Continued Development of Tropical Cyclone Wind Probability Products John A. Knaff – Presenting CIRA/Colorado State University and Mark DeMaria NOAA/NESDIS.

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Presentation transcript:

Continued Development of Tropical Cyclone Wind Probability Products John A. Knaff – Presenting CIRA/Colorado State University and Mark DeMaria NOAA/NESDIS Presentation at 60 th Interdepartmental Hurricane Conference Mobile, AL 22 March 2006

60th IHC22 March Mobile, AL2 Outline Training Activities Verification Activities Results/insights from examination of hurricane warning break points

60th IHC22 March Mobile, AL3 Training Activities Mark DeMaria coordinated with Rick Knabb to provide feedback on a TPC/NHC training session. Several cases rerun for 2004 and 2005 –For Pablo Santos, Miami WFO for an experimental algorithm that uses the probabilities. –Web page with examples and a product description

60th IHC22 March Mobile, AL4

60th IHC22 March Mobile, AL5 Verification: Current Status Developed: –Input data handling (GRIB, ATCF….) –Statistical Methods Scalar measures of skill, accuracy, confidence Conditional measures –Methods to assess deterministic forecasts Remaining –Treatment of the OFCL forecast & wind radii through 5 days. –Integrating the pieces. –How to Interpret the statistics and optimize use.

60th IHC22 March Mobile, AL6 Statistical methods: Probability Bias Mean Forecast Probabilities (F i ) minus the Mean Observed Frequencies (E i ) = 1 or 0 Determines if the probabilities over/under forecast the outcome.

60th IHC22 March Mobile, AL7 Statistical Methods: Brier Score Mean of square of the Forecast Probabilities (F i ) minus the Observed Frequencies (E i ) = 1 or 0 Measures the Mean Square Errors (Accuracy) associated with a probabilistic forecast

60th IHC22 March Mobile, AL8 Statistical Methods: Brier Skill Score A scalar skill score comparing a given Brier Score with the Brier Score of a reference forecasts (OFCL, CLIPER etc.). Assess relative accuracy (skill) of a probabilistic forecast with respect to a reference forecast.

60th IHC22 March Mobile, AL9 Statistical methods: Discrimination Distance Distance between the mean forecast probability (F) for all event (E) and all non-events (E’). Measures the ability of a forecast scheme to discriminate events.

60th IHC22 March Mobile, AL10 Statistical Methods: Conditional Distributions Observed frequency of events Bins of Forecast Probabilities

60th IHC22 March Mobile, AL11 Statistical Methods: Relative Operating Characteristics Forecasts (contingent on the forecast probability) ObservationWarning (W)No Warning (W ’ )Total Event (E)hmE Nonevent (E ’ )fcE’E’ Totalww’w’ N A series of 2x2 contingency tables, which are conditional on a range of forecast probabilities are constructed. For instance a warning would be issued if the probability exceeded 1,2,3,4,…100 % The results of the contingency tables can be quantified in terms of hit rate (hr) = h/(h+m) and false-alarm rate (far)=f/(f+c) A plot of far vs. hr can be created and a skill score created from the area under the curve.

60th IHC22 March Mobile, AL12 Statistical Methods: ROC diagram & Skill Score,where A is the area under the curve Mason and Graham (1999) skill No skill ROC Skill Score

60th IHC22 March Mobile, AL13 Verification Procedure: Test Dataset Dataset –5-day cumulative 64-kt wind probabilities were generated for 342 coastal break points (195 official breakpoints additional points) –These were analyzed when warnings were issued for the 14 storms to the right –N= points Storm NameYear Alex2004 Charley2004 Frances2004 Gaston2004 Ivan2004 Jeanne2004 Arlene2005 Cindy2005 Dennis2005 Emily2005 Katrina2005 Ophelia2005 Rita2005 Wilma2005

60th IHC22 March Mobile, AL14

60th IHC22 March Mobile, AL15 Verification Procedure: Summary Skill Measures Bias = (under forecasts) BS = BS OFCL = BS zero = ROC Relative Operating Characteristics 5-d Cummulative Probabilities for Landfalling Atlantic TC BSS OFCL = 28.30% BSS zero = 36.75%

60th IHC22 March Mobile, AL16 Verification Procedure: Conditional Distribution of Break Point Probabilities Slight under forecast of probabilities for this dataset – due to Wilma

60th IHC22 March Mobile, AL17 Verification Procedure: Summary The probabilities are skillful –Brier Skill Score 28% more accurate than the OFCL deterministic forecast Note 50% of the OFCL forecasts verified –ROC Skill Score 88% –The discrimination distance d=26% is large –Probabilities slightly under forecast and are well calibrated for this limited dataset

60th IHC22 March Mobile, AL18 Can the wind speed probabilities be used do decrease the area warned or increase lead time?

60th IHC22 March Mobile, AL19 Probability Model for NHC Hurricane Warnings NHC storm total hurricane warning lengths NHC storm average hurricane warning lead times Since 2000 warning areas have decreased and lead times increased.

60th IHC22 March Mobile, AL20 Warned Break Points (Warnings in Place) Unwarned Break Points (Warnings in Place) Discrimination Distance N=5025N=123225

60th IHC22 March Mobile, AL21 ? Why are there so many low probabilities at the break points?

60th IHC22 March Mobile, AL22 Distribution of Probabilities at the Ending Break Points ?

Example: Hurricane Rita Warnings are brought down too slowly in this case. 00UTC 24 Sep. 12 UTC 23 Sep. 00 UTC 23 Sep. 12 UTC 22 Sep t=0 at landfall

60th IHC22 March Mobile, AL24 Summary of Warning Break Points Probabilities are useful in the watch/warning process. –Objectively assign of the warnings at fixed lead time? –Average at warnings = 28% –Average at end points of the Warnings = 9% It appears that warnings can be dropped sooner, thus decreasing the area warned area.

60th IHC22 March Mobile, AL25 Future Plans Seasonal Verification Code See what we can learn from the verification. Report to this audience Questions?

60th IHC22 March Mobile, AL26 Deterministic Forecast Probabilities Generates wind radii based on OFCL initial wind radii, lat, lon, and intensity using wind radii cliper (DRCL) Interpolate forecast lat, lon, intensity and wind radii every 2-hours Adjust intensity and wind radii for time for landfall. –Hold intensity and wind radii constant from last valid point until landfall, then interpolate to the next valid point –Check wind radii against the new intensities.