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

?

Diurnal Hovmoller Diagrams: 10-km 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

Time-Latitude DiagramsA

ugus

t, 20

01

30 N 45 N 30 N 45 N

Stage IV WRF

Latitude Latitude

O F O F

O F O F

In all cases: POD=0, FAR=1, CSI=0

What does CSI=0 (or ETS=0) mean to you?

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

Defining objects

Original

Convolved Thresholded

WRF forecasts from 10-km grid

Fitting shapes:Reduce objects to small number of parameters

01

23

45

s4 wrf

July 13 - Region 1

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)