CASA Prediction System over Dallas–Fort Worth Urban Network: Blending of Nowcasting and High-Resolution Numerical Weather Prediction Model

Abstract

This study targeted improving Collaborative Adaptive Sensing of the Atmosphere’s (CASA) 6-h lead time predictive ability by blending the radar-based nowcast with the NWP model over the Dallas–Fort Worth (DFW) urban radar network. This study also depicts the recent updates in CASA’s real-time reflectivity nowcast system by assessing nine precipitation cases over the DFW urban region. CASA’s nowcast framework displayed better primer outcomes than the WRF Model forecast for the lead time of 1 h and 30 min. After that time, the predictive ability of the nowcast framework began decreasing compared to the WRF Model. To broaden CASA’s predictive system lead time to 6 h, the WRF Model forecasts were blended with Dynamic and Adaptive Radar Tracking of Storms (DARTS) nowcast. The HRRR model analysis was used as initial and boundary conditions in the WRF Model. The high-resolution dual-pol radar observations were assimilated into the WRF Model through the 3DVAR data assimilation technique. Three kinds of blending strategies were used and the results were compared: 1) hyperbolic tangent curve (HTW), 2) critical success index (CSIW), and 3) salient cross dissolve (Sal CD). The sensitivity studies were conducted to decide desirable parameters in the blending techniques. The outcomes proved that blending enhanced the prediction skills. Also, the overall performance of blending relies on the accuracy of the WRF forecast. Even though blending results are mixed, the HTW-based technique performed better than the other two techniques.

Improved Streamflow Forecast in a Small-Medium Sized River Basin with Coupled WRF and WRF-Hydro: Effects of Radar Data Assimilation

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The Effect of Anthropogenic Heat and Moisture on Local Weather at Industrial Heat Islands: A Numerical Experiment

“…Therefore, a numerical analysis is carried out using mesoscale Weather Research and Forecasting (WRF) model in the present study. The WRF is a widely used model for regional weather [41,42], air quality [43] and climate simulations [44]. The WRF model-simulated atmospheric profiles are used as first guess for various satellite-based rainfall estimation algorithms [45][46][47].…”

Cross Validation of TEMPEST-D and RainCube Observations Over Precipitation Systems

“…The time scale of most cloud microphysics processes is short, and high temporal resolution measurements are needed to properly capture them. Ground weather radar networks can observe the atmosphere with very high temporal resolution [1]. Many studies [2]- [4] have used weather radar observations to analyze the evolution of cloud microphysical processes within storms.…”