Machine Learning-Driven Retrieval of All-Weather Precipitable Water Vapor From Satellite MODIS Thermal Infrared Observations

Water vapor is an important component in the Earth’s climate and weather processes, underscoring the critical need for precise water vapor data on both regional and global scales. In this paper, an innovative retrieval method is developed to retrieve precipitable water vapor (PWV) using thermal infrared (IR) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) in all weather conditions. Unlike the operational MODIS PWV retrieval algorithm that utilizes 11 IR channels from 4.5 μm to 14.2 μm, two split-window bands at 11 μm and 12 μm as well as one PWV absorption band at 7.2 μm are used. The retrieval approach defines the functional relationship between the retrieved PWV and brightness temperature and multiple dependence elements – latitude, longitude, height, view zenith angle, month, solar zenith angle, and cloud, based on a machine learning method. The retrieval algorithm does not require the first guess and surface emissivity that are traditionally used in satellite IR retrieval methods. The results show that the new PWV retrievals outperform operational PWV data from MODIS thermal IR observations, reducing the root-mean-square error by 26.49% and 12.58% when compared with reference Global Navigation Satellite System (GNSS) and radiosonde PWV, respectively. The retrieval method can reduce the retrieval errors of IR PWV estimates at different clear confidence levels, illustrating the capability of the proposed retrieval approach. This study offers a machine learning-driven retrieval approach to derive all-weather water vapor retrievals using satellite-sensed MODIS thermal IR observations, which could be also applicable to other similar IR sensors.