Short-Range Ensemble Forecast Objectives

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

7. 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

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

9. SREF Performance

10. 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?

11. Case Study #1 22-23 Dec 2004

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

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

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

15. MRCC

16. Case Study #2 23-25 April 2005

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

18. Probability/Mean 0.40” QPF

19. Detroit, MI Plume Diagram

20. NOHRSC

21. 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

22. 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

23. Questions ???

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