| 摘 要： |
Top-of-atmosphere (TOA) outgoing longwave radiation (OLR), a key component of the Earth's energy budget, serves as a diagnostic of the Earth's climate system response to incoming solar radiation. However, existing products are typically estimated using the traditional two-step method, which may bring extra uncertainties. This paper presents a direct machine learning method to estimate TOA OLR by directly linking Himawari-8/Advanced Himawari Imager (AHI) TOA radiances with TOA OLR determined by Clouds and the Earth's Radiant Energy System (CERES) and other information, such as the viewing geometry. Models are built separately under clear- and cloudy-sky conditions using a gradient-boosting regression tree. Independent test results show that the root mean square errors (RMSEs) of the clear-sky and cloudy-sky models for estimating instantaneous values are 7.46 W/m(2) (3.0%) and 11.61 W/m(2) (5.8%), respectively. Daily results are obtained by averaging all the instantaneous results in one day. Intercomparisons of the daily results with CERES TOA OLR data show that the RMSE of the estimated AHI OLR is similar to 6 W/m(2) (3%). The developed high-resolution AHI TOA OLR dataset will be beneficial in analyzing the regional energy budget. |
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