Estimating global downward shortwave radiation from VIIRS data using a transfer-learning neural network

Abstract

In recent years, machine learning (ML) has been successfully used in estimating downward shortwave radiation (DSR). To achieve global estimations, traditional ML models need sufficient ground measurements covering various atmospheric and surface conditions globally, which is difficult to accomplish. Training on the simulated data of a radiative transfer model (RTM) is a possible solution, but widely used RTMs ignore some complex cloud conditions which brings bias to simulations. In this study, a neural network applied with the transfer-learning (TL) concept is introduced to utilize both radiative transfer simulations and ground measurement data, achieving global DSR estimation with only top-of-atmosphere and surface albedo at local solar noon as inputs. The proposed method estimates both instantaneous and daily DSR from Visible Infrared Imaging Radiometer Suite (VIIRS) data at 750-m resolution, and both the estimates are validated by 40 independent stations globally. The root mean-square error and relative root mean square error of instantaneous DSR validation over 25 Baseline Surface Radiation Network, seven Surface Radiation Network, and eight Greenland Climate Network stations in 2013 were 91.2 (16.1%), 106.3 (18.3%), 75.0 (24.2%) W/m2, respectively, and the daily validation achieved 30.8 (15.5%), 33.5 (17.6%), and 31.3 (14.4) W/m2, respectively. The proposed method presents significant high accuracy over polar regions and similar performances over other areas compared with traditional ML models, physics models (e.g., look-up tables and direct estimations), and existing DSR products. The algorithm is also applied to VIIRS swath data to test its global efficacy. Instantaneous mapping captures the spatial pattern of the cloud-mask product, and daily mapping shows spatial patterns similar to the Clouds and the Earth's Radiant Energy System Synoptic TOA and surface fluxes and clouds product, but with more detail. Further analysis indicates that model performance is less sensitive to the quantity of training data after TL has been incorporated. This study demonstrates the advantages of TL on boosting both the generality and accuracy of DSR estimation, which can potentially be applied to other variable retrievals.