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

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 predict...

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Bibliographic Details
Published in:Advances in meteorology 2019, Vol.2019 (2019), p.1-10
Main Authors: Deng, Xiaobo, Zhang, Shenglan, Zhou, Qi, Liu, Hailei
Format: Article
Language:English
Subjects:
Accuracy
Air temperature
Atmospheric models
Brightness temperature
Channels
Correlation analysis
Correlation coefficient
Correlation coefficients
Elevation
Meteorological satellites
Methods
Model accuracy
Neural networks
Numerical weather forecasting
Precipitable water
Regression analysis
Relative humidity
Root-mean-square errors
Statistical analysis
Studies
Summer
Vegetation
Water vapor
Water vapour
Weather forecasting
Weather stations
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Summary: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.
ISSN:1687-9309
1687-9317
DOI:10.1155/2019/2385310