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This study proposed an instantaneous summer air temperature (Tair) estimation model using the Himawari-8 Advanced Himawari Imager (AHI) brightness temperatures (BTs) in split-window channels and other auxiliary data. Correlation analysis and stepwise linear regression were used to select the predictors for Tair estimation. Nine predictors such as AHI BTs in channels 14 and 15, elevation, precipitable water vapor (PWV), and relative humidity (RH) were finally selected. Stepwise linear regression and neural network (NN) methods were applied to construct summer Tair estimation models over China, respectively. The estimated Tair by linear and NN models was evaluated using the observed Tair from 272 meteorological stations over China. The results showed that AHI BTs in channels 14 and 15, elevation, PWV, and RH were more important for Tair estimation than other predictors. The accuracy of the NN models was better than the linear models. The correlation coefficient (R), root mean square error (RMSE), and bias were 0.97, 1.72°C, and 0.04°C, respectively, for the NN model and were 0.89, 3.28°C, and 0.07°C, respectively, for the linear model. About 75.6% of the Tair differences by the NN model were within 2.0°C, and even 45.8% were within 1.0°C. The performance of the Tair estimation model for each site was also investigated, and the accuracy of Tair estimation in southeast China is better than northwest China.

Estimation of Summer Air Temperature over China Using Himawari-8 AHI and Numerical Weather Prediction Data