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Enhancing Winter Storm Forecasts with Short-Range Ensemble Predictions

This presentation explores the implementation of Short-Range Ensemble Forecasts (SREF) to improve winter storm predictions. The SREF approach utilizes multiple model members with varying dynamics to account for chaos and uncertainty inherent in weather forecasting. By assigning probabilities to critical winter weather parameters, this method enhances forecaster confidence and user decision-making capabilities. The session includes case studies of significant winter events, highlighting the effectiveness of probabilistic forecasts in managing uncertainty and enhancing warning processes.

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Enhancing Winter Storm Forecasts with Short-Range Ensemble Predictions

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  1. The Short-Range Ensemble Forecast: Applying Uncertainty and Probabilistic Forecasts of Winter Storms Matt Steinbugl, NOAA/NWS Des Moines Rich Grumm, NOAA/NWS State College

  2. Short-Range Ensemble Forecast Objectives • Convey and apply uncertainty to the forecast process • Recognize and assign probabilities to crucial winter weather forecast parameters • This will allow forecasters: • To increase overall confidence within each individual forecast through a probabilistic approach • To make better decisions while allowing users better decision making capabilities

  3. Why Ensembles – Uncertainty/Chaos

  4. Why Ensembles? • Uncertainty in initial conditions and model calculations can alone lead significant outcome changes (run-to-run) • Need to account for non-linear processes • Atmosphere is chaotic in nature

  5. Why Ensembles? • Needed to deal with inherent forecast uncertainty • Improve significant winter weather forecasts • Recognize high uncertainty/high probability outcomes and relate these to each phase of the forecast process

  6. What is the SREF? Multi-model based ensemble prediction system (EPS) with each member having different dynamical cores and physics packages. 21 individual members: 5 ETA (BMJ) + 5 ETA (KF) + 5 RSM + 6 WRF NMM/ARW (BMJ/KF) = 21 members -3 hourly output out to 87hrs -Produced at NCEP 03Z, 09Z, 15Z and 21Z

  7. Deterministic (GFS) vs. Probabilistic (SREF) Comparing deterministic models is a 50/50 proposition!!!

  8. SREF Performance

  9. Case Study Data • Examine 3 significant winter weather events across the Eastern United States • We need to extract the following from the data: • -Amounts/timing of pcpn? • -PYTPE? • -Temps for Snow vs. Ice? • -Pattern Recognition? • -Atypical/typical event?

  10. Case Study #1 22-23 Dec 2004

  11. Spaghetti/Probability charts - 0° isotherm spread Mean and probability 2m 850mb

  12. Mixed/Conditional Probability charts PYTPE Rain Snow Ice Pellets FZRA

  13. Probability/Mean charts – 0.50/1.00” QPF

  14. MRCC

  15. Case Study #2 23-25 April 2005

  16. Mixed/Conditional Probability charts PYTPE Rain Snow Ice Pellets FZRA

  17. Probability/Mean 0.40” QPF

  18. Detroit, MI Plume Diagram

  19. NOHRSC

  20. Summary • EPSs are an important means of: • Explicitly conveying and applying uncertainty through a probabilistic approach • Visualizing and quantifying uncertainty within the forecast process • Using ensembles will allow forecasters to relate probabilities to each phase of the warning decision process • In turn, this will allow forecasters to make better decisions and users to have better decision making capabilities

  21. SpecialThanks • Rich Grumm, SOO CTP • Karl Jungbluth, SOO DMX • Peter Manousos, SOO NCEP • Jun Du, NCEP/EMC • Steve Wiess, SPC • Jeremy Grams, SPC • David Bright, SPC

  22. Questions ???

  23. References • http://www.hpc.ncep.noaa.gov/ensembletraining/ • http://wwwt.emc.ncep.noaa.gov/mmb/SREF/WMO06_full.pdf • http://wwwt.emc.ncep.noaa.gov/mmb/SREF-Docs/ • AWOC Winter IC 6.3: Using Ensembles in Winter Weather Forecasting • http://mcc.sws.uiuc.edu • http://nws.met.psu.edu/severe/index.jsp • http://nws.met.psu.edu/severe/2006/11May2006.pdf • SREF Exploitation at NCEP’s Hydrometeorological Prediction Center (HPC) http://nws.met.psu.edu/severe/2005/23April2005.pdf • Dealing with uncertainties in forecasts – M Steven Tracton NWS/NCEP/EMC • http://weather.unisys.com/archive/index.html • http://eyewall.met.psu.edu/plumes/Plume.pdf • http://eyewall.met.psu.edu/plumes/PlumeDisplay.html • http://eyewall.met.psu.edu/ensembles/java/ModelDisplay.html

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