AFWA Overview NUOPC Workshop Aug 2010

Transcription

1 Air Force Weather Agency Fly - Fight - Win AFWA Overview NUOPC Workshop Aug 2010 John Zapotocny Chief Scientist Approved for Public Release Distribution Unlimited

2 Overview Mission & Organization AFWA Models Clouds (CDFS II), Land Surface (LIS), Regional NWP (WRF), Dust/Aerosol (WRF-chem), Space Weather Modeling System Hardware Configuration Ensemble Modeling Initiatives Joint Ensemble Forecast System (JEFS) Project Summary Current Global & Mesoscale Ensemble Capabilities Warfighter Applications of Ensemble Output Training on Ensemble Modeling NUOPC Stretch Goal Fly - Fight - Win 2

3 AFWA Mission A Global Team for the Global Fight Maximizing America s Power through the Exploitation of Timely, Accurate, and Relevant Weather Information; Anytime, Everywhere Fly - Fight - Win 3

4 AFWA Mission Who We Support Fly - Fight - Win 4

5 Organization Air Force Director of Weather Fly - Fight - Win 5

6 Organization AFWA Fly - Fight - Win 6

7 AFWA Models Cloud Depiction and Forecast System (CDFS) II World-wide Merged Cloud Analysis generated hourly Hourly global & regional cloud forecasts to 5km resolution Land Information System Soil temperature, soil moisture, snow depth, age, liquid content WRF Regional scale NWP (deterministic and ensemble) WRF-Chem Dust, aerosol, constituent forecasts Space Weather Models Fly - Fight - Win

8 Geostationary Data Polar Orbiting Data Cloud Analysis CDFS II DMSP AVHRR JPSS (Future) GFS Upper Atmos. Temp Near Surface Temp/RH/Wind Surface Observations Snow Analysis Resolution: 12 nm Obs: Surface, SSM/I Freq: Daily, 12Z World-Wide Merged Cloud Analysis (WWMCA) Hourly, global, real-time, cloud Surface Temp Analysis Resolution: 12 nm Obs: IR imagery, SSM/I Temp Freq: 3 Hourly Total Cloud and Layer Cloud data supports National Intelligence Community, cloud forecast models, and global soil temperature and moisture analysis. Fly - Fight - Win 8

9 Cloud Forecast CDFS II Forecasts based on statistical paring of WRF & GFS output with CDFS-II WWMCA analysis Global forecasts at 25 km resolution Regional forecasts at 15 & 5 km resolution Products Total fractional cloud coverage Layer coverage (5-layers) layer top height & thickness layer type 3-hr time step 30 to 84 hr forecast length (depends on grid) Fly - Fight - Win 9

10 Inputs Land Surface Modeling LIS Physics Outputs Applications Topography, Soils Land Cover, Vegetation Properties Meteorology Land Surface Models Soil Moisture & Temperature Evaporation Runoff WRF Theater Forecasts Army/AF Tactical Decision Aid Software Crop Forecasts Snow Soil Moisture Temperature Data Assimilation Modules Snowpack Properties NCEP Fly - Fight - Win 10

11 Surface temperature and heat stress forecasts, 20 Jul 2007, 06Z cycle Regional NWP WRF Weather Research and Forecast (WRF) model 10,0000 FT MSL Turbulence forecasts, 20 Jul 2007, 06Z cycle Development agent is NCAR Implemented for classified support Jul 06 Unclassified transition to WRF completed Dec 09 WRF DA system Currently 3DVAR (WRFVAR) Transition to GSI is being worked - ops cutover planned in 2011 Fly - Fight - Win

12 Regional Scale NWP Current Operational WRF Windows Fly - Fight - Win 12

13 Dust/Aerosol Modeling WRF-Chem WRF-chem is a version of WRF that simultaneously simulates the emission, turbulent mixing, transport, transformation, and fate of trace gases and aerosols. The WRF Atmospheric Chemistry Working Group is guiding the development of WRF-chem Courtesy NCAR New/Improved space borne sensors and assimilation techniques are needed to specify initial conditions Planned IOC early 2011 Fly - Fight - Win 13

