An Improved Wind Probability Program: A Year 2 Joint Hurricane Testbed Project Update Mark DeMaria and John Knaff, NOAA/NESDIS, Fort Collins, CO Stan Kidder, CIRA/CSU, Fort Collins, CO Buck Sampson, NRL, Monterey, CA Chris Lauer and Chris Sisko, NCEP/TPC, Miami, FL Presented at the Interdepartmental Hurricane Conference March 5, 2009

Monte Carlo Wind Probability Model Estimates probability of 34, 50 and 64 kt wind to 5 days Implemented at NHC for 2006 hurricane season Replaced Hurricane Strike Probabilities 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

1000 Track Realizations 64 kt h Cumulative Probabilities MC Probability Example Hurricane Ike 7 Sept UTC

Project Tasks 1.Improved Monte Carlo wind probability program by using situation-depending track error distributions Track error depends on Goerss Predicted Consensus Error (GPCE) 2.Improve timeliness by optimization of MC code 3.Update NHC wind speed probability product Extend from 3 to 5 days Update probability distributions (was based on )

Tasks 2 and 3 Completed Optimized code implemented for 2007 season –Factor of 6 speed up Wind Speed Probability Table –Calculated directly from MC model intensity realizations –Implemented for 2008 season

Task 1: Forecast Dependent Track Errors Use GPCE input as a measure of track uncertainty Divide NHC track errors into three groups based on GPCE values –Low, Medium and High For real time runs, 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

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 In 2008, GPCE predicts TVCN error –GFS, UKMET, NOGAPS, GFDL, HWRF, GFDN, ECMWF

72 hr Atlantic NHC Along Track Error Distributions Stratified by GPCE

2008 Evaluation Procedure GPCE version not ready for 2008 real time parallel runs Re-run operational and GPCE versions for 169 Atlantic cases within 1000 km of land at t=0 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 NHC breakpoints

GPCE MC Model Evaluation Web Page

Individual Forecast Case Page

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

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

Quantitative Evaluation Calculate probabilities at NHC breakpoints –Operational and GPCE versions 34, 50 and 64 kt 12 hr cumulative and incremental to 120 h –169 forecasts X 257 breakpoints = 43,433 data points at each forecast time Two evaluation metrics –Brier Score –Optimal Threat Score

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

Brier Score (BS) Common metric for probabilistic forecasts P i = MC model probability at a grid point (0 to 1) O i = “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

Brier Score Improvements 2008 GPCE MC Model Test Cumulative Incremental

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% Find maximum TS possible Compare best TS for GPCE and operational model runs a b c Forecast Area Observed Area

Threat Score Improvements 2008 GPCE MC Model Test Cumulative Incremental

Potential Impact of GPCE on Hurricane Warnings Automated hurricane warning guidance from MC probabilities under development –Schumacher et al. (2009 IHC) Warning algorithm run for Hurricane Gustav (2008) –Operational and GPCE versions

Summary Code optimization and wind speed table product are complete –Implemented before 2007 and 2008 seasons GPCE-dependent MC model –Tested on 169 Atlantic cases from 2008 –Results are qualitatively reasonable –Improves Brier Score at all time periods relative to operational MC model –Improves Threat Score at most time periods Not tested in the eastern and western Pacific