Cloudy-sky land surface longwave downward radiation (LWDR) estimation by integrating MODIS and AIRS/AMSU measurements
Longwave downward radiation (LWDR) is another major energy source received by the earth's surface apart from solar radiation. Its importance in regulating air temperature and balancing surface energy is enlarged especially under cloudy-sky conditions. 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 a few studies are focused on LWDR estimation under cloudy skies, while their global application is still questionable. In this paper, an alternative strategy is proposed aiming to derive high-resolution (1 km) cloudy-sky LWDR by fusing collocated satellite multi-sensor measurements. The results show that the newly developed method works well and can derive LWDR at a better accuracy with RMSE < 27 W/m2 and bias < 10 W/m2 even under cloudy skies and at 1 km scales. By comparing the CCCM and SSF products of CERES, MERRA, ERA-interim and NCEP-CSFR over the Tibetan Plateau region, the new approach demonstrates its superiority in terms of accuracy, temporal variation and spatial distribution patterns of LWDR. Comprehensive comparison analysis also reveals that, except for the proposed product, the other four products (CERES, MERRA, ERA-interim and NCEP-CSFR) show a big difference from each other in the LWDR spatio-temporal distribution pattern and magnitude. The difference between these products can still be up to 60 W/m2 even at the monthly scale, implying that large uncertainties exist in current LWDR estimations. More importantly, besides the higher accuracy of the proposed method, 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 towards multiple data fusing in the remote sensing community.