Impact of Thunderstorm Location Data Assimilation on Numerical Weather Forecasting

Abstract

Abstract The article compares four lightning detection networks, provides a brief overview of lightning observation data assimilation in numerical weather forecasts, and describes and illustrates the used procedure of lightning location and time assimilation in numerical weather forecasting. Evaluations of absolute errors in temperatures of air at 2 m, humidity at 2 m, air pressure near the surface, wind speed at 10 m, and precipitation are provided for 10 forecasts made in 2020 for days on which intensive thunderstorms were observed in the Krasnodar region of Russia. It has been found that average errors for the forecast area for 24, 48, and 72 h of the forecast decreased for all parameters when assimilation of observed lightning data is used for forecasting. It has been shown that the predicted precipitation field configuration and intensity became closer to references for both areas where thunderstorms were observed and the areas where no thunderstorms occurred.