1. The Fusion of Radar Data and Satellite Imagery With Other Information in the LAPS Analyses Steve Albers August 10, 2010

2. LAPS Radar Ingest Level-II Broadcast Data (IRADS Network) Level-II Broadcast Data (IRADS Network) GSD Central Facility Processing GSD Central Facility Processing Polar netCDF File (GSD “NIMBUS” Format) Polar netCDF File (GSD “NIMBUS” Format) Remap_polar_netcdf.exe 2-D LAPS Grid Reflectivity (VRC) 2-D LAPS Grid Reflectivity (VRC) 3-D LAPS Grid Ref + Vel (VXX) 3-D LAPS Grid Ref + Vel (VXX) Mosaic_radar.x (multiple radar input) Mosaic_radar.x (multiple radar input) 2-D LAPS Grid Reflectivity (VRC) 2-D LAPS Grid Reflectivity (VRC) 3-D LAPS Grid Reflectivity (VRZ) 3-D LAPS Grid Reflectivity (VRZ) NOWRAD netCDF (Low-level Reflectivity) NOWRAD netCDF (Low-level Reflectivity) Level-III AWIPS Level-III AWIPS LAPS script (LapsRadar.pl) calls WFO program (tfrNarrowband2netCDF) vrc_driver.x

3. Remapping Strategy Polar to Cartesian –2D or 3D result (narrowband / wideband) –Average Z,V of all gates directly illuminating each grid box –QC checks applied –Typically produces sparse arrays at this stage

4. Doppler & Other Wind Obs

5. Single / Multi-radar Wind Obs

6. Wind Analysis Flow Chart

7. LAPS 700Hpa Winds

8. Remapping Strategy (reflectivity) Horizontal Analysis/Filter (Reflectivity) –Needed for medium/high resolutions (<5km) at distant ranges –Replace unilluminated points with average of immediate grid neighbors (from neighboring radials) –Equivalent to Barnes weighting at medium resolutions (~5km) –Extensible to Barnes for high resolutions (~1km) Vertical Gap Filling (Reflectivity) –Linear interpolation to fill gaps up to 2km –Fills in below radar horizon & visible echo

9. Horizontal Filter/Analysis BeforeAfter

10. Mosaicing Strategy (reflectivity) Nearest radar with valid data used +/- 10 minute time window Final 3D reflectivity field produced within cloud analysis –Wideband is combined with Level-III (NOWRAD/NEXRAD) –Non-radar data contributes vertical info with narrowband –QC checks including satellite Help reduce AP and ground clutter

11. Reflectivity (800 hPa)

12. Radar X-sect (wide/narrow band)

13. LAPS cloud analysis METAR

14. 3D Cloud Image

15. Cloud Schematic

16. Cloud Analysis Flow Chart

17. Derived products flow chart

18. Cloud/precip cross section

19. Surface Precipitation Accumulation Algorithm similar to NEXRAD PPS, but runs in Cartesian space Rain / Liquid Equivalent –Z = 200 R ^ 1.6 Snow case: use rain/snow ratio dependent on column maximum temperature –Checks on Z and T could be added to reduce bright band effect

20. Storm-Total Precipitation

21. Future Cloud / Radar analysis efforts Account for evaporation of radar echoes in dry air –Sub-cloud base for NOWRAD –Below the radar horizon for full volume reflectivity Processing of multiple radars and radar types –Evaluate Ground Clutter / AP rejection

22. Future Cloud/Radar analysis efforts (cont) Consider Terrain Obstructions Improve Z-R Relationship –Convective vs. Stratiform Precipitation Analysis –Improve Sfc Precip coupling to 3D hydrometeors –Combine radar with other data sources Model First Guess Rain Gauges Satellite Precip Estimates (e.g. GOES/TRMM)

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

24. 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)

25. 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

26. Visible Satellite Improving visible with terrain albedo database –Cloud-clearing (done with current analysis) –Cloud-building (now being tested) 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

27. Visible Satellite Impact

28. CO2 Slicing Method (cloud-top P) Subset of NESDIS Cloud-Top Pressure data –CO2 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

29. 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.

30. Precip type and snow cover

31. The End

32. 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?

33. 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

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

35. LAPS data ingest strategy

36. Cloud/precip cross section

37. The End

38. LAPS radar ingest