70 Development of a GSI-Based EnKF System with Polarimetric Radar Data Assimilation Capabilities for Storm-Scale Ensemble Forecasts

Tuesday, 23 October 2018
Stowe & Atrium rooms (Stoweflake Mountain Resort )
Bryan J. Putnam, CAPS/Univ. of Oklahoma, Norman, OK; and Y. Jung, N. Yussouf, D. R. Stratman, T. A. Supinie, and M. Xue

Dual-polarimetric (dual-pol) radar observations, such as differential reflectivity (ZDR), provide additional information on the microphysical state of convective storms compared to reflectivity alone. This includes further details on the size, phase, and concentration of hydrometeors. These details can be used to better estimate the model microphysical state in data assimilation systems to initialize storm-scale ensemble forecasts (SSEFs) that use multi-moment microphysics schemes, which can contain over a dozen state variables to estimate. The national WSR-88D S-band network has already been upgraded to dual-pol capabilities and several recent studies have shown the benefit of assimilation of dual-pol observations on storm-scale analyses and short-term forecasts. As part of the Spectrum Efficient National Surveillance Radar (SENSR) initiative, we are developing the capability to assimilate the next generation rapid-scan dual-pol Multi-function Phased Array Radar (MPAR) observations in the operational GSI-Based EnKF system. Our goal is to improve the initialization of SSEFs using both WSR-88D and experimental rapid-scan radar observations for the National Severe Storm Laboratory’s (NSSL) experimental Warn-On-Forecast system. The DA system is tested using NSSL’s experimental dual-pol S-band radar KOUN using 2-minute sector scans as a proxy for future dual-pol Phased Array Radar (PPAR) observations of the 31 May 2013 El Reno, OK tornado and Oklahoma City flash flood event. Two sets of experiments will be used for comparison: the first assimilates reflectivity, radial velocity, and conventional observations and the second experiment assimilates ZDRin addition to these observations. ZDRobservations identify gradients of raindrop size which highlight storm-scale processes such as size sorting that are not observed with reflectivity for improved forecast initialization. The impact of the ZDRobservations on short-term forecasts of the event will be assessed both qualitatively and quantitatively within the newly developed system, including testing of probabilistic forecasts preferred in the Warn-on-Forecast system.
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