Comparison of the WRF-FDDA-Based Radar Reflectivity and Lightning Data Assimilation for Short-Term Precipitation and Lightning Forecasts of Severe Convection

Abstract

This work evaluates and compares the performance of the radar reflectivity and lightning data assimilation schemes implemented in weather research and forecasting-four-dimensional data assimilation (WRF-FDDA) for short-term precipitation and lightning forecasts. All six mesoscale convective systems (MCSs) with a duration greater than seven hours that occurred in the Guangdong Province of China during June 2020 were included in the experiments. The results show that both the radar reflectivity data assimilation and lightning data assimilation improved the analyses and short-term forecasts of the precipitation and lightning of the MCSs. On average, for precipitation forecasts, the experiments with radar reflectivity data assimilation performed better than those with lightning data assimilation; however, for lightning forecasts, the experiments with lightning data assimilation performed better in the analysis period and 1 h forecast, and for some cases, the superiority lasted to three forecast hours. This highlights the potential of lightning data assimilation in short-term lightning forecasting.

Impact of Aerosols on NPP in Basins: Case Study of WRF−Solar in the Jinghe River Basin

“…WRF is a mesoscale weather forecast model developed by the National Center for Atmospheric Research of the United States. This model has been successfully applied to data assimilation research [29,30], air quality modeling [31,32], and regional climate simulations, such as short−term rainfall forecasts [33] and typhoon simulation [34]. The WRF was dynamically scaled down using region nesting to improve the simulation resolution.…”