The Impact of Assimilating ZDR Observations on Storm-Scale Ensemble Forecasts of the 31 May 2013 Oklahoma Storm Event

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  • 1 Center for Analysis and Prediction of Storms, University of Oklahoma, Norman, OK
  • 2 School of Meteorology, Norman, OK
  • 3 Cooperative Institute for Mesoscale Meteorology Studies, University of Oklahoma, Norman, OK
  • 4 NOAA/OAR/National Severe Storms Laboratory, Norman, OK
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Abstract

Assimilation of dual-polarization (dual-pol) observations provides more accurate storm-scale analyses to initialize forecasts of severe convective thunderstorms. This study investigates the impact assimilating experimental sector-scan dual-pol observations has on storm-scale ensemble forecasts and how this impact changes over different data assimilation (DA) windows using the ensemble Kalman filter (EnKF). Ensemble forecasts are initialized after 30, 45, and 60 minutes of DA for two sets of experiments that assimilate either reflectivity and radial velocity only (EXPZ) or reflectivity and radial velocity plus differential reflectivity (EXPZZDR). This study uses the 31 May 2013 Oklahoma event which included multiple storms that produced tornadoes and severe hail, with focus placed on two storms that impacted El Reno and Stillwater during the event.

The earliest initialized forecast of EXPZZDR better predicts the evolution of the El Reno storm than EXPZ, but the two sets of experiments become similar at subsequent forecast times. However, the later EXPZZDR forecasts of the Stillwater storm, which organized towards the end of the DA window, produce improved results compared to EXPZ, in which the storm is less intense and weakens. Evaluation of forecast products for supercell mesocyclones (updraft helicity [UH]) and hail show similar results with earlier EXPZZDR forecasts better predicting the UH swaths of the El Reno storm and later forecasts producing improved UH and hail swaths for the Stillwater storm. The results indicate that the assimilation of ZDR over fewer DA cycles can produce improved forecasts when DA windows sufficiently cover storms during their initial development and organization.

Corresponding author address: Ming Xue, Center for Analysis and Prediction of Storms, University of Oklahoma, 120 David L. Boren Blvd., Norman, OK 73072, mxue@ou.edu

Current affiliation: NOAA/NWS/NCEP Weather Prediction Center, College Park, MD

Current affiliation: NOAA/NWS Office of Science and Technology Integration, Silver Spring, MD

Abstract

Assimilation of dual-polarization (dual-pol) observations provides more accurate storm-scale analyses to initialize forecasts of severe convective thunderstorms. This study investigates the impact assimilating experimental sector-scan dual-pol observations has on storm-scale ensemble forecasts and how this impact changes over different data assimilation (DA) windows using the ensemble Kalman filter (EnKF). Ensemble forecasts are initialized after 30, 45, and 60 minutes of DA for two sets of experiments that assimilate either reflectivity and radial velocity only (EXPZ) or reflectivity and radial velocity plus differential reflectivity (EXPZZDR). This study uses the 31 May 2013 Oklahoma event which included multiple storms that produced tornadoes and severe hail, with focus placed on two storms that impacted El Reno and Stillwater during the event.

The earliest initialized forecast of EXPZZDR better predicts the evolution of the El Reno storm than EXPZ, but the two sets of experiments become similar at subsequent forecast times. However, the later EXPZZDR forecasts of the Stillwater storm, which organized towards the end of the DA window, produce improved results compared to EXPZ, in which the storm is less intense and weakens. Evaluation of forecast products for supercell mesocyclones (updraft helicity [UH]) and hail show similar results with earlier EXPZZDR forecasts better predicting the UH swaths of the El Reno storm and later forecasts producing improved UH and hail swaths for the Stillwater storm. The results indicate that the assimilation of ZDR over fewer DA cycles can produce improved forecasts when DA windows sufficiently cover storms during their initial development and organization.

Corresponding author address: Ming Xue, Center for Analysis and Prediction of Storms, University of Oklahoma, 120 David L. Boren Blvd., Norman, OK 73072, mxue@ou.edu

Current affiliation: NOAA/NWS/NCEP Weather Prediction Center, College Park, MD

Current affiliation: NOAA/NWS Office of Science and Technology Integration, Silver Spring, MD

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