Abstract
ABSTRACT The weather system over the Tibetan Plateau can cause disasters around the plateau and its downstream areas. Since there are few surface meteorological observation stations over the Tibetan Plateau, the precipitation here cannot be comprehensively detected. Therefore, satellite precipitation estimation products with high timeliness, accuracy, and full coverage are relied upon. This paper presents an AI-based precipitation estimation method using multi-frame FY-4A meteorological satellite data (FY-4A PRE-M). Firstly, multiple-derived features are constructed according to the brightness temperature of the satellite different channels, and the regional precipitation estimation model is then trained using the light gradient boosting machine (LGBM) algorithm. Based on the research results from precipitation retrieval using the infrared channel radiation features, this model analyzes the possible contribution of the radiation difference between channels at the same time and the radiation change in the same channel at different periods (15, 30, 45 and 60 min) to the precipitation estimation. The algorithm continuously updates the model with the history of available precipitation data. From 1 September 2021 to 31 August 2022, the precipitation estimations using the multi-frame FY-4A satellite data were evaluated through the ground rainfall gauge data based on the spatial – temporal matching. The results show that the multi-frame FY-4A satellite data have advantages in precipitation estimation. The heavy rainfall caused by a Tibetan Plateau shear line is evaluated using this algorithm. The results show that the precipitation estimations of FY-4A PRE-M and IMERG-L are close to the values of rain gauges. The FY-4A PRE-M can well describe the diurnal variations and the distribution of 24 h cumulative precipitation over the Tibetan Plateau, and the heavy rainfall belts in the central and northern Tibetan Plateau are also well monitored.
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