Estimation of Dense Time Series of Urban Air Temperatures from Multitemporal Geostationary Satellite Data

Monitoring and nowcasting of urban air temperatures are of high interest for prediction of heat stress in cities. Routine observation is so far limited by the complex coupling between atmosphere and land surface in urban areas, which makes estimation more difficult. In this study, we have investigated the capability of multitemporal land surface temperatures (LSTs) from the geostationary Spinning Enhanced Visible Infra-Red Imager instrument for estimation of urban air temperatures. The results are very promising with root-mean-square errors (RMSEs) of 1.5-1.8 K for six stations in Hamburg and explained variances of 97%-98%. Both the annual and diurnal cycles were well represented by the empirical models and the use of multitemporal data substantially increased the model performance. Further, the model was run in a forecast mode without actual LST information. Here, the best predictors reached RMSEs of 1.9-2.4 K and R² of 95%-97% for a 2-h forecast.