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.