Abstract
Solar radiation is a critical variable in global change sciences. While most of the current global datasets provide only the total downward solar radiation, we aim to develop a method to estimate the downward global land surface solar radiation and its partitioned direct and diffuse components, which provide the necessary key meteorological inputs for most land surface models. We developed a simple satellite-based computing scheme to enable fast and reliable estimation of these variables. The global Moderate Resolution Imaging Spectroradiometer (MODIS) products at 1° spatial resolution for the period 2003–2011 were used as the forcing data. Evaluations at Baseline Surface Radiation Network (BSRN) sites show good agreement between the estimated radiation and ground-based observations. At all the 48 BSRN sites, the RMSE between the observations and estimations are 34.59, 41.98 and 28.06 W∙m−2 for total, direct and diffuse solar radiation, respectively. Our estimations tend to slightly overestimate the total and diffuse but underestimate the direct solar radiation. The errors may be related to the simple model structure and error of the input data. Our estimation is also comparable to the Clouds and Earth’s Radiant Energy System (CERES) data while shows notable improvement over the widely used National Centers for Environmental Prediction and National Center for Atmospheric Research (NCEP/NCAR) Reanalysis data. Using our MODIS-based datasets of total solar radiation and its partitioned components to drive land surface models should improve simulations of global dynamics of water, carbon and climate.
Highlights
Solar radiation (E) is the principal energy source that drives all land surface processes such as the hydrological cycle, heat transfer and greenhouse gas exchange
A number of global datasets of solar radiation based on various remotely sensed data sources and algorithms have been available at different spatio-temporal resolutions such as Earth Radiation Budget Experiment (ERBE) [8], International Satellite Cloud Climatology Project based on the NASA Goddard Institute for Space Studies radiative transfer model (ISCCP/GISS) [9], University of Maryland/Moderate Resolution Imaging Spectroradiometer (MODIS)
In comparison with other approaches, the satellite-based global solar radiation datasets shall provide better estimation of downward solar radiation on land surface by using direct measurements of atmospheric profiles, and especially the incorporation of more adequate information of cloud properties [7]. Benefiting from their remarkable accuracy, the satellite-based global solar radiation datasets have been frequently used in the studies on global land surface energy budget. These datasets have not been widely applied for land surface modeling, mainly due to the limited temporal coverage of satellite-based data, but another possible reason is that these datasets often only provide the total solar radiation (Eg) without the direct and diffuse components (Eb and Ed, respectively), with some exceptions such as ClimateMonitoring-Satellite Application Facility (CM-SAF), Solar radiation Data (SoDa) [17], Satel-light and Global Land Surface Satellite (GLASS)
Summary
Solar radiation (E) is the principal energy source that drives all land surface processes such as the hydrological cycle, heat transfer and greenhouse gas exchange. In comparison with other approaches, the satellite-based global solar radiation datasets shall provide better estimation of downward solar radiation on land surface by using direct measurements of atmospheric profiles, and especially the incorporation of more adequate information of cloud properties [7]. Benefiting from their remarkable accuracy, the satellite-based global solar radiation datasets have been frequently used in the studies on global land surface energy budget. Growing number of studies support that these two components of solar radiation are significantly important to land surface processes and can potentially influence the greenhouse gas exchange between terrestrial ecosystems and the atmosphere [18,19]
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