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<p>Timely and accurate rainfall information is crucial for mitigating rainfall-related hazards. The Global Satellite Mapping of Precipitation (GSMaP) provides high-resolution rainfall data through the combination of microwave radiometer (MWR) and infrared (IR) retrievals in a Kalman filter. Because MWR retrievals are temporally sparse, GSMaP is susceptible to errors due to IR only sensing cloud top instead of actual precipitation. The inherent relationship between lightning and rainfall provides an opportunity for improving the accuracy of rainfall estimates in thunderstorms. This study explored the feasibility of integrating satellite-based lightning data into the GSMaP near-real-time (NRT) algorithm. The Geostationary Lightning Mapper (GLM) lightning flash rates are introduced as additional feedback in the Kalman filter of GSMaP NRT, generating the GSMaP NRT with Lightning (NRTL) product over the contiguous United States (CONUS). Results for August 2024 show that lightning information tends to reduce overestimation and underestimation of GSMaP in mature and initiating/dissipating thunderstorms, respectively. NRTL also reduced the root mean square error (RMSE) of NRT by about 3.89&#xa0;mm h<sup>− 1</sup> in mature thunderstorms and increased its correlation from 0.09 to 0.2. Overall performance reveals that NRTL outperforms NRT spatially, with higher Kling-Gupta Efficiency (KGE) scores, especially over the central and eastern United States.</p>

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Leveraging GLM Lightning Data over CONUS in GSMaP NRT Precipitation Intensity Estimates

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月22日

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  • Archie Veloria,
  • Hitoshi Hirose,
  • Yuuki Wada,
  • Tomoo Ushio

摘要

Timely and accurate rainfall information is crucial for mitigating rainfall-related hazards. The Global Satellite Mapping of Precipitation (GSMaP) provides high-resolution rainfall data through the combination of microwave radiometer (MWR) and infrared (IR) retrievals in a Kalman filter. Because MWR retrievals are temporally sparse, GSMaP is susceptible to errors due to IR only sensing cloud top instead of actual precipitation. The inherent relationship between lightning and rainfall provides an opportunity for improving the accuracy of rainfall estimates in thunderstorms. This study explored the feasibility of integrating satellite-based lightning data into the GSMaP near-real-time (NRT) algorithm. The Geostationary Lightning Mapper (GLM) lightning flash rates are introduced as additional feedback in the Kalman filter of GSMaP NRT, generating the GSMaP NRT with Lightning (NRTL) product over the contiguous United States (CONUS). Results for August 2024 show that lightning information tends to reduce overestimation and underestimation of GSMaP in mature and initiating/dissipating thunderstorms, respectively. NRTL also reduced the root mean square error (RMSE) of NRT by about 3.89 mm h− 1 in mature thunderstorms and increased its correlation from 0.09 to 0.2. Overall performance reveals that NRTL outperforms NRT spatially, with higher Kling-Gupta Efficiency (KGE) scores, especially over the central and eastern United States.