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Convective-Scale Numerical Weather Prediction and Data Assimilation Research At CAPS

Convective-Scale Numerical Weather Prediction and Data Assimilation Research At CAPS. Ming Xue Director Center for Analysis and Prediction of Storms and School of Meteorology University of Oklahoma mxue@ou.edu September, 2010. ARPS Simulated Tornado.

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Convective-Scale Numerical Weather Prediction and Data Assimilation Research At CAPS

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  1. Convective-Scale Numerical Weather Prediction and Data Assimilation Research At CAPS Ming Xue Director Center for Analysis and Prediction of Storms and School of Meteorology University of Oklahoma mxue@ou.edu September, 2010 ARPS Simulated Tornado

  2. Storm-Scale Convection-Allowing Ensemble and Convection-Resolving Deterministic Forecasting • CAPS/OU has been carrying out a project since 2007 to develop, conduct and evaluate realtime high-resolution ensemble and deterministic forecastsfor convective-scale hazardous weather. Forecasts were directly fed to the NOAA HWT (Hazardous Weather Testbed) and evaluated in realtime by forecasters and researchers in an organized effort. • Goals: To determine the optimal design, configurations, and post-processing of storm-scale ensemble prediction, and to provide the products for evaluation by forecasters and researchers, and test storm-scale data assimilation methods. • Spring 2010: 26-member 4-km ensemble and one 1-km forecastsfor full CONUS domain. 30-hourly daily forecasts over 7 weeks. Assimilation of data from 120+ radars. Multi-model (WSR-ARW, WRF-NMM and ARPS), multi-physics, perturbed IC and LBC (from SREF).

  3. June 14, 2010 OKC Flooding

  4. 1 km WRF-ARW forecasts of composite reflectivity 13h 13Z Observed radar mosaic reflectivity 14h 14Z 15h 15Z

  5. June 14, 2010 OKC Flooding 13h 13Z Probability-matched ensemble mean hourly accumulated precipitation (mm) Max=151mm Max=71% Raw probability ofhourly precipitation >0.5 inch 14h 14Z Max=125mm Max=71% 15h 15Z Max=141mm Max=64%

  6. 12–18Z accumulated precipitation: 18h(June 14, 2010 – OKC Flood Day) SSEF Prob match SSEF mean QPE SREF mean SREF Prob match NAM HWT images

  7. 18–0Z accumulated precipitation: 24h(June 14, 2010 – OKC Flood Day) SSEF Prob match SSEF mean QPE SREF mean SREF Prob match NAM HWT images

  8. ETS for 3-hourly Precip. ≥ 0.5 in 2009 (26-day) 2008 (32-day) With radar With radar no radar no radar 12 km NAM 12 km NAM Probability-matched score generally better than any ensemble member 2 km score no-better than the best 4-km ensemble member – may be due to physics 1-km score better than any 4-km member and than the 4 km PM score. Radar data clearly improves precipitation forecasts, up to 12 hours. High-resolution forecasts clearly consistently better than 12 km NAM.

  9. Comparisons of reflectivity GSS (ETS) scores of SSEF, HRRR and NAM for Spring 2010 CAPS SSEF Ensemble PM Mean CAPS SSEF 1 km Model CAPS SSEF ARW-CN(control w/o radar assimilation) CAPS SSEF ARW-C0(control w/o radar assimilation) HRRR NAM Corollary Lesson: To provide a “fair” comparison Between CAPS and HRRR, the 01Z and 13Z runs for HRRRshould be used

  10. Comparison of CAPS 4 km Cn/C0 2008 Forecasts with McGill 2-km MAPLE Nowcasting System and Canadian 15-km GEM Model 4km with radar MAPLE 4km with radar 4km no radar CSI for 0.2 mm/h Correlation for reflectivity Courtesy of Madalina Surcel of McGill U. (Surcel et al. 2009 Radar Conf.)

  11. Future Plan (over the next three years) • General direction: more emphasis on aviation weather (e.g., 3 weeks in June + May), more runs/day, shorter forecast ranges, fine-tuning of ensemble design, • Multi-scale IC perturbations, EnKF-based perturbations • More intelligent choices of physics suites, possibly introduce stochastic physics. • Addition of Navy’s COAMPS model (4 models total) • Improved control initial condition via advanced data assimilation • Possible EnKFdata assimilation • Possible hybrid ensemble-variational analysis based on the operational GSI framework • Produce calibrated storm-scale ensemble products. Post-analysis and probabilistic products: e.g., calibration, bias removal, detailed performance evaluation, cost-benefit/trade off assessment, effective products for end users (e.g., those for aviation weather, severe storms); • Integration/coordination with national mesoscale ensemble efforts (DTC/DET collaborations). • Possibly set up a CONUS-sized quasi-operational storm-scale ensemble forecasting system using university-based supercomputers.

  12. CAPS Realtime Convection-Allowing-Resolution Hurricane Forecasts • In fall 2010, CAPS is producing experimental single-large-domain 4-km hurricane forecasts over Atlantic • 48 hour forecasts twice daily (00 and 12 TC) • Two sets of WRF-ARW forecasts, using GFS and global EnKF analyses and corresponding LBCs. Global EnKF and forecasts produced by Jeff Whitaker of ESRL. • Goals: Assessing convection-resolving model in predicting TC genesis, track, intensity and structure. • Experiment ongoing and systematic evaluation to be performed.

  13. 87 h Prediction of Hurricane Earl at 4 km dx

  14. 42 hour forecast valid at 2 pm today Dx =4 km 1800x900x50 grid

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