1. The Fusion of Radar Data and Satellite Imagery With Other Information in the LAPS Analyses
Steve Albers
April 15, 2002

2. LAPS radar ingest

3. Remapping Strategy Polar to Cartesian
Average Z,V of all gates directly illuminating each grid point
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
Before
After

10. Mosaicing Strategy (reflectivity)
Nearest radar with valid data used
+/- 10 minute time window
Final reflectivity field produced within cloud analysis
Wideband is combined with Level-III (NOWRAD/NEXRAD)
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
METAR
METAR

14. 3D Cloud Image

15. Cloud Schematic

16. Cloud Analysis Flow Chart

17. Derived products flow chart

18. Cloud/precip cross section

19. Precip type and snow cover

20. 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
Reflectivity limit helps reduce bright band effect

21. Storm-Total Precipitation

22. 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

23. 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)

24. Cloud/Satellite Analysis Additions
3.9 micron data
Improving visible with terrain albedo database
CO2-Slicing method (Cloud-top pressure)

25. 3.9 micron imagery
Difference of 3.9/11 micron detects stratus at night
Works with 11 micron cloud-tops for cloud building
Probably useful for cloud-clearing
Difference of 3.9/11 micron detects clouds in the daytime?
Visible may be similar in cloud masking properties
Visible may be easier for obtaining a cloud fraction
Cloud Phase?
Works from 3.9/11 micron difference at night
Cloud-top phase needs blending throughout LWC/ICE column

26. Visible Satellite Improving visible with terrain albedo database
current analysis only does cloud-clearing
Accurate sfc albedo can work with VIS + 11 micron cloud-tops for cloud building

27. Visible Satellite Impact

28. CO2 Slicing Method (cloud-top P)
Subset of NESDIS Cloud-Top Pressure data
CO2 measurements add value
11u measurements redundant with imagery
Treat as a “cloud sounding” similar to METARs and PIREPs

29. Selected references
Albers, S., 1995: The LAPS wind analysis. Wea. and Forecasting, 10,
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,
Birkenheuer, D., B.L. Shaw, S. Albers, E. Szoke, 2001: Evaluation of local-scale forecasts for severe weather of July 20, 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, 15th 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. The End

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