Transcript Slide 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 Outline • Who/Why • WFO Goals • Activities to Date: – Training Initiatives – Visualization Software Who in NOAA • WFO’s – – – – New York Mt Holly State College Pittsburgh • NCEP – EMC – HPC – OPC • NOAA Earth System Research Laboratory Motivation • Prediction of mesoscale phenomenon within extratropical storms remains a major challenge. Goal for the WFO • Improvement in operational forecaster understanding of uncertainty/predictability. • Improve communication of uncertainties to users/customers. 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 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 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. 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. Current CSTAR Training Initiatives • Wave Packets • Targeted Observations • ALPS Wave Packets Target Observations 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 ALPS New Projections Statistical Functionality A Brief Example • The following are all 168 HR (7 Day) Forecasts from last Thursday • Valid at 8 AM this Morning – Thursday, Nov 4th GEFS Members + ECMWF ECMWF GEFS Mean GEFS Mean + ECMWF = MEAN Example Statistics - 850 Temperatures 850 Temperatures - cont How do I get ALPS ? • Visit the SBU CSTAR Page: • http://dendrite.somas.stonybrook.edu/CSTAR /cstar.html ALPS GFE - Future • Deployment of Probabilistic Products • Aviation Specific Examples – Wind Speed – Wind Direction – Gusts (probability of being reported) • No yet Loaded at OKX Example Probabilistic Products Example Probabilistic Products BUFKIT 10 • SREF (21 Members) • WDTB WRF Ensemble – Resolution: 24 KM – Frequency: 00Z and 12Z 3 – Members: 8 ensemble members (2 ) x 2 Initializations • NMM/ARW • NAM/GFS • KF/BMJ Boundary Layer Winds - Aviation Questions? • CSTAR E-Mail List – Send Jeff Waldstreicher an e-mail • CSTAR WEB PAGE: – http://dendrite.somas.stonybrook.edu/CSTAR/cstar.html