Performance of FY-4B GIIRS temperature products under cloudy skies and their enhancement of surface precipitation type forecasting

Accurately identifying precipitation types and their timings during the cold season is vital for decision-makers to minimize adverse effects on transportation, the economy, and public safety. However, a persistent challenge remains in forecasting precipitation types due to the incorrect thermal structure in numerical weather models. Building upon our previous work, this study first evaluates the performance of FY-4B GIIRS temperature products by contrasting them with the ERA5 dataset and FY-4 A GIIRS, further examines the ability to diagnose precipitation types. The temperature products of FY-4B GIIRS demonstrate better agreement with ERA5 than those of FY-4 A GIIRS. This is evident in the lower RMSE across various pressure levels, and especially by its decreased instances of missing data coupled with a higher proportion of high-quality data (QFlag: perfect and good). An abrupt heavy snow event was carried out to analyze the evolution of FY-4B GIIRS's low-level temperature and its correlation with precipitation type during rapid rain-to-snow transitions. FY-4B GIIRS successfully tracked diurnal thermal variations in Luoyang and Zhengzhou, aligning with observed precipitation types shifts. It is noteworthy that it represented varied cooling durations in the two cities, consistent with real-time cold air movements. While ECMWF IFS predictions of precipitation types were spot-on for Luoyang, they exhibited significant discrepancies in Zhengzhou due to flawed thermal structures. Given the ECMWF IFS's inconsistent mix-phase forecasting, integrating FY-4B GIIRS low-level temperatures with ground-based 2 m temperatures can offer valuable insights for forecaster.