Precipitation estimation by infrared brightness temperature measurement of FengYun-4A imager

Wang, Gen; Song, Yafei; Shen, Yuan; Jiang, Yun

doi:10.1117/12.2682809

Cited by 1 publication

(1 citation statement)

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“…The data and code supporting the results of this work can be downloaded from https://doi.org/10.5281/zenodo. 10056935 (G. Wang, 2023). This study was jointly supported by the Outstanding Youth Science Fund for Universities in Anhui Province (Grant 2022AH020093), the Anhui Key Research and Development Programme Projects (Grant 2022h11020002), the Natural Science Foundation of Anhui Province (Grant 2108085QD183), the Anhui Provincial Colleges Excellent Young Talent Support Program Project (Grant gxyq2022081), the Chaohu University Scientific Research Startup Funding Project (Grant KYQD-202211), the Chaohu University Discipline Construction Quality Improvement Project (Grant kj22zsys02), and the National Natural Science Foundation of China (Grant 41805080).…”

Section: Conflict Of Interestmentioning

confidence: 99%

FY‐4A/AGRI Infrared Brightness Temperature Estimation of Precipitation Based on Multi‐Model Ensemble Learning

Wang,

Han,

et al. 2024

Earth and Space Science

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Satellite infrared detectors cannot penetrate clouds, especially precipitating clouds. Improving precipitation estimation accuracy based on infrared brightness temperature has always been important but challenging. In this paper, based on the infrared brightness temperature of the Advanced Geosynchronous Radiation Imager (AGRI) onboard China's Feng‐Yun 4A satellite, we develop and evaluate a new precipitation estimation method. First, using static data, physical characteristics of clouds, cloud image texture features, temporal motion features, and AGRI infrared channel brightness temperature, we construct features for a machine learning model. Then, we develop precipitation estimation methods. Precipitation is estimated in two steps: classification and regression. We employ a random forest classification model to identify whether there is precipitation in a given field of view. If there is precipitation, a multi‐model ensemble regression learning method is used to estimate the areas with this precipitation. The ensemble learning method uses convex optimization to integrate prediction results based on the optimization of hyperparameters of five basic models (i.e., those of random forest, XGBoost, LightGBM, decision tree, and extra tree models). Furthermore, two regression stacking ensemble models—the Least Absolute Shrinkage and Selection Operator (herein referred to as Stacking1‐LASSO) and K‐nearest neighbor (herein referred to as Stacking2‐KNN)—are used to predict the results of the aforementioned basic models. The results of basic models are used as inputs of these two stacking models. Finally, based on the Integrated Multi‐satellitE Retrievals for GPM (IMERG) precipitation product and rain gauge precipitation data, we conduct precipitation estimation experiments and evaluate our methods. The results show that ensemble learning models have greater accuracy in estimating precipitation than the basic models. When using IMERG precipitation as the target precipitation, ensemble learning models can estimate the central area of heavy precipitation during typhoons Ampil and Maria. The ensemble learning estimation effect is better than that of Stacking2‐KNN. Moreover, when rain gauge data is used as the target precipitation, ensemble learning can also estimate the center of heavy precipitation and with good consistency with recorded satellite brightness temperature data.

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Section: Conflict Of Interestmentioning

confidence: 99%