Introduction of Lightning into the GSMaP Rainfall Measurements through Optimized Power-Law Model

Abstract Lightning and rainfall are associated with severe weather conditions. These two parameters, occurring either simultaneously or with a lag, have an intrinsic relationship. This study focuses on characterizing this inherent lightning–rainfall relation, which in turn is used to perform corrections of rainfall rate estimation based on satellite measurements. The approach is to analyze the lightning–rainfall relation at varying spatial and temporal scales and to select the best resolution at which the lightning flash rate can best model rainfall volume. Then, the parameters of a power-law model are optimized to assimilate lightning flashes into the Global Satellite Mapping of Precipitation (GSMaP) rainfall product. The results revealed that the lightning and rainfall relationship varies depending on the weather system. The lightning–rainfall relation, which can be empirically described by a power law, is most apparent in thunderstorms followed by frontal systems and least in typhoons. The differences in relationships are due to the variations in lightning and rainfall collocation per weather system. Rainfall volumes are also found to vary heavily on low lightning flash rates. Considering the best-case resolution of 100 km and 60 min for thunderstorms, the lightning-corrected GSMaP rainfall volume shows a reduction in root-mean-square error (RMSE) by about 59% and an increase in the correlation from 0.6 to 0.7 concerning radar rainfall volume. The correction scheme is also effective from resolutions of 40 to 250 km in thunderstorms, achieving moderate positive correlations with RMSE about 5 mm h −1 and below. Significance Statement Accurate and timely information on precipitation is necessary to mitigate the impacts of rainfall-related hazards. While satellites provide consistent rainfall information at wide coverage, satellite-based measurements are still prone to error. Considering the association between rainfall and lightning occurrences, we came up with a technique to improve the performance of the GSMaP rainfall measurements through the introduction of lightning data. This study is a step forward to leveraging the future global coverage of lightning measurements from satellite-based lightning mappers that can enable more accurate global rainfall estimates.