Fly - Fight - Win 2 d Weather Group Mr. Evan Kuchera HQ AFWA 2 WXG/WEA Template: 28 Feb 06 Approved for Public Release - Distribution Unlimited AFWA Ensemble.

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Presentation transcript:

Fly - Fight - Win 2 d Weather Group Mr. Evan Kuchera HQ AFWA 2 WXG/WEA Template: 28 Feb 06 Approved for Public Release - Distribution Unlimited AFWA Ensemble Prediction System

Fly - Fight - Win 2 Philosophy There are five characteristics of a good forecast: Timely (Available before decision is made) Communicative (information is easy to understand) Focused (directly impacts the decision) Useful (not something already known) Reliable (information is correct) Ensembles are great for addressing the last two bullets, but the first three must be kept in mind

Fly - Fight - Win 3 Overview Purpose: To discuss the AFWA transition from a prototype (JEFS) to an operational (AFWEPS) 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/techniques for “high impact” variables Concise, warfighter-focused products Emphasis on training and outreach

Fly - Fight - Win 4 Mesoscale Ensemble 40 km Northern Hemisphere Pre-processing GFS ensemble from six hours earlier is used for initial/lateral boundary conditions (NOGAPS soon) Model configuration 10 independent model configurations with varying physics and lower boundary conditions (land surface, SSTs) run at 06/18Z to 132 hours The table lists different physics packages used by each member

Fly - Fight - Win 5 Mesoscale Ensemble 12 km CONUS/SWA/EAST ASIA Pre-processing IC/LBC from 40 km NHEMI (also SREF for CONUS domain) Model configuration same as NHEMI Hourly output to 48 hours on 12 km domains

Fly - Fight - Win 6 Mesoscale Ensemble 4 km SWA/CONUS Run as nests of the 12 km domains to 24 hours CONUS is a floater that we move around with the seasons

Fly - Fight - Win 7 Post-Processing High Impact Weather Main focus of post-processing efforts is on probabilistic assessment of “high-impact” weather Specialized applications for dust and cloud forecasting Physics-based probabilistic (i.e. diagnosis uncertainty) algorithms for lightning, visibility, precipitation type, and surface wind gust Deterministic (i.e. no diagnosis uncertainty) algorithms used for snow accumulation, icing, turbulence Other diagnostics (joint probability of instability/shear for severe weather, surface winds/snowfall for blizzard conditions)

Fly - Fight - Win 8 30 hour ice storm forecast “MIX” PROBABILITY FRZR PROBABILITY SNOW PROBABILITY

Fly - Fight - Win 9 Forecast Example: 51 hour icing forecast

Fly - Fight - Win 10 Precipitation Meteogram Observations in circles

Fly - Fight - Win 11 Objective Verification

Fly - Fight - Win 12 Way Ahead Near Term: Establish operational flow of “full” NOGAPS and NCEP SREF ensembles to allow initialization of mesoscale members (~12 km) Explore ETKF on NHEMI (or global) mesoscale domain Explore new techniques for model physics diversity Continue refining ensemble forecast techniques on high-impact forecast variables (clouds, dust, surface winds, precipitation, visibility, icing, turbulence) Continue close relationships with forecasters to further cement ensemble presence “in the field” Explore stronger relationships with “decision makers” who can benefit from stochastic information

Fly - Fight - Win 13 Way Ahead Medium Term: Work to establish agreements on operational data flows of raw ensemble model output from as many centers as possible Go “operational” with basic mesoscale ensemble Hopefully multi-center with Navy and NOAA Develop sophisticated software to allow interrogation of full raw ensemble and user selected decision thresholds Likely “reach-back” capability due to large volumes of ensemble data Implement standardized training programs

Fly - Fight - Win 14 Way Ahead Long Term: Leverage techniques for initial condition generation found most effective (4DVAR/ENKF/Hybrids/etc) Design new model physics to fully represent uncertainties in sub- grid scale processes Entrain advancements in land surface and aerosol modeling Increase membership and resolution as computing resources allow