An Interpretable Nonlinear Intelligent Bias Correction Method for FY-4A/GIIRS Hyperspectral Infrared Brightness Temperatures.
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| Title: | An Interpretable Nonlinear Intelligent Bias Correction Method for FY-4A/GIIRS Hyperspectral Infrared Brightness Temperatures. |
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| Authors: | Wang, Gen1 (AUTHOR) wanggen@chu.edu.cn, Xu, Bing2 (AUTHOR), Ye, Song1,3 (AUTHOR), Zhi, Xiefei3,4 (AUTHOR), Zhang, Tiening2,5 (AUTHOR), Yang, Youpeng1,4 (AUTHOR), Liu, Yang2 (AUTHOR), Xie, Feng3,5 (AUTHOR), Liu, Qiao4,5 (AUTHOR), Zhang, Haili1,5 (AUTHOR) |
| Source: | Remote Sensing. Mar2026, Vol. 18 Issue 5, p748. 24p. |
| Subjects: | Ensemble learning, Brightness temperature, Infrared spectroscopy, Data assimilation, Numerical weather forecasting |
| Abstract: | Highlights: What are the main findings? An intelligent bias correction method integrating ensemble learning and SHAP analysis is proposed for FY-4A/GIIRS brightness temperature data, significantly improving the accuracy of estimating the systematic bias component from observation increments, while enhancing model stability and generalization performance. The SHAP interpretability framework is applied to satellite bias correction for the first time, quantitatively revealing the complex nonlinear interaction mechanisms between key forecast predictors and the systematic bias component within observation increments. What are the implication of the main finding? This method generates high-quality bias-corrected brightness temperatures by effectively removing the systematic bias component from observation increments, providing a more reliable data foundation for the assimilation of hyperspectral satellite observation, thus supporting improvements in numerical weather prediction. A full-process example of "channel selection, intelligent correction, mechanism interpretation" is established, advancing the explainable and reliable application of artificial intelligence in the meteorological field. The hyperspectral infrared observations of the Geostationary Interferometric Infrared Sounder (GIIRS) on the Fengyun-4A (FY-4A) satellite are an important data source for numerical weather prediction (NWP) assimilation. However, there are systematic differences between observed and simulated brightness temperatures (i.e., the observation increments contain predictable systematic bias components). To address the issue that traditional linear methods struggle to capture the nonlinear relationships between biases and forecast predictors, this study proposes an intelligent bias correction method that integrates ensemble learning and explainable artificial intelligence. First, the entropy reduction method is used to select 69 mid-wave channels. Then, Random Forest, XGBoost, LightGBM, Decision Tree, and Extra Tree are used as base learners to construct a weighted average ensemble model. Training and validation are conducted using high-frequency clear-sky observation data from FY-4A/GIIRS during Typhoon Lekima. The results show that: (1) the ensemble learning correction method outperforms single models and traditional offline methods, with root mean square errors of brightness temperature bias of less than 0.9209 K for the training set and 1.4447 K for the test set; (2) Shapley Additive Explanations (SHAP)-based interpretability analysis reveals the contribution and nonlinear influence mechanisms of factors such as longitude, atmospheric thickness, surface temperature, and total precipitable water on bias correction. This study provides an intelligent bias correction framework with both high precision and explainability, offering a reference for the bias correction and assimilation applications of hyperspectral satellite observations like GIIRS. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? An intelligent bias correction method integrating ensemble learning and SHAP analysis is proposed for FY-4A/GIIRS brightness temperature data, significantly improving the accuracy of estimating the systematic bias component from observation increments, while enhancing model stability and generalization performance. The SHAP interpretability framework is applied to satellite bias correction for the first time, quantitatively revealing the complex nonlinear interaction mechanisms between key forecast predictors and the systematic bias component within observation increments. What are the implication of the main finding? This method generates high-quality bias-corrected brightness temperatures by effectively removing the systematic bias component from observation increments, providing a more reliable data foundation for the assimilation of hyperspectral satellite observation, thus supporting improvements in numerical weather prediction. A full-process example of "channel selection, intelligent correction, mechanism interpretation" is established, advancing the explainable and reliable application of artificial intelligence in the meteorological field. The hyperspectral infrared observations of the Geostationary Interferometric Infrared Sounder (GIIRS) on the Fengyun-4A (FY-4A) satellite are an important data source for numerical weather prediction (NWP) assimilation. However, there are systematic differences between observed and simulated brightness temperatures (i.e., the observation increments contain predictable systematic bias components). To address the issue that traditional linear methods struggle to capture the nonlinear relationships between biases and forecast predictors, this study proposes an intelligent bias correction method that integrates ensemble learning and explainable artificial intelligence. First, the entropy reduction method is used to select 69 mid-wave channels. Then, Random Forest, XGBoost, LightGBM, Decision Tree, and Extra Tree are used as base learners to construct a weighted average ensemble model. Training and validation are conducted using high-frequency clear-sky observation data from FY-4A/GIIRS during Typhoon Lekima. The results show that: (1) the ensemble learning correction method outperforms single models and traditional offline methods, with root mean square errors of brightness temperature bias of less than 0.9209 K for the training set and 1.4447 K for the test set; (2) Shapley Additive Explanations (SHAP)-based interpretability analysis reveals the contribution and nonlinear influence mechanisms of factors such as longitude, atmospheric thickness, surface temperature, and total precipitable water on bias correction. This study provides an intelligent bias correction framework with both high precision and explainability, offering a reference for the bias correction and assimilation applications of hyperspectral satellite observations like GIIRS. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18050748 |
