![]() (2005, 2006, 2007, 2008) introduced the 3DVAR methodology for radar data assimilation into the Weather Research and Forecasting Model (WRF). Three-dimensional variational data assimilation (3DVAR) is a commonly used method for radar data assimilation because of its relative simplicity and low computational cost. Studies have shown that radar data assimilation can help with short-term prediction of convective weather by providing more accurate and detailed information on the mesoscale structure in the initial condition ( Qiu and Xu 1992 Shapiro et al. The poorly described mesoscale features in the initial condition lead to an inability of current NWP models to accurately simulate the timing, location, and evolution of convective storms, which later lowers forecast skills as the integration time increases. Current conventional observation systems often provide very little scale-appropriate information for convective storms ( Guichard et al. An important reason for this low skill is that modeling of convection depends very much on the quality of the initial condition ( Kalnay 2003 Tong and Xue 2008). At present, NWP models only have limited skill in convective storm prediction and related quantitative precipitation forecast (QPF) ( Weckwerth et al. ![]() ![]() With the ongoing upgrade of the current Weather Surveillance Radar-1988 Doppler (WSR-88D) network to include dual-pol capabilities (started in early 2011), the findings from this study can be a helpful reference for utilizing the dual-pol radar data in numerical simulations of severe weather and related quantitative precipitation forecasts.ĭespite the steady improvement in high-resolution numerical weather prediction (NWP) models in the last two decades, accurate forecast of convective storms remains a significant challenge ( Emanuel et al. Additionally, K DP and Z DR data assimilation is shown to be superior to Z H and Z DR and Z H-only data assimilation when the warm-rain microphysics is adopted. Significant positive impacts on short-term forecast are obtained for both storms. The results show that the Z H, Z DR, K DP, and VR data substantially improve the initial condition for two mesoscale convective storms. The main goals of this study are first to demonstrate and compare the impact of various dual-pol variables in initialization of real case convective storms and second to test how the dual-pol fields may be better used with a 3DVAR system. A warm-rain scheme is constructed to assimilate Z H, Z DR, and K DP data using the three-dimensional variational data assimilation (3DVAR) system with the Advanced Research Weather Research and Forecasting Model (ARW-WRF). In this study, the horizontal reflectivity Z H, differential reflectivity Z DR, specific differential phase K DP, and radial velocity VR collected by the C-band Advanced Radar for Meteorological and Operational Research (ARMOR) are assimilated for two convective storms. The dual-pol radar measurements have been shown to provide a more accurate precipitation estimate compared to traditional radars. The dual-polarization (dual pol) Doppler radar can transmit/receive both horizontally and vertically polarized power returns.
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