Joe Klemp National Center for Atmospheric Research Boulder, Colorado Convection Resolving NWP using WRF

36h WRF Precip Forecast Analyzed Precip 27 Sept 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

km Cumulus Parameterization Resolved Convection LES PBL Parameterization Two Stream Radiation 3-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) 3 rd order Runge-Kutta split-explicit time differencing Conserves mass, momentum, dry entropy, and scalars using flux form prognostic equations 5 th order upwind or 6 th 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: 1 o 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 domain 4 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 forecastComposite 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 of Supercells 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 PersistsDissipates 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. ObservedForecast

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 YesNo Cases Predicted 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 WRF Initialized 15/1200Z 4 km WRF Initialized 17/0000Z

Hurricane Isabel 3 h Precip Forecast Initialized: 12 UTC 15 Sep 03 WRF Model 10 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 Z 41 h forecast from 4 km WRF

Hurricane Isabel Surface-Wind Forecast Initialized: 00 UTC 17 Sep 03 WRF Model 4 km grid 2 day forecast

Problems with Traditional Verification Schemes truth forecast 1forecast 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?