Estimation of Daily All-Wave Surface Net Radiation with Multispectral and Multitemporal Observations from GOES-16 ABI

Abstract

As a vital parameter describing the Earth surface energy budget, surface all-wave net radiation (R n) drives many physical and biological processes. Remote estimation ofR n using satellite data is an effective approach to monitor the spatial and temporal dynamics of Rn. Accurate dailyR n estimation typically depends on the spatiooral resolutions of satellite data. There are currently few high-spatial-resolution dailyR n products from polar-orbiting satellite data, and they exhibit limited accuracy due to sparse diurnal observations. In addition, traditional estimation approaches typically require cloud mask and clear-sky albedo as inputs and ignore the length ratio of daytime (LRD), which may lead to large errors. To overcome these challenges and obtainR n data with improved spatial resolution and accuracy, an operational approach was proposed in this study to derive daily 1-kmR n, which takes the advantages from a radiative transfer model, a machine learning algorithm, and multispectral and dense diurnal temporal information of geostationary satellite observations. An improved all-sky hybrid model (AHM) coupling radiative transfer simulations with a random forest (RF) model was first developed to estimate the shortwave net radiation (R ns). Then, another RF model was developed to estimate the dailyR n from Rns, incorporating the LRD, which is called extended hybrid model (EHM). Data from the Advanced Baseline Imager (ABI) onboard the new-generation Geostationary Operational Environmental Satellite (GOES)-16 with a 5-min temporal resolution and a 1-km spatial resolution were used to test the proposed method. Compared to traditional lookup table (LUT) algorithms, the results show that AHM not only makes the process of Rns estimation simple and efficient but also has high accuracy in estimating instantaneous all-sky Rns. Benefiting from high spatiooral resolutions, our daily Rns estimates using GOSE-16 data exhibited superior performance compared to using the 1-km Moderate Resolution Imaging Spectroradiometer (MODIS) and 1° Clouds and the Earth's Radiant Energy System (CERES) product. Using accurate daily Rns estimates and LRD as inputs, the EHM model shows reasonably good results for estimatingR n (R2, RMSE, and bias of 0.91, 20.95 W/m2, and-0.05 W/m2, respectively). Maps of 1-km Rns andR n exhibit similar spatial patterns to those from the 1° CERES product, but with substantially more spatial details. Overall, the proposedR n retrieval scheme can accurately estimate all-sky 1-km Rns andR n at mid-to low-latitudes and can be easily adapted and applied to other GOES-16-like satellites, such as Himawari-8, Meteosat Third Generation (MTG), and Fenyun-4. This study demonstrates the advantages of estimatingR n using geostationary satellites with improved accuracy and resolutions.