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Current Products and Future Plans for the Expanded UW Short-Range Ensemble Forecast System

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. Mean & Std. Dev. for sea-level pressure at F24. 4 mb. 5 mb.

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Current Products and Future Plans for the Expanded UW Short-Range Ensemble Forecast System

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  1. 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

  2. Mean & Std. Dev. for sea-level pressure at F24 4 mb 5 mb

  3. Probability of surface wind speed > 21 kt (SCA)

  4. Probability of 12h accum. precip. > 0.01” (rain/no-rain) PSCZ

  5. 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)

  6. Evaluate the expanded UW MM5 SREF system • Compare skill scores, spread-error correlations, • verification rank histograms, ROC curves, and error • variance diagrams using these ensemble systems as • benchmarks: • Old UW MM5 SREF systems (2000-01; 5-member) • “Poor Man’s” ensemble (PEPS; 7-member) • NCEP SREF system (Eta-RSM; 10-member) • Multimodel applications • Combine UW MM5 ensemble with: • PEPS • NCEP SREF system • Stochastic OR model/field parameter perturbations • Ideas not fully developed; try this next year

  7. Spread-Error Scatter Diagrams HIGH & LOW SPREAD CASES ALL CASES

  8. Spread-Error Correlation Theory (Houtekamer 1993; Whitaker and Loughe 1998) 2 1-exp(-2) 2(s,E) = ; =std(ln s) 2   1- exp(-2) Developing a Prediction System for Forecast Skill • Are spread and skill well correlated for other parameters? • ie. – wind speed & precipitation • Use sqrt to transform data to be normally distributed. • Do spread-error correlations improve after bias removal? • How do the correlations compare to the 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)

  9. 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

  10. 00 UTC T - 48 h CENT- MM5 12 UTC T - 36 h CENT- MM5 00 UTC T - 24 h CENT- MM5 12 UTC T - 12 h CENT- MM5 00 UTC T CENT- MM5 Does not have mesoscale features * “spun-up” CENT-MM5 analysis F48 F36 F24 F12 F00* M = 4 verification Temporal Short-range Ensemble with the centroid • BENEFITS: • Yields mesoscale spread • Less sensitive to one synoptic-scale model’s time variability • Best forecast estimate of “truth”

  11. NEW verification methods/scores? • gradient-magnitude • pattern recognition • event-based scoring CENT-MM5 “adjusted” OUTPUT OBSERVATIONS Noise TRUE VALUES Bias parameters Measurement error Small-scale structure Large-scale structure (after Fuentes and Raftery 2001) Mesoscale Verification • Will 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 forecasts • Useful for creating verification rank histograms • Bayesian combination of “adjusted” centroid with • observations (e.g.- Fuentes and Raftery 2001) • Accounts for scale differences (change of support problem) • Can correct for MM5 biases

  12. Probability Forecasts • Expanded UW SREF probability of precip forecasts • Compare 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 Deterministic-Style Solutions • Centroid • Compare to mean & members using both verification approaches • Bayesian Model Averaging (BMA) • i.e.- Weighted mean

  13. Innovative Forecast Products/Tools GOAL: VISUALIZING FORECAST UNCERTAINTY WITHOUT NEEDING A TON OF PRODUCTS • Work with NWS-Seattle, Whidbey NAS forecasters • (specialized products for warning criteria) • Work with MURI visualization team at UW APL • (ways to visualize uncertainty)

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