Cloudy-sky Longwave Downward Radiation Estimation by Combining MODIS and AIRS/AMSU Measurements

Longwave downward radiation (LWDR) is another main energy source received by the earth's surface except solar radiation. Its importance in regulating air temperature and balancing surface energy is enlarged especially under cloudy-sky. Unfortunately, to date, a large number of efforts have been made to derive LWDR from space under only clear-sky conditions leading to difficulty in utilizing space-based LWDR in most models due to its spatio-temporal discontinuity. Currently, only few studies focused on LWDR estimation under cloudy-sky conditions, while their global application is still questionable. In this paper, an alternative strategy is proposed aiming to derive high resolution(1km) cloudy-sky LWDR by fusing collocated satellite multi-sensor measurements. The results show that the newly developed method can work well and can derive LWDR at better accuracy with RMSE<27 W/m2 and bias < 10 W/m2 even under cloudy skies and at 1km scales. By comparing to CALIPSO-CloudSat-CERES-MODIS (CCCM) and SSF products of CERES, MERRA, ERA-interim and NCEP-CSFR products, the new approach demonstrates its superiority in terms of accuracy, temporal variation and spatial distribution pattern of LWDR. The comprehensive comparison analyses also reveal that, except for the proposed product, other four products (CERES, MERRA, ERA-interim and NCEP-CSFR) also show a big difference from each other in the LWDR spatio-temporal distribution pattern and magnitude. The difference between these products can still up to 60W/m2 even at the monthly scale, implying large uncertainties in current LWDR estimations. Besides the higher accuracy of the proposed method, more importantly, it provides unprecedented possibilities for jointly generating high resolution global LWDR datasets by connecting the NASA's Earth Observing System-(EOS) mission (MODIS-AIRS/AMSU) and the Suomi National Polar-orbiting Partnership-(NPP) mission (VIIRS-CrIS/ATMS). Meanwhile, the scheme proposed in this study also gives some clues for multiple data fusing in the remote sensing community.