1. Satellite Data as used in the Local Analysis and Prediction System (LAPS) Steve Albers May 13, 2008

2. LAPS cloud analysis METAR

3. 3D Cloud Image

4. Cloud / Radar W-E X-sect

5. Cloud Analysis Flow Chart

6. Cloud/precip cross section

7. Precip type and snow cover

8. Cloud/Satellite Analysis Topics 11 micron IR 3.9 micron data Improving visible with terrain albedo database CO2-Slicing method (Cloud-top pressure)

9. 11 micron imagery T(11u) best detects mid-high level clouds Cloud Clearing Step Cloud Building Step Iterative Adjustment Step –Forward model converts cloud-sounding T(11u) estimate –Constrained 1DVAR iteration fits cloud layers to observed T(11u)

10. 3.9 micron imagery T(3.9u) – T(11u) detects stratus at night –Currently used with 11u cloud-tops for cloud building –Testing underway for cloud-clearing –Additional criteria include T(11u) and land fraction T(3.9u) – T(11u) detects clouds in the daytime? –Visible may be similar in cloud masking properties –Visible may be easier for obtaining a cloud fraction Cloud Phase? –Could work using T(3.9u) – T(11u) at night –Cloud-top phase needs blending throughout LWC/ICE column

11. Visible Satellite Improving visible with terrain albedo database –Cloud-clearing (done with current analysis) –Cloud-building (now being tested) Easiest to do over low-latitude oceans Accurate sfc albedo can work with VIS + 11 micron cloud-tops Visible cloud fraction can be used to correct apparent brightness temperature to yield improved cloud-top temperature

12. Visible Satellite Impact

13. CO2 Slicing Method (cloud-top P) Subset of NESDIS Cloud-Top Pressure data –CO2 (fractional) measurements add value –11u measurements (0 or 1 cloud fraction) redundant with imagery? –Imagery has better spatial and temporal resolution? Treat as a “cloud sounding” similar to METARs and PIREPs

14. Temperature Analysis Derived Satellite Soundings –GOES –POES (via MADIS)

15. Wind Analysis Cloud Drift Winds (via MADIS) –3 hourly –Experimental 1 hourly

16. fdfdfd GOES/POES Soundings Cloud-Drift Winds ACARS Temperature ACARS/Radar Wind

17. Humidity Analysis GOES Retrieved Integrated Water Vapor (GVAP) Utilizes Gradients Tech Memo and Posters by Dan Birkenheuer Earlier method uses GOES sounder with OPTRAN

19. MM5 Fcst Synthetic Imagery Implemented by Brent Shaw

20. WRF Fcst Synthetic Imagery Ongoing with Isidora Jankov and Louie Grasso (CIRA) Future efforts incorporating Community Radiative Transfer Model (CRTM)?

21. Going Global with G-LAPS Global LAPS Domain running in real-time Expanding our satellite image coverage –global geosynchronous satellites

22. Global IR Satellite Animations for Science On A Sphere (SOS) Start with AWC / McIDAS animation Apply QC Overlay MTSAT imagery supplied by Taiwan CWB Merge with “Blue Marble” using variable transparency

23. Global Cylindrical IR image for SOS

24. Global IR Satellite Animations for Science On A Sphere (SOS) Testing with GSD / McIDAS feed 15 minute frames –time interpolation from 30 minute data –testing with Meteosat Second Generation

25. Further Information http://laps.noaa.gov

26. Questions??

27. Cloud Schematic

28. Selected references Albers, S., 1995: The LAPS wind analysis. Wea. and Forecasting, 10, 342-352. Albers, S., J. McGinley, D. Birkenheuer, and J. Smart, 1996: The Local Analysis and prediction System (LAPS): Analyses of clouds, precipitation and temperature. Wea. and Forecasting, 11, 273-287. Birkenheuer, D., B.L. Shaw, S. Albers, E. Szoke, 2001: Evaluation of local-scale forecasts for severe weather of July 20, 2000. Preprints, 14th Conf on Numerical Wea. Prediction, Ft. Lauderdale, FL, Amer. Meteor. Soc. Cram, J.M.,Albers, S., and D. Devenyi, 1996: Application of a Two-Dimensional Variational Scheme to a Meso-beta scale wind analysis. Preprints, 15 th Conf on Wea. Analysis and Forecasting, Norfolk, VA, Amer. Meteor. Soc. McGinley, J., S. Albers, D. Birkenheuer, B. Shaw, and P. Schultz, 2000: The LAPS water in all phases analysis: the approach and impacts on numerical prediction. Presented at the 5th International Symposium on Tropospheric Profiling, Adelaide, Australia. Schultz, P. and S. Albers, 2001: The use of three-dimensional analyses of cloud attributes for diabatic initialization of mesoscale models. Preprints, 14th Conf on Numerical Wea. Prediction, Ft. Lauderdale, FL, Amer. Meteor. Soc.

29. Derived products flow chart

30. Future Precip analysis efforts Combine radar with other data sources –Model First Guess –Rain Gauges –Satellite Precip Estimates (e.g. GOES/TRMM)

31. Future LAPS analysis work Surface obs QC –Operational use of Kalman filter (with time-space conversion) –Handling of surface stations with known bias Improved use of radar data for AWIPS –Multiple radars –Wide-band full volume scans –Use of Doppler velocities Obtain observation increments just outside of domain –Implies software restructuring Add SST to surface analysis Stability indices –Wet bulb zero, K index, total totals, Showalter, LCL (AWIPS) –LI/CAPE/CIN with different parcels in boundary layer –new (SPC) method for computing storm motions feeding to helicity determination More-generalized vertical coordinate?

32. Recent analysis improvements More generalized 2-D/3-D successive correction algorithm –Utilized on 3-D wind/temperature, most surface fields –Helps with clustered data having varying error characteristics –More efficient for numerous observations –Tested with SMS Gridded analyses feed into variational balancing package Cloud/Radar analysis –Mixture of 2D (NEXRAD/NOWRAD low-level) and 3D (wide-band volume radar) –Missing radar data vs “no echo” handling –Horizontal radar interpolation between radials –Improved use of model first guess RH &cloud liq/ice

33. Cloud type diagnosis Cloud type is derived as a function of temperature and stability

34. LAPS data ingest strategy

35. Dummy Image

36. Humidity Analysis Satellite Radiances