All-Sky Temperature and Humidity Retrieval from the MWRI-RM Onboard the FY-3G Satellite

Abstract. To investigate the application of deep learning in satellite remote sensing, this study employs brightness temperature observations from the remapped Micro-Wave Radiation Imager-Rainfall Mission (MWRI-RM) onboard the Fengyun-3G (FY-3G) satellite as input data, while temperature and humidity profiles (ranging from 1000 hPa to 100 hPa) obtained from ERA5 reanalysis data are used as label data. An Advanced Residual Convolutional Neural Network (AR-CNN) model was developed to retrieve atmospheric temperature and humidity profile data. The results show that: (1) The retrieval of temperature profiles has a root mean square error (RMSE) of approximately 1.24 K, and the RMSE for humidity profile retrieval is 12.98 %. (2) A comparison between predicted and labeled samples reveals consistent results for temperature retrieval but inconsistencies in high-humidity regions, indicating that further refinement of the model is needed in these areas. (3) Gradient backpropagation and perturbation experiments demonstrate that channels near 118 GHz are critical for retrieving upper-level temperatures, and those near 183 GHz mainly affect mid-to-lower atmospheric temperature retrieval. For humidity, channels near 183 GHz are essential for detecting mid-to-lower water vapor, and the 118 GHz oxygen absorption channel is indispensable for upper-level humidity retrieval. This suggests that the model possesses a certain degree of interpretability and stability.