Estimating All-Weather Surface Longwave Radiation from Satellite Passive Microwave Data

Abstract

Surface longwave radiation (SLR) is an essential geophysical parameter of Earth’s energy balance, and its estimation based on thermal infrared (TIR) remote sensing data has been extensively studied. However, it is difficult to estimate cloudy SLR from TIR measurements. Satellite passive microwave (PMW) radiometers measure microwave radiation under the clouds and therefore can estimate SLR in all weather conditions. We constructed SLR retrieval models using brightness temperature (BT) data from an Advanced Microwave Scanning Radiometer 2 (AMSR2) based on a neural network (NN) algorithm. SLR from the European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA5) product was used as the reference. NN-based models were able to reproduce well the spatial variability of SLR from ERA5 at the global scale. Validations indicate a reasonably good performance was found for land sites, with a bias of 1.32 W/m2, root mean squared error (RMSE) of 35.37 W/m2, and coefficient of determination (R2) of 0.89 for AMSR2 surface upward longwave radiation (SULR) data, and a bias of −2.26 W/m2, RMSE of 32.94 W/m2, and R2 of 0.82 for AMSR2 surface downward longwave radiation (SDLR) data. AMSR2 SULR and SDLR retrieval accuracies were higher for oceanic sites, with biases of −2.98 and −4.04 W/m2, RMSEs of 6.50 and 13.42 W/m2, and R2 values of 0.83 and 0.66, respectively. This study provides a solid foundation for the development of a PMW SLR retrieval model applicable at the global scale to generate long-term continuous SLR products using multi-year satellite PMW data and for future research with a higher spatiotemporal resolution.