First, we quantify benefit of the extra channels in the estimation of precipitation intensity. Using the Advanced Himawari Imager on the Himawari-8 geostationary satellite as a proxy for the ABI, we have constructed a supervised machine learning methodology for the estimation of radar-like precipitation intensity. We derive the truth precipitation intensity data from the Global Precipitation Measurement Mission (GPM) Dual-Frequency Precipitation Radar (DPR) and time-align these data with Himawari-8 satellite images. A model is first trained using only those wavelengths available in GOES-13/15 as features. Once this baseline model is established, a series of models are trained by adding in a new GOES-R channel one at a time to the feature set. The performance of these models is then compared to the performance of the baseline GOES-13/15 model.
To assess the benefit of GLM, GOES-13/15 imager data is fused with total lightning data to create a virtual radar-like analysis of precipitation intensity. To do so, we post-process total lightning data obtained from NASA’s Pseudo Geostationary Lightning Mapper (PGLM) dataset. This data is used as a proxy for the lightning data generated from the GLM. This work will describe the use and benefits of combining lightning data with other data sources to construct an improved picture of radar-like precipitation intensity.