1. Air Force Weather Agency Fly - Fight - Win Satellite Data Assimilation at the U.S. Air Force Weather Agency JCSDA Science Workshop 2009 Satellite Data Assimilation at the U.S. Air Force Weather Agency JCSDA Science Workshop 2009 John Zapotocny Chief Scientist Approved for Public Release – Distribution Unlimited

2. Fly - Fight - Win Overview Mission Overview Products & Services Organization Meteorological Models Clouds and Surface Characterization Regional scale NWP JCSDA projects 2

3. Fly - Fight - Win 3 Mission Overview A Global Team for the Global Fight Maximizing America’s Air, Space, Cyberspace, and Land Power by enabling decision makers to exploit relevant environmental information across the full spectrum of warfare.

4. Fly - Fight - Win 4 Mission Overview Who We Support National Decision Makers Air Force and Army Warfighters Coalition Forces Base and Post Weather Units

5. Fly - Fight - Win 5 Products and Services Terrestrial Weather

6. Fly - Fight - Win 6 Products and Services Space Weather

7. Fly - Fight - Win 7 Products and Services Climatology

8. Fly - Fight - Win 8 AFWA Organization 1st Weather Group Offutt AFB 1st Weather Group Offutt AFB ~1400 Total Personnel FOA Commander Offutt AFB, Neb. FOA Commander Offutt AFB, Neb. A – Staff Offutt AFB A – Staff Offutt AFB 25th OWS D-M AFB, Ariz. 25th OWS D-M AFB, Ariz. 26th OWS Barksdale AFB, La. 26th OWS Barksdale AFB, La. 15th OWS Scott AFB, Ill. 15th OWS Scott AFB, Ill. 2nd WS Offutt AFB 2nd WS Offutt AFB 2nd SYOS Offutt AFB 2nd SYOS Offutt AFB AF Combat Weather Center Hurlburt Fld, FL AF Combat Weather Center Hurlburt Fld, FL 14th WS Asheville, N.C. 14th WS Asheville, N.C. 2nd Weather Group Offutt AFB 2nd Weather Group Offutt AFB Model Devel & Ops

9. Fly - Fight - Win Meteorological Models Clouds and Surface Characterization Clouds Cloud Depiction and Forecast System (CDFS) II World-wide Merged Cloud Analysis generated hourly Global and regional cloud forecasts Stochastic Cloud Forecast Model (SCFM) - Global Diagnostic Cloud Forecast (DCF) - Regional Surface characterization Land Information System (LIS) Soil moisture Snow depth, age, liquid content Soil temperature

10. Fly - Fight - Win 10 Polar Orbiting DataGeostationary Data Snow Analysis Resolution: 12 nmi Obs: Surface, SSM/I Freq: Daily, 12Z Surface Temp Analysis Resolution: 12 nmi Obs: IR imagery, SSM/I Temp Freq: 3 Hourly GFS Upper Atmos. Temp Near Surface Temp/RH/Wind Surface Observations World-Wide Merged Cloud Analysis (WWMCA) Hourly, global, real-time, cloud analysis @12.5nm Total Cloud and Layer Cloud data supports National Intelligence Community, cloud forecast models, and global soil temperature and moisture analysis. Global Cloud Analysis System Cloud Depiction and Forecast System II DMSP AVHRR Future NPOESS Supports IR Cloud Detection Supports Visible Cloud Detection Cloud Height

11. Fly - Fight - Win 11 Cloud Forecast Models Stochastic Cloud Forecast Model Global cloud cover model developed by 2 WXG (Dr. Dave McDonald) Pairs GFS Temp, RH, VV, and Surface Press. with WWMCA cloud amounts 16 th mesh Polar Stereographic projection 5 vertical layers 3-hr time step 84 hr forecast SCFM products:  Total fractional cloud coverage  Layer coverage (5-layers)  500 meter AGL, 850mb, 700mb, 500mb, 300mb layers

12. Fly - Fight - Win 12 Regional cloud cover model developed by AFRL (Don Norquist) Pairs AFWA WRF output with CDFS-II WWMCA analysis Statistically “chooses” which clouds best correlate with WRF “predictors” 45/15/5 km WRF grids & global ½ degree GFS grid 3-hr time step 30 to 80 hr forecast (depends on grid) DCF products:  Total fractional cloud coverage  layer coverage (5-layers)  layer top height & thickness  layer type Cloud Forecast Models Diagnostic Cloud Forecast

13. Fly - Fight - Win 13 Topography, Soils Land Cover, Vegetation Properties Meteorology Snow Soil Moisture Temperature Land Surface Models Data Assimilation Modules Soil Moisture & Temperature Evaporation Runoff Snowpack Properties Inputs Outputs Physics WRF Theater Forecasts Army/AF Tactical Decision Aid Software Crop Forecasts NCEP Applications Land Information System Satellite Data is Primary Input Satellite Data

14. Fly - Fight - Win Surface temperature and heat stress forecasts, 20 Jul 2007, 06Z cycle 10,0000 FT MSL Turbulence forecasts, 20 Jul 2007, 06Z cycle Weather Research and Forecast (WRF) model Development agent is NCAR Improved forecast capability over MM5 to better meet warfighter requirements Implemented for classified support Jul 06 Unclassified transition to WRF ongoing AFWA WRF DA system Currently 3DVAR (WRFVAR) Transition to GSI underway Meteorological Models Regional Scale NWP

15. Fly - Fight - Win 15 AFWA WRF Windows Proposed Configuration Central America, North Africa, and South Asia are 20km; all other parent windows are 15km.

