Estimation of Air Temperature under Cloudy Conditions Using Satellite-Based Cloud Products

This letter presents a novel method for instantaneous air temperature under cloudy conditions (Ta,cloudy) estimation using satellite-derived cloud top temperature (CTT), cloud top height (CTH), and Global Forecast System (GFS) forecasts. The radiosonde profiles were used to analyze the relationship between Ta,cloudy and CTT, CTH. The results showed that it is feasible to estimate Ta,cloudy using CTT and CTH, especially for low and middle cloud conditions. Linear and neural network (NN)-based Ta,cloudy estimation models were constructed and validated using the Visible Infrared Imaging Radiometer Suite (VIIRS) CTT, CTH, and GFS Tair for summer 2017 and 2018. The NN model performs better than the linear model, and GFS Tair can obviously improve the accuracy of Ta,cloudy estimation. The correlation coefficient (R), root-mean-square error (RMSE), and bias of the NN model with GFS Tair were 0.953, 1.950 °C, and -0.030 °C, respectively. The estimation model performed better under low and warm clouds than high and cold cloud conditions.