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The Short-Range Ensemble Forecast: SREF

The Short-Range Ensemble Forecast: SREF Applying Uncertainty and Probabilistic Forecasts of Winter Storms Matt Steinbugl, NOAA/NWS Des Moines (formerly) Rich Grumm, NOAA/NWS State College. Short-Range Ensemble Forecast Objectives. Convey and apply uncertainty to the forecast process

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The Short-Range Ensemble Forecast: SREF

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  1. The Short-Range Ensemble Forecast: SREF Applying Uncertainty and Probabilistic Forecasts of Winter Storms Matt Steinbugl, NOAA/NWS Des Moines (formerly) 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 confidence in a 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 Risk of heavy rain Prob of 4” snow

  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 Error Combo GFS/NAM SREF Mean

  9. Case Study Data • Examine 2 significant winter weather events across the Eastern United States • Determine the following: • -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 http://eyewall.met.psu.edu/ensembles/java/ModelDisplay.html Mean and probability 2m 850mb

  12. Mixed/Conditional Probability charts PYTPE Rain Snow http://eyewall.met.psu.edu/ensembles/java/ModelDisplay.html Ice Pellets FZRA

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

  14. So, what happened ??? • Our guests can look at the handouts • Please don’t share with NWS folks…. • This case is part of a training scenario- • yet to be completed !

  15. Case Study #2 23-25 April 2005 Detroit, Michigan

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

  17. Probability/Mean 0.40” QPF over 24 hr Starting 9 hours later Starting 21Z

  18. Detroit, MI Plume Diagram You can get these for DSM, ALO, DBQ and BRL http://eyewall.met.psu.edu/plumes/PlumeDisplay.html

  19. NOHRSC

  20. Summary • Ensemble Prediction Systems are an important means of: • 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. http://eyewall.met.psu.edu/ensembles/java/ModelDisplay.html • Spaghetti charts, model variance and normalized anomaly http://eyewall.met.psu.edu/plumes/PlumeDisplay.html • Plume charts for DSM, ALO, DBQ, BRL http://wwwt.emc.ncep.noaa.gov/mmb/SREF/FCST/COM_US/web_js/html/mean_surface_prs.html • NCEP Environmental Modeling Center SREF page

  22. Questions ???

  23. 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

  24. 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

  25. MRCC

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