14 Space Weather Models Sample Derived Products Fly - Fight - Win

15 AFWA Modeling System Current Configuration 9477 GFLOPS Gigabit ACN Gigabit 30 Nodes: Prod5 (1578 Gflops) 12 Nodes: Dev3 (576 Gflops) Private High Performance Network 48 Nodes: Prod7 (2550 Gflops) 16 Nodes: SProd2 (813 Gflops) GTWAPS-S ACN-S 10 Nodes: SProd (434 Gflops) 29 Nodes: DC2 (3,526 Gflops) DREN 1 Node: SGTDev2 (32 Gflops) Fly - Fight - Win 15

16 AFWA Modeling System 2011 Configuration 128 Nodes: Linux HPC (10240 Gflops) GFLOPS Gigabit ACN Gigabit 30 Nodes: Prod5 (1578 Gflops) Est: Mid Nodes: Dev4 (1186 Gflops) Private High Performance Network 16 Nodes: Linux Dev/Test (1280 Gflops) 48 Nodes: Prod7 (2550 Gflops) 32 Nodes: Linux HPC-S (2560 Gflops) GTWAPS-S ACN-S 10 Nodes: SProd (434 Gflops) 64 Nodes: DC3 (5120 Gflops) DREN 1 Node: SGTDev2 (32 Gflops) Fly - Fight - Win 16

17 Ensemble Modeling Initiatives Joint Ensemble Forecast System (JEFS) Collaboration across multiple operational and R&D centers to create an ensemble prototype for military operations Run real-time in development environment, make available to users New ensemble products; evaluate objectively and subjectively Pathfinder for how information could be used to improve military decision making Fly - Fight - Win 17

18 Ensemble Modeling Initiatives Joint Ensemble Forecast System (JEFS) Primary findings (full report available) Multi-center global ensembles are quite robust and accurate Calibration was unsuccessful Mesoscale ensembles valuable but are under-dispersive DoD Information Assurance (IA) made AFWA-FNMOC realtime data sharing very difficult in development environment Involving users in product development is crucial Decision makers generally not ready for probabilistic weather Fly - Fight - Win 18

19 Ensemble Modeling Initiatives Ongoing Work Improve initial condition spread on Mesoscale ensembles and increase diversity Ingest NCEP SREF (done), RUC (late 2010), NOGAPS (late 2010) Downscale to finer resolutions Implement more physics variations in WRF 3.1 (done) Create convective scale ensembles with advanced microphysics and aerosols (WRF-Chem) to better simulate dust and aerosols (ongoing) Focus post-processing efforts on high-impact variables Forecast probabilities of lightning, strong winds, hail, ceiling/visibility, heavy rain/snow/ice, etc Develop algorithms for sub-grid scale phenomena based on physical processes and regression against past observational datasets where available Fly - Fight - Win 19

20 AFWEPS Webpage Available products for global (GEPS) and mesoscale (MEPS): Precipitation Amount Precipitation Type Snowfall Amount Cloud Cover Lightning Hail Dust Lofting Potential Severe Thunderstorms Blizzard Surface Wind Gust Ceiling/Visibility Wind Chill Heat Indices Haboob Threat Realtime verification also available on webpage Fly - Fight - Win 20

21 AFWA Global Ensemble Global Ensemble Prediction Suite (GEPS) combines raw ensemble members from NCEP (GFS), CMC (GEM), and FNMOC (NOGAPS) 58 total members at one degree; 240 hours at six hour intervals Main products are probabilities of high-impact variables like precipitation, wind speed, lightning, etc Also produce wind speed and precipitation meteograms that are popular No bias corrections, but algorithms are used to diagnose sub-grid variables (and associated uncertainty) Plan is to pull all datasets from NOMADS server on dedicated communications line from NCEP to DoD Currently pull just GFS/GEM this way Currently NOMADS access only over open internet AFWA-generated GEPS products sent back to NCEP for wider dissemination? Fly - Fight - Win 21

22 GEPS Example 24 hour snowfall ending Sun 7 Feb 2009 at 00Z 174-hour global GEPS (58-member combination of GFS, NOGAPS, and GEM ensembles) showed remarkably high probabilities for high-impact snow event given 7-day lead (much of DC area well over six inches observed): Fly - Fight - Win 22

23 GEPS Precipitation Meteogram Fly - Fight - Win 23

24 Pre-processing Mesoscale Ensemble (MEPS) 40 km Northern Hemisphere GFS ensemble from six hours earlier is used for initial/lateral boundary conditions (NOGAPS to be included Sept 2010) 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 24

