Spatial downscaling methods for the 2-meter air temperature grid data based on multiple machine learning models

doi:10.6046/zrzyyg.2022041

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Abstract

High-resolution meteorological data serve as an important data basis for fine-scale meteorological services. Using the hourly 2-meter air temperature grid data from January 2020 to March 2021 and the terrain factors such as altitude, longitude, and latitude, this study aimed to enhance the resolution of 2-meter air temperature grid data with a resolution of 1 km to 100 m through downscaling based on four machine learning methods, namely LightGBM (LGB), XGBoost (XGB), gradient boosting tree (GBT), and random forest (RF). Then, this study conducted the weighted fusion of downscaling results of different models. Finally, the downscaling results of different models were compared with the bilinear interpolation results, and the results are as follows. The results of each downscaling model were relatively consistent with the observational data. Compared with the bilinear interpolation results, the results of the LGB, XGB, and RF models had similar spatial structures but were more detailed. All downscaling models yielded the same spatio-temporal distribution characteristics of errors. Compared with the bilinear interpolation results, the data of the LGB, XGB, and GBT models showed significantly higher precision, and their root mean square errors (RMSEs) decreased by 5.2%, 4.1%, and 4.6%, respectively. Meanwhile, the RMSE after weighted fusion decreased by 5.9%, which was higher than that of any single machine learning model. The downscaling results of the LGB, XGB, and GBT models were improved to a certain degree compared with the bilinear interpolation results under different terrain conditions, especially in high-altitude areas (above 600 m). The correlation coefficients of results of the LGB, XGB, and BGT models and model based on weighted fusion increased by 0.45%, 0.40%, 0.63%, and 0.66%, respectively, and their RMSEs decreased by 9.1%, 8.0%, 12.7%, and 13.1%, respectively. These results indicate that the downscaling model based on the weighted fusion of different machine learning methods can both improve spatial resolution and maintain data precision and, thus, is suitable for downscaling research on 2-meter air temperature data in the study area. This study can be used as a reference for developing high-resolution data products.

Keywords real-time grid product 2-meter air temperature downscaling machine learning weighted fusion

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Issue Date: 20 March 2023

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	Xianfeng LI
	Zhengguo YUAN
	Weihua DENG
	Liyuan YANG
	Xueying ZHOU
	Lili HU

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Xianfeng LI,Zhengguo YUAN,Weihua DENG, et al. Spatial downscaling methods for the 2-meter air temperature grid data based on multiple machine learning models[J]. Remote Sensing for Natural Resources, 2023, 35(1): 57-65.

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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2022041 OR https://www.gtzyyg.com/EN/Y2023/V35/I1/57

Fig.1 Schematic map of the study area

Fig.2 Flow chart of the 2-meter temperature downscaling

Fig.3 the spatial distribution of 2 m temperature at 16: 00 on 1 March 2021

Fig.4 the RMSE spatial distribution of different models

Fig.5 Hourly variation of statistical index of different models between 1 January and 31 March in 2021

Fig.6 Comparison of statistical index of different models at different altitudes

Fig.7 Comparison of statistical index bias of different mode at different altitudes

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