMI, GFNI • GPCE Input • Spread of CONU member track forecasts • Initial latitude • Initial and forecasted intensity • Explains 15-50% of CONU track error variance • GPCE estimates radius that contains ~70% of CONU verifying positions at each time • Since 2008, GPCE predicts TVCN error • GFS, UKMET, NOGAPS, GFDL, HWRF, GFDN, ECMWF

  11. 72 hr Atlantic NHC Along Track Error Distributions Stratified by GPCE

  12. 2009 Evaluation Procedure • Run operational and GPCE versions • 149 Atlantic forecasts • 333 east Pacific forecasts • 519 west Pacific forecasts • Atlantic, east Pacific run in real time starting Sept 2009 • Qualitative Evaluation: Post 34, 50, 64 kt probabilities on web page for NHC • Operational, GPCE and difference plots • Quantitative Evaluation: Calculate probabilistic forecast metrics from output on 1 deg lat/lon grid over basin-wide domains

  13. GPCE MC Model Evaluation Web Pages http://rammb.cira.colostate.edu/research/tropical_cyclones/tc_wind_prob_2009/gpce.asp

  14. Quantitative Evaluation • Two evaluation metrics: Brier Score, Threat Score • Compare operational and GPCE versions AL01-AL11 WP01-WP28 EP01-EP20

  15. Brier Score (BS) • Common metric for probabilistic forecasts • Pi= MC model probability at a grid point (0 to 1) • Oi= “Observed probability” (=1 if yes, =0 if no) • Perfect BS =0, Worst =1 • Calculate BS for GPCE and operational versions • Skill of GPCE is percent improvement of BS • Possible problem if most of domain has zero probability

  16. Brier Score Improvements Atlantic West Pacific East Pacific

  17. Threat Score (TS) • Choose a probability threshold to divide between yes or no forecast • Calculate Threat Score (TS) • Repeat for wide range of thresholds • Every 0.5% from 0 to 100% • Average TS over all probability thresholds a c b Observed Area Forecast Area

  18. Threat Score Improvements Atlantic West Pacific East Pacific

  19. Transition to Ensemble Forecast Systems Track, intensity, structure from global/regional model ensembles Replacement of tracks from global/regional models ensembles Track error modified by GPCE input Operational MC model

  20. Impact of Ensemble NumberIke 2008 7 Sept 12 UTC N=10 N=100 N=10000 N=1000

  21. Probability Products • Operational • Graphical/text displays of 34, 50, 64 kt wind probabilities to 120 hr • Wind speed probability table • Experimental • NHC error “cone” from MC model • Objective guidance for watches/warnings • Landfall timing and intensity distributions • Automated generation of WFO web products • Ideas for new products • HFIP Ensemble Workshop, April 2010

  22. NHC Operational Probability Products

  23. NHC Probability Cone • Operational product shows 67th percentile of NHC track errors from past 5 years • Experimental version uses MC model realizations

  24. Objective Guidance for Hurricane Watches and Warnings • Use 48 hr cumulative probability thresholds for raising, lowering warnings • P=8% to add warning • P=0% to remove warning NHC Objective Hurricane Gustav (2008) Example

  25. Landfall Timing, Intensity Distributions

  26. WFO Local Applications

  27. Summary • Monte Carlo probability model replaced NHC’s strike probabilities in 2006 • GPCE-dependent MC model • First step towards use of ensembles in NHC operational products • Improves Threat Score at all time periods in all basins in 2009 • Improves Brier Score at most time periods • Transition towards increased use of model ensembles • MC model can be used to develop prototype ensemble products

  28. Back-up Slides

  29. NHC Strike Probabilities • First implemented during Hurricane Alicia (1983) • Sheets (1983), BAMS • Developed to convey track error • De-emphasize “skinny black line” • Complement to deterministic track, intensity, structure forecast • Target audience: “Sophisticated” users, not general public

  30. Strike Probability Methodology • Estimate probability of a TC center coming within 60 nmi of any given point • Fit bi-variate normal distributions to previous 10 years of NHC track errors • Weak dependence on current storm characteristics • Maximum probability constraints included • Text product at fixed locations, mostly coastal • Lead times out to 72 hr • Graphical products added in mid-1990’s

  31. Original Strike Probabilities

  32. Strike Probability Limitations • Only included track forecast uncertainty • Event not directly related to impact • Storm center within 60 nmi • Probabilities only extended to 72 h • NHC forecast extended to 120 h in 2003 • Probability update program maintained by J. Jarrell who retired in 1999

  33. MC Model Applications • NHC/JTWC text and graphical products • Input to automated WFO product generation during hurricane landfalls • Space shuttle operation decisions • Input to retail store planning algorithms • Watch/Warning objective guidance • Forecast improvement impact study for HFIP

  34. Forecast-Dependent Probabilities • Operational MC model uses basin-wide track error distributions • Can situation-dependent track distributions be utilized? Track plots courtesy of J. Vigh, CSU

  35. Tropical Storm Hanna 5 Sept 2008 12 UTC 34 kt 0-120 h cumulative probability difference field (GPCE-Operational) All GPCE values in “High” tercile

  36. Hurricane Gustav 30 Aug 2008 18 UTC 64 kt 0-120 h cumulative probability difference field (GPCE-Operational) All GPCE values in “Low” tercile

  37. Individual Forecast Case Page

  38. Operational and GPCE Probabilities Calculated at 257 NHC Breakpoints West Coast of Mexico and Hawaii breakpoints excluded to eliminate zero or very low probability points

  39. Brier Score Improvements2008 GPCE MC Model Test Cumulative Incremental

  40. Threat Score Improvements2008 GPCE MC Model Test Cumulative Incremental

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