Predictability of High Impact Weather during the Cool Season over the Eastern U.S:

CSTAR Operational Aspects

Matthew Sardi and Jeffrey TongueNOAA/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– Members: 8 ensemble members (23) 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