Merging Satellite Retrievals and Reanalyses to Produce Global Long-Term and Consistent Surface Incident Solar Radiation Datasets

Fei Feng,Kaicun Wang

doi:10.3390/rs10010115

Abstract

Surface incident solar radiation (Rs) is a key parameter in many climatic and ecological processes. The data from satellites and reanalysis have been widely used. However, for reanalysis, Rs data has been shown to have substantial spatial bias, and the time span of reliable satellite Rs is too short for climatic and ecological studies. Combining reanalysis and satellite data would be an effective method for generating long-term and consistent Rs datasets. Here, we apply a cumulative probability density function-based (CPDF) method to merge eight reanalyses with the latest available satellite Rs data from Clouds and Earth’s Radiant Energy System Energy Balanced and Filled (CERES EBAF) surface retrievals. The CPDF method not only reduces the spatial bias of the reanalysis Rs data, but also makes the Rs datasets in a global, long-term and consistent way. The observed Rs data collected at 54 Baseline Surface Radiation Network (BSRN) stations from 1992 to 2016 are used to evaluate the method. Results show that the CPDF method could reduce the mean absolute biases (MAB) of the reanalysis Rs effectively by 21.24–64.36%. The European Centre for Medium-Range Weather Forecasts Re-Analysis interim (ERA-interim) reanalysis Rs data, which are available for 1979 onward, perform the best before MAB = 13.20 W·m−2 and after MAB = 10.40 W·m−2 merging. This small post-merging MAB of the ERA-interim reanalysis is caused by the MAB of 9.90 W·m−2 in the satellite Rs retrievals. The Japanese 55-year reanalysis provides Rs values back to 1958, and CPDF can reduce its MAB by 32.87%, to 11.17 W·m−2. The National Oceanic and Atmospheric Administration (NOAA)-CIRES twentieth-century reanalysis (CIRES) and the ECMWF twentieth-century reanalysis (ERA20CM) provide century-long Rs estimates. CIRES performs better after merging. The MAB of CIRES can be reduced by 32.10%, to 12.99 W·m−2, while ERA20CM’s can be reduced by 12.51%, to 16.40 W·m−2.

Highlights

Surface Incident solar radiation (Rs), which is often referred to as the downward solar irradiance, is a key parameter in many climate and ecological processes, such as evapotranspiration [1,2,3], canopy photosynthesis [4], net primary production [5,6], crop growth management [7], and so on
Substantial biases in the reanalysis Rs data were found at most marine strato-cumulus regions, off the western coasts of the continents, which is likely due to bias in the cloud properties from the reanalysis systems
We calculated the mean absolute bias (MAB) of the ratio of each seasonal Rs mean to annual mean Rs (Table 3), using Baseline Surface Radiation Network (BSRN) as a reference; the results showed that the merged reanalyses have lower mean absolute biases (MAB) of the ratio of each seasonal mean Rs to annual mean Rs, and indicated that cumulative probability density function-based (CPDF) can mostly reduce relative bias of seasonal mean Rs to annual mean Rs, except for the ERA20CM, whose MAB remained the same

Summary

Introduction

Surface Incident solar radiation (Rs), which is often referred to as the downward solar irradiance, is a key parameter in many climate and ecological processes, such as evapotranspiration [1,2,3], canopy photosynthesis [4], net primary production [5,6], crop growth management [7], and so on. Several long-term observation networks were developed under the high-quality control. The Baseline Surface Radiation Network (BSRN) [14] which provides high-quality Rs observations, has been widely used in the assessment of Rs variation at local sites [15] and the evaluation of Rs derived by satellite data and reanalyses [16,17]. The Surface Radiation Budget Network (SURFRAD), the U.S component of BSRN, was developed by the National Oceanic and Atmospheric Administration (NOAA) to provide accurate, continuous, and long-term Rs data for the United States [18]. There are studies [19,20,21,22,23,24,25] suggesting that sunshine duration records can be used to reconstruct long-term Rs with reliable accuracy Both ground-based observations and sunshine duration records are spatially sparse

Methods

Results

Conclusion