Warn-on-Forecast Capabilities and Possible Contributions by CAPS By Ming Xue Center for Analysis and Prediction of Storms and School of Meteorology University of Oklahoma February, 2010 ARPS Simulated Tornado

Capabilities Storm-scale model development Data assimilation system development Ensemble forecasting Thunderstorm/tornado dynamics and predictability studies High-performance computing Pre- and post-processing/analysis

Storm-scale model development ARPS model – a complete system optimized to storm-scale applications. Thunderstorm dynamics, tornadogenesis, and physics process studies Part of storm-scale multi-model ensemble Initial test system for integrated scalable peta-scale EnKF/LETKF systems Prediction tool in CASA realtime forecasting demonstrations WRF ARW and NMM Capabilities to initialize the models using storm-scale radar assimilation capabilities Part of a multi-model ensemble framework Using ARW in our FAA project for RR and HRRR Model physics/dynamical/computational frameworks Advanced microphysics (e.g., multi-moment scheme) and their interaction with radar DA (e.g., dual-pol DA and microphysics parameter estimation) Parallel pre- and post-processing tools, QC

Ensemble forecasting A multi-model, multi-physics, perturbed IC/LBC storm-scale ensemble prediction framework and evaluation/demonstration via HWT spring forecast experiments Research on optimal configuration/perturbation generation techniques (e.g., ETKF) Ensemble post-processing/calibration Thunderstorm/tornado-scale ensemble forecasting using EnKF

Data assimilation Experience and expertise in variational data assimilation ARPS 3DVAR/cloud analysis system, and 4DVAR Knowledge and experience with GSI 3DVAR system Experience and expertise in developing and applying ensemble-DA systems ARPS EnKF system with sophisticated radar data assimilation capabilities (Vr, Z, dual-pol data, parameter estimation, coupling with two-moment microphysics scheme, multi-scale data sources, parallel capabilities) Developing an EnKF system for RR/HRRR (with ESRL) Developing an ensemble-var hybrid system based on GSI (with NCEP and ESRL) NSF Peta-Apps grant to develop a scalable ensemble DA system for peta-scale computers Opportunities to test these capabilities in realtime via HWT and in CASA.

Thunderstorm/tornado dynamics and predictability studies Involved in VORTEX-2 NSF tornado dynamics/DA grant CASA – tornado prediction/process studies/tornado vortex characterization Convective initiation studies with IHOP cases Sensitivity/predictability studies

High-performance computing Infrastructure development from LEAD Complete pre-processing/DA/prediction/post-processing capabilities scalable up to 10,000 processors and beyond NSF Peta-Apps project to develop a scalable ensemble DA system collaborating with CS scientists/supercomputing centers Completely portable/multi-platform workflow control system for complex realtime forecasting Access to national supercomputing resources for research and realtime experiments Experience/capability to develop/optimize scalable parallel systems

ETS for 3-hourly Precip. ≥ 0.5 in from HWT Spring Forecast Experiments 2008 (32-day) 2009 (26-day) 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. With radar no radar 12 km NAM With radar no radar 12 km NAM

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

BIAS for 1 h precip of 2009 ≥0.1 inch/h 12 h fcst of 1 h precip. ≥ 0.1in

50-m Grid Forecast v.s. Observation (Movie) Forecast Low-level Reflectivity Observed Low-level Reflectivity Movie 43 minute forecast Used ARPS 3DVAR/Cloud analysis DA Short-Range Radar Initialized Prediction of Thunderstorms, Strong Winds, Gust Fronts, Downbursts, and Tornadoes using NWP Model Using 3DVAR/Cloud Analysis DA

Observed Damage Track v.s Predicted Surface Wind Swaths Dx = 250 m > 1 hour long track 3 May 1999 F5-Tornado Outbreak in Central Oklahoma With 3-moment microphysics Required 3-moment microphysics for the best results

Movie Anx at 2155 UTC Obs at 0.48° Of OKC radar 2155 UTC 40 min fcst at 2235 UTC Obs at 0.48° of OKC radar 2235 UTC 70 x 70 km ARPS EnKF Results for the May 8, 2003 tornadic case (Lei et al. 2009)