16. Fly - Fight - Win JCSDA Projects DoD requirements demand improved cloud, aerosol, and surface trafficability forecasts JCSDA Projects underway to: Enhance cloud height and type specification Improve accuracy of cloud forecasts Couple land/air model assimilation and forecasts Improve accuracy/resolution of cloud, land, dust, and regional NWP models Long-Term Goal is coupled/unified data assimilation and forecast system AFWA Coupled Analysis & Prediction System

17. Fly - Fight - Win 17 (1 x 1 km grid spacing) Spatial resolution: Horizontal: 1 x 1 km, Vertical: # of layers in model (SFC to 10mb) Temporal resolution: 1hr steps for 0-12hrs, 3hr steps for 12-24hrs, 12hr steps for 24-72hrs Quantify aerosol/cloud “amount” on 1km grid for each layer of model Predict slant path (visible/IR) detection by integrating layered cloud/aerosol forecasts For visual acquisition, output defaults to CFLOS-like product that accounts for aerosols as well as clouds. For IR acquisition, output defaults to TDA product since we must account for sensor type, target temp, background temp, etc. in addition to slant path clouds, aerosols. DUST Layers of Model Modeled Clouds Modeled Aerosol (Dust) JCSDA Projects High Fidelity Cloud & Aerosol Characterization are Driving Requirements

18. Fly - Fight - Win WWMCA vs. CloudSAT 18 Cloud Coverage CloudSat Observed WWMCA Analyzed

19. Fly - Fight - Win 19 Visible - near-IR composite 1557 UTC Wed 5 Jun 05 North Pole Summer Cloud Mask Ice Snow Water- droplet cloud Ice- particle cloud Cloud Phase, Snow, Ice Mask Cloud Optical Properties Essential to EO Targeting

20. Fly - Fight - Win 20 AIRS Cloud Retrieval Hyperspectral DA for Cloud Specification 300 280 260 240 220 Retrieved Observed Retrieval with CRTM achieves close match with measurements Tracks transition from clear to cloudy Retrieved vs. measured cloudy AIRS spectra AIRS 11-μm brightness temperature (K)

21. Fly - Fight - Win NOGPS WGPS WGPS_250mb WGPS_damp3 WGPS_10mb  WGPS_250mb vs. WGPS & WGPS_250mb vs. NOGPS: Assimilation of COSMIC data only in troposphere sustains positive impacts in troposphere and decreases the RMSE of T forecasts in stratosphere as shown in WGPS.  WGPS_damp3 vs. WGPS: The enhanced damping at the model top only marginally changes the RMSE of T(U) forecasts.  WGPS_10mb vs. WGPS: Moving the model top to 10mb decreases the RMSE of U and T forecasts in the stratosphere. RMSE of 36hr Forecasts over SWA w.r.t. Sondes COSMIC Impact in WRF

22. Fly - Fight - Win 22 Precipitation Analysis LIS Enhancement Use high resolution climatology (PRISM) to constrain satellite precipitation observations TRMM Estimate High-Resolution Climatology Downscaled precipitation analysis Increasing reliance upon space-based precipitation observations PRISM Group on the web– http://www.prism.oregonstate.edu

23. Fly - Fight - Win 23 LIS-WRF Coupling AFWA, NASA Joint Project LIS initialized runs were able to reduce WRF warm bias LIS affected 0-48 hour fcst variables of surface weather, boundary layer, cloud, and precipitation LIS soil and snow fields capture fine scale surface features, reflecting important role in high resolution NWP Demonstrate and evaluate using LIS to initialize WRF SE Asia domain 4 seasonal test case periods Coupling via ESMF STUDY RESULTS:

24. Fly - Fight - Win 24 AER, Inc. Ice Water Content compared to Radar Returns Cloud Optical Properties Initial Comparisons Data courtesy of Atmospheric and Environmental Research, Inc. AF Cloud Profiling Radar: Ka-band AFRL radar New GOES-based algorithms improve cloud top height assignment AFWA implementing new algorithms in CDFS II Added benefits Improved analysis will improve cloud predictions from DCF, SCFM, & ADVCLD

25. Fly - Fight - Win 25 Long-Term Conceptual Design Unified Weather Analysis and Prediction System Satellite Data Land Data Assimilation (LIS) Coupled Data Mobility apps Global Snow Information Global Surface Albedo More accurate Weather forecasts Target Identification Global Cloud Information NWP initialization High resolution physics based Cloud forecasts Support CFLOS tools Directed Energy & TCA support Atmospheric Data Assimilation System (4D-VAR)

26. Fly - Fight - Win 26