An Improved Method Combining ANN and 1D-Var for the Retrieval of Atmospheric Temperature Profiles from FY-4A/GIIRS Hyperspectral Data

Abstract

In our study, a retrieval method of temperature profiles is proposed which combines an improved one-dimensional variational algorithm (1D-Var) and artificial neural network algorithm (ANN), using FY-4A/GIIRS (Geosynchronous Interferometric Infrared Sounder) infrared hyperspectral data. First, according to the characteristics of the FY-4A/GIIRS observation data using the conventional 1D-Var, we introduced channel blacklists and discarded the channels that have a large negative impact on retrieval, then used the information capacity method for channel selection and introduced a neural network to correct the satellite observation data. The improved 1D-Var effectively used the observation information of 1415 channels, reducing the impact of the error of the satellite observation and radiative transfer model, and realizing the improvement of retrieval accuracy. We subsequently used the improved 1D-Var and ANN algorithms to retrieve the temperature profiles, respectively, from the GIIRS data. The results showed that the accuracy when using ANN is better than using improved 1D-Var in situations where the pressure ranges from 800 hPa to 1000 hPa. Therefore, we combined the improved 1D-Var and ANN method to retrieve temperature profiles for different pressure levels, calculating the error by taking sounding data published by the University of Wyoming as the true values. The results show that the average error of the retrieved temperature profiles is smaller than 2 K when using our method, this method makes the accuracy of the retrieved temperature profiles superior to the accuracy of the GIIRS products from 10 hPa to 575 hPa. All in all, through the combination of the physical retrieval method and the machine learning retrieval method, this paper can certainly provide a reference for improving the accuracy of products.