Current Products and Future Plans for the Expanded UW Short-Range Ensemble Forecast System
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Transcript of Current Products and Future Plans for the Expanded UW Short-Range Ensemble Forecast System
Eric P. Grimit
Department of Atmospheric Sciences, University of Washington
Seattle, Washington
Current Products and Future Plans for the Expanded UW Short-Range Ensemble Forecast System
Mean & Std. Dev. for sea-level pressure at F24
5 mb4 mb
Probability of surface wind speed > 21 kt (SCA)
Probability of 12h accum. precip. > 0.01” (rain/no-rain)
PSCZ
Future Research Plans
Evaluate the expanded UW MM5 SREF system and investigate multimodel applications
Develop a mesoscale forecast skill prediction system
Additional Work mesoscale verification probability forecasts deterministic-style solutions additional forecast products/tools (visualization)
•Evaluate the expanded UW MM5 SREF systemCompare skill scores, spread-error correlations,verification rank histograms, ROC curves, and errorvariance diagrams using these ensemble systems asbenchmarks:
•Old UW MM5 SREF systems (2000-01; 5-member)•“Poor Man’s” ensemble (PEPS; 7-member)•NCEP SREF system (Eta-RSM; 10-member)
•Multimodel applicationsCombine UW MM5 ensemble with:
•PEPS•NCEP SREF system
•Stochastic OR model/field parameter perturbationsIdeas not fully developed; try this next year
Spread-Error Scatter DiagramsALL CASES HIGH & LOW SPREAD CASES
2(,E) = ; =std(ln )2 1-exp(-2)
1- exp(-2)2
(Houtekamer 1993; Whitaker and Loughe 1998)Spread-Error Correlation Theory
Spread-error correlation depends on the time variation of spread
For constant spread (=0) = 0. Spread is the most useful predictor of
skill when it is extreme (large or small)
•Are spread and skill well correlated for other parameters?ie. – wind speed & precipitationUse sqrt to transform data to be normally distributed.
•Do spread-error correlations improve after bias removal?
•How do the correlations compare to the theory?
Developing a Prediction System for Forecast Skill
•What is “high” and “low” spread?need a spread climatology, i.e.- large data set
•What are the synoptic patterns associated with “high” and “low” spread cases?
Use NCEP/NCAR reanalysis data and compositing software
•How do the answers change for the expanded UW MM5 ensemble?
•Is forecast skill correlated with the spread of a temporal ensemble?
Temporal ensemble = lagged forecasts all verifying at the same time
Spread of a temporal ensemble ~ forecast consistency
Temporal Short-range Ensemble
F36 F24 F12F48
with the centroid
BENEFITS:•Yields mesoscale spread
•Less sensitive to one synoptic-scale model’s time variability
•Best forecast estimate of “truth”
CENT-CENT-MM5MM5
CENT-CENT-MM5MM5
CENT-CENT-MM5MM5
CENT-CENT-MM5MM5
CENT-CENT-MM5MM5
CENT-CENT-MM5MM5
CENT-CENT-MM5MM5
CENT-CENT-MM5MM5
CENT-CENT-MM5MM5
CENT-CENT-MM5MM5
00 UTCT - 48 h
12 UTCT - 36 h
00 UTCT - 24 h
12 UTCT - 12 h
00 UTCT
F00* Does not have mesoscale features* “spun-up” CENT-MM5 analysisM = 4
verification
Mesoscale VerificationWill verify 2 ways:•At the observation locations (as before)•Using a gridded mesoscale analysis
SIMPLE possibilities for the gridded dataset:
•“adjusted” centroid analysis (run MM5 for < 1 h)Verification has the same scales as the forecastsUseful for creating verification rank histograms
•Bayesian combination of “adjusted” centroid withobservations (e.g.- Fuentes and Raftery 2001)Accounts for scale differences (change of support problem)Can correct for MM5 biases
TRUEVALUES
OBSERVATIONSCENT-MM5“adjusted”
OUTPUT
Bias parameters
Noise
Measurement error
Large-scale structure Small-scale structure (after Fuentes and Raftery 2001)
NEW verification methods/scores?
•gradient-magnitude•pattern recognition•event-based scoring
•Expanded UW SREF probability of precip forecastsCompare to:
•Sample climatology•NGM MOS•NCEP SREF•Old ensemble
•Calibrate using weighted ranks(Eckel and Walters 1998)
•Calibrate using Bayesian Model Averaging (BMA) weights(Hoeting et al. 1999)
•Look at probability forecasts for other parameters
Probability Forecasts
Deterministic-Style Solutions•Centroid
Compare to mean & members using both verification approaches
•Bayesian Model Averaging (BMA)i.e.- Weighted mean
Probability of Warning Criteria at McGuire AFB Bas e d o n 1 5 /0 6 Z MM5 En s e m b le
0
10
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Date/T ime
Pro
ba
bili
ty (
%)
T S torm
W inds> 35k t
W inds> 50k t
S now> .5"/hr
Fzg Rain
15/06 12 18 16/00 06 12 18 17/00 06
Innovative Forecast Products/Tools
•Work with NWS-Seattle, Whidbey NAS forecasters(specialized products for warning criteria)
•Work with MURI visualization team at UW APL(ways to visualize uncertainty)
GOAL: VISUALIZING FORECAST UNCERTAINTYWITHOUT NEEDING A TON OF PRODUCTS