25 Pre-processing Mesoscale Ensemble 12 km CONUS/SWA/EAST ASIA 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 25

26 Run as nests of the 12 km domains to 24 hours Mesoscale Ensemble 4 km SWA/CONUS CONUS is re-locatable cycle to cycle by entering a center lat/lon at a URL (software does the rest) Fly - Fight - Win 26

27 4 km SWA ensemble 12 Apr 2010 Iraq IR satellite loop from 09-18Z 50 knot wind gust probability at 19Z 58 knots observed at 1911Z Black contour=where individual ensemble member forecasted 40 knots sustained Lightning probability loop from 09-18Z 00Z 12 Apr 2010 ensemble run Black contours=where individual ensemble member forecasted intense lightning Fly - Fight - Win 27

28 Warfighter Applications Applying Weather in Operational Risk Management MISSION: Static line training airdrop Surface Wind Thresholds: Red: >13kt Yellow: 10-13kt Green: 0-9kt Current (deterministic) Wx Risk Tolerance: N/A Forecast: 12 kt Decision Input: Yellow Future (probabilistic) Wx Risk Tolerance: Low (10%) Forecast: 12 kt, range: 5-26 kt G (25%), Y(33%), R(42%) Decision Input: Red Decision: GO Observed: 19 kt Decision: NO GO Observed: 19 kt Result: 13 injured personnel Result: training delayed kt Fly - Fight - Win 28

29 Warfighter Applications Remotely Piloted Aircraft (RPA) Operations Target 1130 Hrs: RPA arrives at target after loitering in safe area Initial Assessment Risk Analysis Fcst. Refinement Mission Adjustment 0934 Hrs: RPA departs on search and destroy mission through valley region Hrs: RPA ordered to investigate, if chance of success > 25%. Weather from GIG indicates only 8% chance of target acquisition due to fog at 1055 Hrs Hrs: 4 RPA and weather server iterate to try to find acceptable risk in window of opportunity. Forecast for good visibility is 30% at 1130 Hrs Hrs: RPA requests further details from weather server. High-res. terrain downscaling indicates 17% of good visibility Hrs: Intel reports high-priority, fleeting target of opportunity in a mountain pass. RPA could reach target by 1055 Hrs. Mountain areas are fortified by enemy. Fly - Fight - Win Risk still too high. 29 3

30 AFW Training on Ensemble Modeling (CAC/Password required) AFW Training for Ensemble Forecasting: Intro to Ensemble Forecasting Statistics for Ensemble Forecasting Introduction to Stochastic Weather Prediction Ensemble Verification Techniques Use and Misuse of Ensemble Stamp Charts (CAC/Password required) The 16 th WS has provides training on how to use the 4 km ensemble products to users in southwest Asia and CONUS via the AFWEPS page. Additional training will hinge on operational requirements in a particular area of ensemble forecasting application. Fly - Fight - Win 30

31 AF NUOPC Stretch Goal Goal: Create calibrated ceiling and visibility forecasts IOC: Use physics-based moisture-related predictors and regression against CONUS observations for probability of ceiling less than 3000/1000/500 feet and visibility less than 5/3/1 miles. FOC: Study physical ingredients more thoroughly, calibrate against real-time and local observations to achieve highly tuned probabilities Status: IOC complete Regression ingredients include 2 meter RH, 10 meter wind speed, and precipitable water Stoelinga and Warner (1999) equations provide estimates of uncertainty to calculate probability of ceiling or visibility restrictions due to precipitation FOC target implementation is 2012 Fly - Fight - Win 31

32 AF NUOPC Stretch Goal Implemented Initial CIG/VIS (Large-Scale) Prototype Develop and Optimize CIG/VIS Calibration/Verification Technique 2QCY09 SOW #1 SOW #2 End of Investigation 1QCY10 2QCY10 3QCY10 4QCY10 1QCY11 2QCY11 3QCY11 4QCY11 1QCY12 Begin CIG/VIS Cal / Ver Investigation Develop advancements of current capabilities Test advanced cig/vis capabilities Begin case studies of 1 st year s SOW Refine & Verify advanced new capabilities Test refined cig/vis capabilities Incorporate science of 2 nd year s SOW 2QCY12 Implement IOC: CIG/VIS on Global Ensemble Implement FOC: Calibrated & Verified CIG/VIS on Global & Regional Ensemble 32 Fly - Fight - Win

33 Questions? Fly - Fight - Win