Integrated Retrieval of the Temperature and Humidity Profiles of Atmospheric Boundary Layer by Combining Ground-Based Infrared Hyperspectral Interferometers and Microwave Radiometers

Atmospheric temperature and humidity profiles are the basic parameters used to describe the vertical distribution of atmospheric states. Continuous observations of accurate temperature and humidity profiles are essential for exploring boundary layer thermal and dynamic characteristics. To this end, an intelligent retrieval algorithm (IReA) based on a convolutional neural network (CNN) is proposed to retrieve atmospheric temperature and humidity profiles by combining observations from ground-based infrared hyperspectral radiometers and microwave radiometers (MWRs). The results show that the inclusion of microwave observations can effectively improve the retrieval accuracy of temperature and humidity profiles relative to the results from atmospheric emitted radiance interferometer (AERI) under clear-sky conditions, where the root mean square error (RMSE) of the temperature profile is 0.79 K and the RMSE of the humidity profile is 0.95 g/kg. The accuracies of different retrieval methods are also evaluated. In general, the RMSE derived from IReA is improved by at least 9% compared to the results from the physical retrieval method and BP neural network method. Given that clouds are semitransparent in the microwave region, the retrieval accuracy of the temperature and humidity profile of IReA are also improved under cloudy conditions when microwave observations are introduced.