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Mr. Evan Kuchera HQ AFWA 2 WXG/WEA Template: 28 Feb 06

AFWA Ensemble Prediction System. Mr. Evan Kuchera HQ AFWA 2 WXG/WEA Template: 28 Feb 06. Approved for Public Release - Distribution Unlimited. Overview. Purpose: To discuss progress towards an AFWA ensemble prediction system

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Mr. Evan Kuchera HQ AFWA 2 WXG/WEA Template: 28 Feb 06

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  1. AFWA Ensemble Prediction System Mr. Evan Kuchera HQ AFWA 2 WXG/WEA Template: 28 Feb 06 Approved for Public Release - Distribution Unlimited

  2. Overview • Purpose: To discuss progress towards an AFWA ensemble prediction system • AFWA is exploring how ensembles can best be exploited to improve DoD forecast processes and warfighter decision making • Diverse global and mesoscale models • Probabilistic algorithms for “high impact” variables • Concise, warfighter-focused products • Emphasis on training and outreach

  3. Webpage https://weather.afwa.af.mil/host_home/DNXM/JEFS/jefs.html Available products for global (JGE) and mesoscale (JME): Precipitation Amount Precipitation Type Snow Amount Cloud Cover Lightning Dust Lofting Severe Weather Blizzard Surface Wind Gust Visibility Wind Chill Heat Index Realtime verification also available on webpage

  4. Mesoscale Ensemble • Pre-processing • GFS ensemble from six hours earlier is used for initial/lateral boundary conditions • Model configuration • 10 independent model configurations with varying physics and lower boundary conditions (land surface, SSTs) run twice a day • Also producing 31-member merged JME-SREF over CONUS • The table lists different physics packages used by each member

  5. Current Mesoscale Domains 30km 30 km 10 km East Asia SWA 10 km 3 km 3 km resolution reserved for complex terrain areas 30/10 resolutions best for available resources Hourly output on the 10 km domains to 48 hours CONUS

  6. 8-day storm forecast

  7. Calibration Algorithms • As opposed to “traditional” calibration… • Observations/gridded analysis for real-time calibration are not always available • Real-time calibration can reduce skill in forecasting rare events and pattern changes • Use regression with past model-obs data sets and physics-based algorithms to produce a climatologically calibrated forecast • Accounts for diagnosis uncertainty for unresolved variables • Does not necessarily account for model biases • Lightning, visibility, and precipitation type created so far

  8. 30 hour ice storm forecast SNOW PROBABILITY “MIX” PROBABILITY FRZR PROBABILITY

  9. 21 hr lightning forecast

  10. Dust Lofting—30 hr fcst

  11. Precipitation Meteogram Observations in circles

  12. Surface Wind Meteogram

  13. Objective Verification

  14. Way Ahead • Global (2009) • Begin work to operationalize products that are ready • Mesoscale (2009-2011) • Further work needed to generate more representative mesoscale initial condition/model perturbations—crucial to success • Continue making products in development environment • Statistical post-processing work also important • Forecast variables of interest with algorithms; mitigate biases • Large effort required—reserved for highest impact variables • Training and outreach • Outreach to decision makers; work hand-in-hand with forecasters for maximum exploitation capability • Formalize training on certainty forecasting principles

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