NHC Ensemble/Probabilistic Guidance Products

Transcription

1 NHC Ensemble/Probabilistic Guidance Products Michael Brennan NOAA/NWS/NCEP/NHC Mark DeMaria NESDIS/STAR HFIP Ensemble Product Development Workshop 21 April 2010 Boulder, CO 1

2 Current Ensemble/Probability Based Forecaster Tools at NHC Genesis Probabilities (Satellite) Goerss Predicted Consensus Error (GPCE) SHIPS Rapid Intensification (RI) Index Wind Speed and Intensity Probabilities Storm Surge Probabilities None of these are actually based on ensemble system output! 2

3 Genesis Probabilities (Satellite) Based on the following input parameters in 5 x 5 squares: Climatological formation probability (1949-present) Percentage of each 5 x 5 square over land Distance from the center of each 5 x 5 square to any existing TCs Maximum climatological SST Magnitude of hpa vertical wind shear 850-hPa circulation Vertical Instability (Θe profile from surface to 200 hpa) Percentage of GOES IR pixels < 40 C Cloud-cleared water vapor brightness temperature (a measure of mid- to upper-level moisture) Operational domains cover Atlantic and East Pacific (divided into 8-sub basins) 3

4 Satellite Genesis Probabilities 2009 East Pacific 850-hPa circulatio n Vertical shear Cold pixel count Instability WV brightness temp 4

5 Satellite Genesis Probabilities 2009 East Pacific Total Genesis Probability Available online at: 5

6 Goerss Predicted Consensus Error (GPCE) Goerss (2007), Mon. Wea. Rev. Estimates track error based on the spread of the members of the TVCN track consensus Currently composed of: GFS, ECMWF, UKMET, NOGAPS, HWRF, GFDL, GFDN Designed so that verifying TC position will be inside the GPCE circle about 70% of the time Objective measure of confidence of the consensus track forecast Available to forecasters in real-time when making the forecast 6

7 SHIPS Rapid Intensification Index Kaplan et al. (2010), Wea. Forecasting Statistical guidance for rapid intensification Current version operational in 2008 Provides probability of 25, 30, and 35 kt intensity increase in the next 24 hours Based on eight predictors: Tropical Cyclone Previous 12-h intensity change Difference between current intensity and MPI Atmospheric 200-hPa divergence hpa vertical wind shear hpa relative humidity Oceanic Upper-ocean heat content Satellite Standard deviation of IR brightness temperature (convective symmetry) Coverage of tops with brightness temperatures < -30 C Available to forecasters in real-time with other SHIPS output 7

8 SHIPS RI Index Rick (2009) ** 2009 E. Pacific RI INDEX EP RICK 10/16/09 12 UTC ** ( 30 KT OR MORE MAX WIND INCREASE IN NEXT 24 HR) 12 HR PERSISTENCE (KT): 20.0 Range:-20.0 to 35.0 Scaled/Wgted Val: 0.7/ MB SHEAR (KT) : 5.4 Range: 15.2 to 1.6 Scaled/Wgted Val: 0.7/ 0.9 D200 (10**7s-1) : 58.0 Range:-10.0 to Scaled/Wgted Val: 0.5/ 0.3 POT = MPI-VMAX (KT) : Range: 46.6 to Scaled/Wgted Val: 0.7/ MB REL HUM (%): 80.4 Range: 64.0 to 88.0 Scaled/Wgted Val: 0.7/ 0.2 % area w/pixels <-30 C: 97.0 Range: 26.0 to Scaled/Wgted Val: 1.0/ 0.5 STD DEV OF IR BR TEMP : 11.5 Range: 35.4 to 2.7 Scaled/Wgted Val: 0.7/ 1.1 Heat content (KJ/cm2) : 56.2 Range: 4.0 to 67.0 Scaled/Wgted Val: 0.8/ 0.4 Prob of RI for 25 kt RI threshold= 84% is 7.3 times the sample mean(11.5%) Prob of RI for 30 kt RI threshold= 74% is 9.6 times the sample mean( 7.7%) Prob of RI for 35 kt RI threshold= 70% is 13.4 times the sample mean( 5.2%) 8

9 Wind Speed and Intensity Probability Products DeMaria et al. (2009), Wea. Forecasting Depict location-specific probabilities of 34-kt, 50 kt, and 64-kt winds Created using 1,000 Monte Carlo realizations created by random sampling of NHC track and intensity forecast errors from the previous 5 years Centered on official NHC forecast Errors are serially correlated Uses climatology and persistence model for wind radii Accounts for inland decay Available to NHC forecasters after forecast is made but prior to release of advisory Steered EMs and other users toward these products in lieu of the cone graphic since they provide information on impacts 9

10 Wind Speed Probabilities Hurricane Bill (2009) 00 UTC 26 Aug 2009 Track and Intensity of 1,000 realizations Hurricane-force wind probabilities 10

11 Wind Speed Probabilities Hurricane Bill (2009) 00 UTC 26 Aug day Cone Graphic Tropical-Storm force wind probabilities Note that the threat for tropical-storm force winds extends well outside the cone from Bermuda to New England 11

12 Intensity Probabilities Probability of cyclone intensity falling into various categories Probability calculated from the same 1,000 realizations used to calculate wind speed probabilities Valid for the cyclone at a particular time not at any particular location 12

