1. Predictability of High Impact Weather during the Cool Season over the Eastern U.S: CSTAR Operational Aspects Matthew Sardi and Jeffrey Tongue NOAA/NWS, New York, NY 4 November 2010

2. Outline Who/Why WFO Goals Activities to Date: – Training Initiatives – Visualization Software

3. Who in NOAA WFO’s – New York – Mt Holly – State College – Pittsburgh NCEP – EMC – HPC – OPC NOAA Earth System Research Laboratory

4. Motivation Prediction of mesoscale phenomenon within extratropical storms remains a major challenge.

5. Goal for the WFO Improvement in operational forecaster understanding of uncertainty/predictability. Improve communication of uncertainties to users/customers.

6. Specifics Upton, NY (KOKX): – Improved understanding of cyclone evolution and precipitation bands – Ensemble Forecast Systems (EFS) application to Aviation Low-level winds Precipitation type Snowfall rate – Mentors to the SBU students 1 SCEP 1 STEP 4 Volunteers

7. Specifics (cont) WFO Philadelphia, PA (KPHI): – Storm surge – Coastal flooding State College, PA (KCTP): – Visualization Software – Training – Data management WFO Pittsburgh, PA (KPIT): – Training – Visualization – Graphical Forecast Editor (GFE) Applications

8. Specifics (cont) Hydrometeorological Prediction Center (HPC): – Precipitation banding. – Cyclone track verification for the winter weather desk, medium range forecast products, as well as the snowfall and QPF products. – HPC will host visiting forecasters, scientists, and project students. Ocean Prediction Center (OPC): – EFS application to cyclone track and intensity. – East Coast Marine impacts - high winds and waves.

9. Specifics (cont) Environmental Modeling Center (EMC): – EFS sensitivities related to the Weather Storms Reconnaissance Program – Impacts of wave packets – Training of forecasters: Impact of targeted observations SREF system – Cyclone verification Environmental System Research Laboratory (ESRL): – EFS sensitivities related to the Weather Reconnaissance Program – Training on the impact of targeted observations on predictability.

10. Current CSTAR Training Initiatives Wave Packets Targeted Observations ALPS

11. Wave Packets

12. Target Observations

14. Advanced Linux Prototype System (ALPS) Running on a “non-baseline” AWIPS Workstation. Looks and Feels like D2D Designed for probabilistic forecasting Visualizing Ensemble Data – Weighting Ensemble Members – Generating Probabilistic Grids – Etc

15. ALPS

16. New Projections

17. Statistical Functionality

18. A Brief Example The following are all 168 HR (7 Day) Forecasts from last Thursday Valid at 8 AM this Morning – Thursday, Nov 4th

19. GEFS Members + ECMWF

20. ECMWF

21. GEFS Mean

22. GEFS Mean + ECMWF = MEAN

23. Example Statistics - 850 Temperatures

24. 850 Temperatures - cont

25. How do I get ALPS ? Visit the SBU CSTAR Page: http://dendrite.somas.stonybrook.edu/CSTAR /cstar.html

26. ALPS GFE - Future Deployment of Probabilistic Products Aviation Specific Examples – Wind Speed – Wind Direction – Gusts (probability of being reported) No yet Loaded at OKX

27. Example Probabilistic Products

29. BUFKIT 10 SREF (21 Members) WDTB WRF Ensemble – Resolution: 24 KM – Frequency: 00Z and 12Z – Members: 8 ensemble members (2 3 ) x 2 Initializations NMM/ARW NAM/GFS KF/BMJ

31. Boundary Layer Winds - Aviation

32. Questions? CSTAR E-Mail List – Send Jeff Waldstreicher an e-mail CSTAR WEB PAGE: – http://dendrite.somas.stonybrook.edu/CSTAR/cstar.html