High Resolution Assimilation of Radar Data for Thunderstorm Forecasting on OSCER Keith A. Brewster, Kevin W. Thomas, Jerry Brotzge, Yunheng Wang, Dan Weber.

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Presentation transcript:

High Resolution Assimilation of Radar Data for Thunderstorm Forecasting on OSCER Keith A. Brewster, Kevin W. Thomas, Jerry Brotzge, Yunheng Wang, Dan Weber and Ming Xue CAPS and School of Meteorology University of Oklahoma 2007 OSCER Supercomputing Symposium Norman, Oklahoma, October, 2007

CASA & NEXRAD Radars CASA NetRad NSF ERC: Collaborative Adaptive Sensing of Atmosphere –X-Band Dual-Polarization Radars –30 km nominal range –Dual-Pol attenuation correction –Clutter filter –Collaborative, Adaptive Scanning –Fill-in below coverage of NEXRAD –Toward phased-array panels – low-cost! NEXRAD –S-Band Radars –Current Weather Service Operational Radars –14 covering domain –Data used out to 230 km

CASA NetRad IP1 Southwest Oklahoma

CASA NetRad KFDR NEXRAD

Near Real-Time NWP Runs CASA & NexRad CASA Only NexRad OnlyNo Radars Four Runs in Near Real-time Hourly output graphics posted to web: Weeks in Spring hour 1-km resolution forecasts Use Reflectivity ARPS Model ADAS Analysis with cloud analysis and latent heat adjustment Run on Parallel Linux Boxes OU OSCER (150 proc/2 runs at a time)

Forecast Domain  x = 1 km nx=603 ny=543 nz=53

Assimilation Strategy IAU hour Forecast40-min Assimilation IAU 2150 Forecast Model started manually when storms enter the network or with initial development within the network

Computing - Networking CASA Radars NexRad Radars Other Weather Observations Other Numerical Weather Model Data CASA SOCC CAPS Ingest Cluster IDD (Internet) Internet NFS OSCER Top Dawg HPC CAPS Webserver DataFile Archive

Computing – Number Crunching TopDawg Total 1024 processors Pentium4 Xeon EM64T 3.2 GHz GigE Network (976 Mbps, latency 9  s) ARPS Analysis/Forecast 150 processors per job (75 nodes) 4 forecasts, 2 jobs run at a time Horizontal Domain Decomposition, MPICH2 15x10 processors h wall for each forecast h wall for data processing and analysis ~50 GB of data each day created for archive

Building Pseudo-Volume from CASA Scans t 3  3 = 5.0˚ t 6  6 = 11.0˚ t 4  4 = 7.0˚ t 2  2 = 3.0˚ t 1  1 = 2.0˚ t 7  7 = 14.0˚ Low-level 360° Scan Upper-level Sector Scans t 0  0 = 1.0˚ Automated to select scans in middle of 10-min assimilation window. t 5  5 = 9.0˚

Summary of 2007 Cases 21 Sets of Runs for 20 Cases Weather Summary: –With Funnels and/or Tornadoes (2) –Squall Lines (8) –Clusters of Cells or Individual Cells (6) –Broken Lines and/or Cells on Boundaries (5) –Precip Area with Synoptic Scale Low (1) Some developing, some intensifying, decaying Wide Range of Time-of-Day

Sample Impact of CASA NetRad Data NEXRAD Only NetRad & NEXRAD ADAS Analysis of Hydrometeors Converted to Reflect Hgt=~250 m AGL

Cross-Section NEXRAD Only NEXRAD & NetRad NetRad Low-Level Info

01 Z May 09 Forecast Animation

Reflectivity All Radars Refl + Vr All Radars KTLX (Verif) 40 min - End of Assimilation Period

Reflectivity All Radars Refl + Vr All Radars KTLX (Verif) 40 min - End of Assimilation Period Stronger Vort Improved Refl

2-hour Forecast Reflectivity All Radars KTLX (Verif) Refl + Vr All Radars

2-hour Forecast Reflectivity All Radars KTLX (Verif) Refl + Vr All Radars Stronger, Closer

Vorticity During Assimilation

2-3 hr Vorticity Forecasts

Triangles: Observed Circulations No Radar Data

2-3 hr Vorticity Forecasts Solid Green: NetRad Only Orange: NexRad Only Red: Reflectivity Both

2-3 hr Vorticity Forecasts Radial Velocity Data Used from All Radars Four Circulations Tracked in this run

Accomplishments Completed MPI of ADAS with cloud analysis Able to process CASA radar –Automated Data Selection –Data I/O and Quality Control (ref & vel) –Remapping and hydrometeor assignment –Reflectivities are well-handled in model Baseline Runs Complete

Ongoing/Future Work Complete MPI of Vr analysis and run simulations with Vr Detailed verification of model forecasts –Comparison of QPF to NSSL 1-km Q2 –Comparison of location and strength of forecast circulation centers vs. radar-observed circulation centers (April 10, May 8/9) Prepare for 2008 Experiment –Complete MPI of 3DVAR and comparison of results –Integrate NWP in end-user training –Design near real-time evaluation for integration in NWC Hazardous WeatherTestbed