Joe Klemp

National Center for Atmospheric Research

Boulder, Colorado

Convection Resolving NWP using WRF

36h WRF Precip Forecast

Analyzed Precip

27 Sept. 2002

Goals: Develop an advanced mesoscale forecast and assimilation system, and accelerate research advances into operations

• Collaborative partnership, principally among NCAR, NOAA, DoD, OU/CAPS, FAA, and university community• Multi-agency WRF governance; development conducted by 15 WRF Working Groups • Software framework provides portable, scalable code with plug-compatible modules• Ongoing active testing and rapidly growing community use

– Over 1,400 registered community users, annual workshops and tutorials for research community– Daily experimental real-time forecasting at NCAR , NCEP, NSSL, FSL, AFWA, U. of Illinois

• Operational implementation at NCEP and AFWA in FY04

Weather Research and Forecasting Model

1 10 100 km

Cumulus ParameterizationResolved Convection

LES PBL Parameterization

Two Stream Radiation3-D Radiation

Model Physics in High Resolution NWP

Physics“No Man’s Land”

Convection Resolving NWP using WRF

Questions to address:

• Is there any increased skill in convection-resolving forecasts, measured objectively or subjectively?

• Is there increased value in these forecasts?

• What can we expect given that the detailed aspects of convection may be inherently unpredictable at forecast

times of O(day)?

• If the forecasts are more valuable, are they worth the cost?

• Terrain-following hydrostatic pressure vertical coordinate

• Arakawa C-grid, two-way interacting nested grids (soon)

• 3rd order Runge-Kutta split-explicit time differencing

• Conserves mass, momentum, dry entropy, and scalars using flux form prognostic equations

• 5th order upwind or 6th order centered differencing for advection

• Physics for CR applications: Lin microphysics, YSU PBL, OSU/MM5 LSM, Dudhia shortwave/RRTM longwave radiation, no cumulus parameterization

WRF Mass Coordinate Core

Model Configuration for 4 km Grid

• Domain

– 2000 x 2000 km, 501 x 501 grid– 50 mb top, 35 levels– 24 s time step

• Initialization

– Interpolated from gridded analyses– BAMEX: 40 km Eta CONUS analysis– Isabel: 1o GFS global analysis (~110 km)

• Computing requirements

– 128 Processors on IBM SP Power 4 Regatta– Run time: 106 min/24h of forecast

Bow Echo and Mesoscale Convective Vortex Experiment (BAMEX)

Goal: Study the lifecycles of mesoscale convective vortices and bow echoes in and around the St. Louis MO area

10 km WRF forecast domain4 km WRF forecast domain

Field program conducted 20 May – 6 July 2003

Real-time WRF 4 km BAMEX Forecast

Composite NEXRAD Radar

4 km BAMEX forecast 36 h Reflectivity

4 km BAMEX forecast 12 h Reflectivity

Valid 6/10/03 12Z

Real-time WRF 4 km BAMEX Forecast

Composite NEXRAD RadarReflectivity forecast

Initialized 00 UTC 9 June 03

Real-time WRF 4 km BAMEX Forecast

Initialized 00 UTC 10 June 03

Reflectivity forecast Composite NEXRAD Radar

Real-time 12 h WRF Reflectivity Forecast

Composite NEXRAD Radar

4 km BAMEX forecast

Valid 6/10/03 12Z

10 km BAMEX forecast

22 km CONUS forecast

Real-time WRF 4 km BAMEX Forecast

Composite NEXRAD Radar23 h Reflectivity Forecast

Line ofSupercells

Valid 5/30/03 23Z

Realtime WRF 4 km BAMEX Forecast

Composite NEXRAD Radar30 h Reflectivity Forecast

Squall line

6” hail 00Z

Valid 6/23/03 06Z

Realtime WRF 4 km BAMEX Forecast

Composite NEXRAD Radar30 h Reflectivity Forecast

Missed

Valid 6/12/03 06Z

Realtime WRF 4 km BAMEX Forecast

Composite NEXRAD RadarReflectivity Forecast

12 h

24 h

Squall line

Persists Dissipates

Initialized 5/24/03 00Z

Preliminary BAMEX Forecast Verification

(Done, Davis, and Weisman)

Number of MCSs for each 36 h forecast initialized at 00 UTC.

Observed Forecast

Observed Model

Hovmoller Depiction of Hourly Precip

Data have been averaged in the latitudinal direction

Preliminary BAMEX Forecast Verification

(Done, Davis, and Weisman)

Subjective analysis of organized convection

Criteria for successful forecast: forecast system within 400 km and 3 h of those observed.

Probability of detection (POD) = 58%

False alarm rate (FAR) = 28%

Cases Observed

Yes No

CasesPredicted

123 47

90

Yes

No

Preliminary Findings for BAMEX Forecasts

• Rapid spinup of storm-scale structure from large-scale IC

• Forecasts were helpful to field operations planning, particularly on the number of systems, their mode and location

• 4 km WRF replicates overall MCS structure and character better than 10 km WRF with cumulus parameterization

– More detailed representation of convective mode

– No improvement in precipitation threat scores

• Skill in forecasting systems as high after 21 h as during the first 6-12 h, suggesting mesoscale control of initiation

• Convective trigger function wasn’t needed

Convection resolving forecasts should be a useful tool for predicting significant convective outbreaks and severe weather

Hurricane Isabel

NOAA –17 AVHRR 13 Sep 03 14:48 GMT

Hurricane Isabel Track

18/1700Z

10 km WRFInitialized 15/1200Z

4 km WRFInitialized 17/0000Z

Hurricane Isabel 3 h Precip Forecast

Initialized:12 UTC 15 Sep 03

WRF Model10 km grid

5 day forecast

48 h Hurricane Isabel Reflectivity Forecast

4 km WRF forecastRadar Composite

Initialized 00 UTC 17 Sep 03

Hurricane Isabel Reflectivity at Landfall

Radar Composite

18 Sep 2003 1700 Z

41 h forecast from 4 km WRF

Hurricane Isabel Surface-Wind Forecast

Initialized:00 UTC 17 Sep 03

WRF Model4 km grid

2 day forecast

Problems with Traditional Verification Schemes

truth forecast 1 forecast 2

Issue: the obviously poorer forecast has better skill scores

From Mike Baldwin NOAA/NSSL

Scientific Questions for Storm-Scale NWP

• What is the predictability of storm-scale events, and will resolution of fine-scale details enhance or reduce their prediction?

• What observations are most critical, and can high-resolution data from national networks be used to initialize NWP models in real time?

• What physics is required, and do we understand it well enough for practical application?

• How can ensembles be utilized for storm-scale prediction?

• What are the most useful verification techniques for storm and mesoscale forecasts?

• What networking and computational infrastructures are needed to support high-resolution NWP?

• How can useful decision making information be generated from forecast model output?