wrf verification: new methods of evaluating rainfall prediction chris davis ncar (mmm/rap)...
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WRF Verification:
New Methods of Evaluating Rainfall Prediction
Chris Davis
NCAR (MMM/RAP)
Collaborators: Dave Ahijevych, Mike Baldwin, Barb Brown, Randy Bullock, Jennifer Mahoney, Kevin Manning, Rebecca Morss, Stan Trier, John Tuttle and Wei Wang
WRF Verification Effort
Case studies
Real-time forecasts
Extended-period case studies
Idealized tests of physical parameterizations
Application of new verification methods
Objectives of New Verification Methods
Reduce dimension of verification problem
Make statistics sensitive to error magnitude
Address and target fundamental processes in models
Provide useful feedback to developers and users
Make automated, yet insightful
00 Z
00 Z
12 Z
110 W 102 W 94 W 86 W 78 W
“Standard”: 102-110 W
“Out of phase”:96-102 W
Semidiurnal: 92-96 W
Mainly Diurnal: 78-92 W
Daily Cycle of Rainfall (Echo Frequency)
Diurnal Rainfall Signatures in NWP models
Models:
Method:
NCEP Eta: hydrostatic, 22-km, 50 levels, eta (step-mountain) coordinate, two-phase ice, Betts-Miller-Janjic cumulus scheme, MYJ boundary layer, OSU land surface model. Two 48-h forecasts per day.
Weather Research and Forecast Model (WRF): nonhydrostatic, 22-km, 28 levels, height-coordinate, two-phase ice, Betts-Miller-Janjic cumulus scheme, MRF boundary layer, slab surface model. Two 48-h forecasts per day.
Compile 3-hourly precipitation forecasts and analyses for July and August 2001.
Analyze all data to common 10-km grid.
Average precipitation from 30 N – 45 N.
Assume “echo” is averaged 3-h rainfall > 0.1 mm.
00Z Eta 12-36 h
12Z Eta 12-36 h
00Z WRF 12-36 h
12Z WRF 12-36 h
GM
TG
MT
Stage IV
GM
T
Longitude Longitude
Diurnal Hovmoller Diagrams: 22-km Eta and WRF
?
An Example of Rainfall Prediction Errors
Left: 24-42 h forecasts from WRF model
Right: Observations from NCEP analysis
Gray: 40% echo freq. from 4-year climatology
110 W 78 W
A Proposed Approach (based somewhat on Ebert and McBride)
– Define precipitation/convective objects and shapes
– Diagnose errors in location, shape, orientation, size,
timing, etc.
– Characterize basic attributes of precipitation/convection
within objects: intensity, density, etc.
– In parallel: Investigate user issues
Summary and Issues
Large NWP-model errors (WRF, Eta) in the diurnal and
propagating aspects of warm-season rainfall
Better representation of latitude of rainfall than longitude
Do we need cloud-resolving grids to capture properly?
Rainfall Statistics
Method yields errors on location (x,y,t), size and
orientation of rain areas and allows partitioning of
areas with similar attributes
PDFs of rainfall intensity are evaluated: appropriate for
application to inherently stochastic processes
How will this improve models more readily than
“traditional” methods (ETS, bias)?
Rain-area Verification
•Intensity PDF contains more information than bias: strongly tied to cumulus and/or cloud physics schemes
•Systematic shape errors could indicate problems in identifying modes of organized convection
•Systematic timing/location errors could point to errors in treating diurnal and orographic effects
Summary and Issues (continued)