Title: The Fusion of Radar Data and Satellite Imagery With Other Information in the LAPS Analyses
1The Fusion of Radar Data and Satellite Imagery
With Other Information in the LAPS Analyses
- Steve Albers
- January 23, 2003
2LAPS radar ingest
3Remapping 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
4Doppler Other Wind Obs
5Single / Multi-radar Wind Obs
6Wind Analysis Flow Chart
7LAPS 700Hpa Winds
8Remapping Strategy (reflectivity)
- Horizontal Analysis/Filter (Reflectivity)
- Needed for medium/high resolutions (lt5km) 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
9Horizontal Filter/Analysis
Before
After
10Mosaicing 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
11Reflectivity (800 hPa)
12Radar X-sect (wide/narrow band)
13LAPS cloud analysis
METAR
METAR
METAR
143D Cloud Image
15CloudSchematic
16 Cloud Analysis Flow Chart
17Derived products flow chart
18Cloud/precip cross section
19Precip type and snow cover
20Surface 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
21Storm-Total Precipitation
22Future 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
23Future 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)
24Cloud/Satellite Analysis Topics
- 11 micron IR
- 3.9 micron data
- Improving visible with terrain albedo database
- CO2-Slicing method (Cloud-top pressure)
2511 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)
263.9 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
27Visible 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
28Visible Satellite Impact
29CO2 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
30Selected 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, 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.
31The End
32Future 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?
33Recent 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
34Cloud type diagnosis
Cloud type is derived as a function of
temperature and stability
35LAPS data ingest strategy
36Dummy Image