13 Intensity Probability Verification Performed for in the Atlantic and East Pacific Forecast probabilities were grouped into 10% bins Sample sizes are still quite small for the high probability bins and for all bins for the major hurricane categories Results for h forecasts for tropical storm category shown 13

14 Observed Frequency 100% Atlantic Tropical Storm Forecasts h 90% 80% 70% 60% 50% 40% 30% 12 hr 24 hr 36 hr 48 hr 20% 10% Number of Forecasts 0% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Forecast Probabilty Bin

15 Observed Frequency 100% East Pacific Tropical Storm Forecasts h 90% 80% 70% 60% 50% 40% 30% 12 hr 24 hr 36 hr 48 hr 20% 10% Number of Forecasts 0% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Forecast Probability Bin

16 Probabilistic Storm Surge Based on an ensemble of about 2,000 SLOSH model runs centered around the official NHC forecast Creates alternate scenarios based on historical NHC forecast errors of Along and across track Intensity Varies radius of maximum wind in parametric wind model 16

17 Probabilistic Storm Surge Run when hurricane watch or warning in effect for the U.S. Output: Probabilities of storm surge exceeding thresholds from 2-25 ft Exceedence height for various probability threshold (e.g., the surge height that has a 10% chance of being exceeded) Available within about 30 minutes of advisory release time Difficult to verify, but preliminary comparisons with deterministic SLOSH runs underway 17

18 Use of Monte Carlo Model as Testbed for Ensemble-Based Products Provides 1000 storm tracks and surface wind fields Includes probabilistic verification Brier Score, Threat Score, bias, reliability Many products derived from output Landfall timing distributions, time of arrival of 34, 50, 64 kt winds, watch/warning guidance, WFO local products Path from statistical to dynamical model ensemble products NHC tracks error distributions modified by ensemble input Tracks can be replaced by dynamical model forecasts Tracks, intensity, structure replaced by dynamical model forecasts 18

19 1000 Realizations from MC Model for Hurricane Gustav 30 Aug 2008 at 12 UTC 19

20 Application: Landfall Timing, Intensity Distributions 20

21 Application: Distribution of time of arrival of 34, 50, 64 kt winds 21

22 Application: Objective Watch/Warning Guidance Goal Develop an objective scheme for issuing tropical storm and hurricane watches/warnings based on MC wind speed probability forecasts Data MC cumulative wind speed probabilities (64-kt and 34-kt) U.S. mainland tropical storm and hurricane watches/warnings from at 340 U.S. breakpoints Methodology Test various wind speed probability thresholds prob > prob(up) > put warning up prob < prob(down) > take warning down Choose thresholds based on model fit (total distance of watch/warning) to NHC Maximize threat score Minimize MAE 22

23 Application: Objective Watch/Warning Guidance hurricane warning hurricane watch tropical storm warning tropical storm watch MC Prob Product Threshold Prob Avg Warning Distance (nmi) Statistics (MCP fit to NHC) forecast wind spd up (%) MCP NHC Hit Rate Threat MAE (nmi) N 36 hr 64 kt hr 64 kt hr 34 kt hr 34 kt Example: Gustav 2008 Hurricane Warnings Blue = NHC only Green = MCP only Red = NHC and MCP Black Dashes = Observed Warning Lengths NHC = 560 nmi, MCP = 452 nmi, Obs = 145 nmi 23

24 Objective Watch/Warning Guidance Continuing Work Impacts of future track and intensity forecasts on watches and warnings Reduce track and intensity errors sampled by the MC model Use objective watch/warning scheme to assess resulting reduction in warning distance/duration 20% error reduction 5% reduction in warning length 50% error reduction 13% reduction in warning length Example (right, Gustav 2008): 20% error reduction 50 nmi (12%) warning reduction (blue) TC conditions of readiness (TC-COR) guidance for DoD installations TCCOR levels correspond to time of onset of 50-kt winds TCCOR 1/2/3/4 correspond to 12/24/48/72 hours Estimated TCCOR probability thresholds from Atlantic analyses Initial validation indicates good skill (B. Sampson) Collection of TC-COR data for various Atlantic and Pacific bases underway, use to refine thresholds 24

25 Application: WFO Local Products Coordinated with P. Santos and D. Sharp on coastal and inland verification Presented by P. Santos at 2010 AMS Conference Used to define thresholds for product generation Threat score the most useful cases 400 forecasts 20 TCs 25

26 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 GPCE version accepted by NHC for (hopefully) 2010 operational implementation 26

27 Impact of GPCE input on MC Model 64 kt Wind Probabilities Hurricane Gustav 30 Aug UTC, Low Model Spread 64 kt h cumulative probability difference field (GPCE-Operational) All GPCE values in Low tercile 27

28 Evaluation of GPCE Version in 2009 Two evaluation metrics: Brier Score, Threat Score Compare operational and GPCE versions AL01-AL11 WP01-WP28 EP01-EP20 28

29 Threat Score Improvements with GPCE version Atlantic West Pacific East Pacific 29

30 Next Step: Use Tracks from HFIP Ensembles Statistically generated tracks HFIP demo model tracks 30

31 Convergence of the MC model Theoretical analysis of MC methods 1 E ~ N Empirical test Run with varying N Compare to run with N=500,000 N=1000 in operational model E max N E avg kt wind probability error as a function of the number of realizations N for Hurricane Ike (2008) case N 31

32 Impact of Ensemble Number Ike Sept 12 UTC N=10 N=100 N=1000 N=