OKC TDWR v.s. 500m Grid 15-min Fcst Low-level reflectivity from OKC TDWR radar at 2208 UTC, 8 May m forecast Z, Vort and Vectors at Z= 1km, 2210 UTC, 8 May No uniform storm-environment, mesoscale perturbations and mesonet data important

© Patrick Marsh 7:21pm (0021Z) Lawton Tornado Minco Tornado 10:54pm (0354Z) Tornadoes of 8-9 May 2007 El Reno tornado Lawton tornado Union City tornado CASA X-band Radar Network – 30 km range

Predictions at z=2km for May 9, UTC, 2h fcst Minco tornado at 0354Z 0420UTC, 2h 20min fcst 0440UTC, 2h 40min fcst El Reno tornado at 0443Z Union City tornado at 0426Z

Probabilistic/Ensemble Forecasts from EnKF Analyses Left: OKC radar observed reflectivity at 0335 UTC, May 9, 2007 Probability of Z>40dBZZ=40dBZ contours 90 min fcst at 0330 UTC

Vorticity contours from ensemble predictions at z=2km 0400UTC, 2h fcst Minco tor. at 0354Z 0420UTC, 2h 20min fcst 0440UTC, 2h40min fcst El Reno tor. at 0443Z Unit City tor. at 0426Z

May 9, 2007 – Ensemble/Probabilistic Forecasting CNTL case -- CASA and WSR-88D data assimilated using EnKF from 1:00Z to 2:00Z at 5 minute intervals. Observed tornado location (reported at approximately 3:54Z) is indicated by the red triangle. (Snook et al. 2010a,b – being submitted)

Planned CASA Forecast Experiment for Spring 2010 (Hour-long forecasts every 10 minutes) Rapidly updated forecasts DA: 3DVAR and later EnKF

Assimilation of Coastal Radar Data for Hurricane Ike (2008) Direct analysis of radar radial velocity data using ARPS 3DVAR Analysis of radar reflectivity via ARPS complex cloud analysis scheme Data from 2 coastal WSR-88D radars used (KLCH 、 KHGX) Prediction by ARPS or WRF-ARW 30 min cycles over 6 h ARPS or WRF Forecast with GFS LBC DA CTRL ARPS and WRF Forecast with GFS IC and LBC ARPS 3DVAR Cloud Analysis 0000 UTC 0600 UTC 1200 UTC 1800 UTC (Zhao and Xue 2009 GRL)

Reflectivity Observation and ARPS Forecast of Ike (2008),  x = 4 km +0h +3h +6h +9h +12h Observed composite reflectivity ARPS control forecast (vectors at z=3km) using GFS analysis at 06 UTC as IC and GFS LBC no radar with radar obs ARPS forecast with Ref+Vr every 30 min 0-6Z UTC using ARPS 3DVAR and cloud analysis and GFS background/LBC

Results using ARPS 3DVAR/Cloud Analysis GFS IC

Results assimilating data from two coastal radars using EnKF for Hurricane Ike (2008)

RR v.s. HRRR for an MCS case – hourly cycling with GSI and radar data Red: 3 km HRRR, Blue: 13 km RR Using DDFI with radar

Tests with HRRR configuration with hourly updated GSI and radar data HRRR_DFIRAD - self-cycled HRRR with RR DDFI RR_DFIRAD – using RR fcst background and RR DDFI RR_DFI – using RR background and standard WRF DFI RR_NoDFI – using RR background but no DFI

EnKF analysis using 2-moment microphysics for May 24, 2004 tornadic thunderstorm case KOUN Observation Analysis using KTLX data Reflectivity Z Diff. Ref. Zdr Specific diff. phase

Other results WRF Hybrid-DA system applied to Ike radar DA problem WRF Hybrid-DA for Ike over ocean

How can CAPS best contribute? Main areas: Development, testing and inter-comparison of VAR/EnKF/hybrid DA systems/methods Assimilation of dual-pol data in combination with advanced microphysics Design and testing of optimal WoF ensemble forecasting capabilities Dynamics/process/predictability/sensitivity studies Realtime forecasting demonstration/evaluation