Abstract
FY-4A/GIIRS (Geosynchronous Interferometric Infrared Sounder) is the first infrared hyperspectral atmospheric vertical detector in geostationary orbit. Compared to other similar instruments, it has the advantages of high temporal resolution and stationary relative to the ground. Based on the characteristics of GIIRS observation data, we proposed a humidity profile retrieval method. We fully utilized the information provided by the observation and forecast data, and used the two-dimensional brightness temperature data with the dimension of time and optical spectrum as the input of the CNN (convolution neural network model). Then, the obtained brightness temperature data were shown to be more suitable as the input for the physical retrieval method for humidity than the conventional correction method, improving the accuracy of humidity profile retrieval. We performed two comparative experiments. The first experiment results indicate that, compared to ordinary linear correction and ANN (artificial neural network algorithm) correction, our revised observed brightness temperature data are much closer to the simulated brightness temperature obtained by inputting ERA5 reanalysis data into RTTOV (Radiative Transfer for TOVS). The results of the second experiment indicate that the accuracy of the humidity profile retrieved by our method is higher than that of conventional ANN and 1D-Var (one-dimensional variational algorithm). With ERA5 reanalysis data as the reference value, the RMSE (Root Mean Squared Error) of the humidity profiles by our method is less than 8.2% between 250 and 600 hPa. Our method holds the unique advantage of the high temporal resolution of GIIRS, improves the accuracy of humidity profile retrieval, and proves that the combination of machine learning and the physical method is a compelling idea in the field of satellite atmospheric remote sensing worthy of further exploration.
Highlights
Accurate atmospheric temperature and humidity profiles can be obtained based on hyperspectral infrared observations, and are mainly utilized in numerical weather predictions, disaster weather warnings, etc. [1,2,3,4]
The target output during model training was the simulated brightness temperature obtained by inputting the ERA5 data into the RTTOV model
Combined with the weight function curve in Section 4.3.1, we focused on the humidity profiles of the retrieval of the three methods between 250 and 600 hPa
Summary
Accurate atmospheric temperature and humidity profiles can be obtained based on hyperspectral infrared observations, and are mainly utilized in numerical weather predictions, disaster weather warnings, etc. [1,2,3,4]. Accurate atmospheric temperature and humidity profiles can be obtained based on hyperspectral infrared observations, and are mainly utilized in numerical weather predictions, disaster weather warnings, etc. The common hyperspectral infrared detection instruments carried on meteorological satellites mainly include AIRS 2021, 13, 4737 reports [5,6] have revealed that satellite hyperspectral infrared data have a very important impact on the assimilation and prediction results of the NWP AIRS and IASI have been assimilated by many NWP centers, such as the European Centre for Medium-Range Weather Forecasts and the National Centers for Environmental Prediction in the U.S, which have very positive impacts on improving the accuracy of water, wind and weather forecasts. With the further improvements of the NWP vertical resolution, it is expected that hyperspectral infrared data will play a greater role [7,8,9,10]
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