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Surface longwave radiation (SLR) derived from remotely sensed data facilitates understanding of the SLR in global climate change. Hyperspectral infrared sounders aboard space platforms provide information on the surface and vertical structure of Earth’s atmosphere. However, currently, SLR products estimated from these observations are unavailable, which hampers their application potential for Earth’s radiation budget in the context of global warming. To address this issue, we developed simple and effective SLR model under clear-sky conditions using at-sensor spectral radiances from Atmospheric Infrared Sounder (AIRS). The model was found to be insensitive to AIRS instrument noise, and showed good performances based on a simulation dataset. The AIRS footprint geometrical model was proposed to match the AIRS and Moderate Resolution Imaging Spectroradiometer (MODIS) data to estimate the cloud fraction. Validation against ground-based measurements found that the surface upward longwave radiation model has a bias of 3.18 W/m2, root-mean-square error (RMSE) of 30.51 W/m2, and R2 of 0.84; the surface downward longwave radiation model has a bias of 0.77 W/m2, RMSE of 29.09 W/m2, and R2 of 0.78. The large validation biases at two ground sites reflect the limited spatial representativeness for AIRS footprints. Terrain-induced altitude differences and spatial inhomogeneity can redistribute the spatial distributions of SLR. Moreover, the model performances were weakly dependent on seasonal variation. The results indicate that the proposed model provides a foundation for the further development of operational SLR products.

Short-term earthquake prediction remains a challenge. In this study, we investigated earthquake predictability using a consecutive statistical evaluation framework (CSEF). Two widely used anomaly detection methods—Z-score (ZS) and Robust Satellite Techniques (RST)—were evaluated using the Atmospheric Infrared Sounder surface temperature data based on global M ≥ 6 earthquakes with focal depths of ≤70 km from 2006 to 2020. Retrospective correlation analyses reveal accuracy and missed detection rates of 80.33% & 19.67% and 80.93% & 19.67% for ZS and RST, respectively. For earthquake forecasting performance in seismically active regions, accuracy rates are within 0–1% and false alarm rates are up to 50–80%. Areas near earthquake-prone regions have the highest accuracy rates. The accuracy rates can be >10% within some regions in Japan and Indonesia. Overall temporal average Matthews correlation coefficients (MCC) range from −0.48 to 0.21; global spatial average MCCs for each day from 2006 to 2020 are between −0.1 and 0.1. After 2012, the ZS method yields higher MCC values than the RST method. Our results confirm the reliability of CESF for assessment of earthquake forecasting capability, and the possibility of forecasting earthquakes at earthquake-prone areas. This approach can be applied to long-term analyses of precursory parameters, anomaly detection methods, and hypotheses, all of which are essential to the ultimate goal of routine and consistent earthquake forecasting.