1. National Weather Service 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. National Weather Service Short-Range Ensemble Forecast Objectives Convey and apply uncertainty to the forecast process Convey and apply uncertainty to the forecast process Recognize and assign probabilities to crucial winter weather forecast parameters 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 increase overall confidence within each individual forecast through a probabilistic approach To make better decisions while allowing users better decision making capabilities To make better decisions while allowing users better decision making capabilities

3. National Weather Service Why Ensembles – Uncertainty/Chaos

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

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

6. National Weather Service

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. National Weather Service Deterministic (GFS) vs. Probabilistic (SREF) Model Initial Conditions (ICs) Model cores Remarks GFS 1 IC 1 model core run-to-run (jumpiness) SREF Multiple ICs Multiple cores More consistency Comparing deterministic models is a 50/50 proposition!!!

9. National Weather Service SREF Performance

10. National Weather Service Case Study Data Examine 3 significant winter weather events across the Eastern United StatesExamine 3 significant winter weather events across the Eastern United States We need to extract the following from the data: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. National Weather Service Case Study #1 22-23 Dec 2004

12. National Weather Service Spaghetti / Probability charts - 0° isotherm Spaghetti / Probability charts - 0° isotherm Mean and probability spread 2m850mb

13. National Weather Service Mixed/Conditional Probability charts PYTPE Mixed/Conditional Probability charts PYTPERain Ice Pellets Snow FZRA

14. National Weather Service Probability/Mean charts – 0.50/1.00 QPF

15. National Weather Service MRCC

16. Case Study #2 23-25 April 2005

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

18. National Weather Service Probability/Mean 0.40 QPF

19. National Weather Service Detroit, MI Plume Diagram

20. National Weather ServiceNOHRSC

21. Summary EPSs are an important means of: EPSs are an important means of: Explicitly conveying and applying uncertainty through a probabilistic approachExplicitly conveying and applying uncertainty through a probabilistic approach Visualizing and quantifying uncertainty within the forecast processVisualizing 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. National Weather Service Special Thanks 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. National Weather Service Questions ???

24. National Weather Service 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 NCEPs Hydrometeorological Prediction Center (HPC) http://nws.met.psu.edu/severe/2005/23April2005.pdf 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