Abstract
Satellite infrared hyperspectral instruments can obtain a wealth of atmospheric spectrum information. In order to obtain high-precision atmospheric temperature and humidity profiles, we used the traditional One-Dimensional Variational (1D-Var) retrieval algorithm, combined with the information capacity-weight function coverage method to select the spectrum channel. In addition, an Artificial Neural Network (ANN) algorithm was introduced to correct the satellite observation data error and compare it with the conventional error correction method. Finally, to perform the temperature and humidity profile retrieval calculation, we used the FY-3D satellite HIRAS (Hyperspectral Infrared Atmospheric Sounder) infrared hyperspectral data and combined the RTTOV (Radiative Transfer for TOVS) radiative transfer model to build an atmospheric temperature and humidity profile retrieval system. We used data on the European region from July to August 2020 to carry out the training and testing of the retrieval system, respectively, and used the balloon-retrieved sounding data of temperature and humidity published by the University of Wyoming as standard truth values to evaluate the retrieval accuracy. Our preliminary research results show that, compared with the retrieval results of conventional deviation correction, the introduction of ANN algorithm error correction can improve the retrieval accuracy of the retrieval system effectively and the RMSE (Root-Mean-Square Error) of the temperature and humidity has a maximum accuracy of improvement of about 0.5 K (The K represents the thermodynamic temperature unit) and 5%, respectively. The temperature and humidity results obtained by the retrieval system were compared with Global Forecast System (GFS) forecast data. The retrieved temperature RMSE was less than 1.5 K on average, which was better than that for the GFS; the humidity RMSE was less than 15% as a whole, and better than the forecast profile between 100 hpa (1 hpa is 100 pa, the pa represents the air pressure unit) and 600 hpa. Compared with AIRS (Atmospheric Infrared Sounder) products, the result of the retrieval system also had a higher accuracy. The main improvement of the temperature was at 200 hpa and 800 hpa, with maximum accuracy improvements of 2 K and 1.5 K, respectively. The RMSE of the humidity retrieved by the system was also better than the AIRS humidity products at most pressure levels, and the error of maximum difference could reach 15%. After combining the two algorithms, the FY-3D/HIRAS infrared hyperspectral retrieval system could obtain higher-precision temperature and humidity profiles, and relevant results could provide a reference for improving the accuracy of business products.
Highlights
Temperature and humidity profiles are important atmospheric parameters
In the conventional 1D-Var retrieval algorithm, we focus on introducing a Neural Network algorithm to correct the observation data error, using the information capacity-weight function coverage method to select temperature and humidity channels, and combine the RTTOV radiative transfer model to calculate the background error covariance and the observation error covariance matrix
We showed through experiments that the average RMSE error of the temperature and humidity profiles obtained by our retrieval system was less than the Global Forecast System (GFS) forecast profile, the retrieval result obtained by the conventional method, and the AIRS temperature and humidity products
Summary
Temperature and humidity profiles are important atmospheric parameters. Highaccuracy temperature and humidity profiles improve the accuracy of numerical weather prediction. The background error covariance matrix describes the error and correlation between the forecast value and the true value of each pressure level of the atmospheric state vector [47]. In this experiment, we used the GFS forecast data and ERA5 reanalysis data in the training data of the retrieval system model to calculate the deviation. Where x represents the background error; xki is the k-th sample data of the i-th level; E xi is the average error of the forecast value of the i-th level; n is the total number of samples.
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