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Science and Technology Infusion Plan for Winter Weather Services. Sam Contorno. NWS S&T Committee September 17, 2002. Outline. Team Composition Vision / Benefits Goals / Targets Key Information Gaps Key Solutions Outstanding R & D Needs Summary. Sam Contorno – NWS/OST

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Science and Technology Infusion Plan


Winter Weather Services

Sam Contorno

NWS S&T Committee

September 17, 2002

  • Team Composition
  • Vision / Benefits
  • Goals / Targets
  • Key Information Gaps
  • Key Solutions
  • Outstanding R & D Needs
  • Summary
winter weather services team composition
Sam Contorno – NWS/OST

Mark Tew – NWS/OCCWS

Marty Ralph – OAR/ETL

Jack Dostalek – NESDIS/CIRA

John Cortinas – OAR/NSSL/CIMMS

Andy Edman – NWS/WR SSD

Paul Sisson – NWS/WFO BTV

Jeff Waldstreicher – NWS/ER

Peter Manousos – NWS/NCEP/HPC

Allen White – OAR/ETL

Paul Neiman – OAR/ETL

Paul Stokols – NWS/OCCWS

Winter Weather ServicesTeam Composition
winter weather services vision benefits
Winter Weather ServicesVision / Benefits
  • Vision
  • Winter Storm Warning Lead Time > 24 Hrs
  • Probabilistic Guidance Out to 10 Days
  • Large Winter Storms Result in Losses of $Billions and Influences Regional/National Economy.
  • Better Information = Better Decisions by Public Prior to Arrival of
  • Inclement Wx
  • $ Millions in Savings for Public Works & Transportation Sector
winter weather key information gaps
Winter WeatherKey Information Gaps
  • More Accurate Cyclone Track: More Precise Position of Precipitation Cutoff and Rain/Snow Line/Snow Level
  • Limited Coastal and Offshore Observations Aloft
  • Understanding of Model Guidance Uncertainty
  • More Accurate Identification of Precipitation Type, Cloud Microphysical Info, and Vertical Distribution of Moisture
  • More Accurate Identification of Mesoscale Snow Bands and Orographic Effects
winter weather key s t solutions current programmatic phase












Winter Weather Key S&T SolutionsCurrent Programmatic Phase

Targeted Obs.

Dual Polarization




Satellite Upgrades


8km WRF*

SR Ensembles

Advanced Ensembles



Weather-Climate Connection


Meso Snowband Technique




Snow Level Detection Algorithm


Precip Type Algorithms*

Weather Event Simulator


Winter Weather & NWP PDS

winter weather outstanding r d needs
Winter WeatherOutstanding R&D Needs
  • Develop Sensors that Accurately Detect Different Precipitation Types/Measure Snow Depth
  • Develop Ensemble Techniques for Probabilistic Forecasts of Severity of Large-Scale Winter Storms
  • Improve Understanding of Mesoscale and Microphysical Processes that Lead to Heavy Winter Precipitation Events
  • Improve Understanding of Orographic Effects on Mesoscale Distribution of Winter Precipitation
  • Develop Artificial Intelligence Systems to Forecast Snowbands/Heavy Snowfall/Significant Icing
  • Develop Improved Collaborative Forecast Tools
  • Develop Improved Verification Procedures
winter weather summary
Winter WeatherSummary


Lead Times > 24 Hrs; Prob. Guidance 10 Days

  • R&D Needs
  • Precipitation Type Sensors
  • Probabilistic Forecast Techniques
  • Artificial Intelligence to Detect Heavy Snowbands
  • Understanding of Mesoscale and Microphysical Processes

Increasing Performance

  • Deploy High Resolution, More Accurate Models
  • Enhance Decision Assistance Tools
  • Expand Targeted Observations
  • Deploy Advanced Ensemble Techniques
  • Deploy Dual Polarization/Satellite Upgrades

Continuous Training




primary customers partners
Primary Customers/Partners
  • General Public
  • Emergency Managers
  • Federal/State/Local Governments
  • Transportation Industries
  • International Interests
  • Educational Organizations/Institutions of Higher Learning
  • Media
  • Private Sector Weather Forecasters
key products services
Key Products/Services
  • Winter Weather Watches/Warnings
  • Zone Forecasts
  • Winter Weather Advisories/Outlooks
  • Public Information Statements
  • Heavy Snow/Icing Discussions
  • Probabilistic Heavy Snow/Icing Forecasts
  • Short-term (1-6h) Winter Weather Discussion
profiler snow level detection snow advisory raised to winter storm warning
Profiler Snow-Level DetectionSnow Advisory raised to Winter Storm Warning

Prototype profiler snow-level product showed 2700 ft snow level at the coast, 1300 ft lower than the snow level that had been predicted before landfall.

NWS’ Portland OR SOO (Bill Schneider) upgraded earlier Snow Advisory to a Winter Storm Warning based on this lower snow level and passes being at 4000 ft.

Forecaster’s use of these data increased warning lead time by 15 h in this case and increased POD by avoiding a miss.


snow level