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 ( T_{a,\text {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 T_{a,\text {cloudy}} and CTT, CTH. The results showed that it is feasible to estimate T_{a,\text {cloudy}} using CTT and CTH, especially for low and middle cloud conditions. Linear and neural network (NN)-based T_{a,\text {cloudy}} estimation models were constructed and validated using the Visible Infrared Imaging Radiometer Suite (VIIRS) CTT, CTH, and GFS T_{\text {air}} for summer 2017 and 2018. The NN model performs better than the linear model, and GFS T_{\text {air}} can obviously improve the accuracy of T_{a,\text {cloudy}} estimation. The correlation coefficient (R), root-mean-square error (RMSE), and bias of the NN model with GFS T_{\text {air}} 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.