Abstract. Downward shortwave radiation (SW) and photosynthetically
active radiation (PAR) play crucial roles in Earth system dynamics.
Spaceborne remote sensing techniques provide a unique means for mapping
accurate spatiotemporally continuous SW–PAR, globally. However, any
individual polar-orbiting or geostationary satellite cannot satisfy the
desired high temporal resolution (sub-daily) and global coverage
simultaneously, while integrating and fusing multisource data from
complementary satellites/sensors is challenging because of co-registration,
intercalibration, near real-time data delivery and the effects of
discrepancies in orbital geometry. The Earth Polychromatic Imaging Camera
(EPIC) on board the Deep Space Climate Observatory (DSCOVR), launched in
February 2015, offers an unprecedented possibility to bridge the gap between
high temporal resolution and global coverage and characterize the diurnal
cycles of SW–PAR globally. In this study, we adopted a suite of
well-validated data-driven machine-learning models to generate the first
global land products of SW–PAR, from June 2015 to June 2019, based on
DSCOVR/EPIC data. The derived products have high temporal resolution
(hourly) and medium spatial resolution (0.1∘×0.1∘), and they include estimates of the direct and diffuse components
of SW–PAR. We used independently widely distributed ground station data from
the Baseline Surface Radiation Network (BSRN), the Surface Radiation Budget
Network (SURFRAD), NOAA's Global Monitoring Division and the U.S. Department
of Energy's Atmospheric System Research (ASR) program to evaluate the
performance of our products, and we further analyzed and compared the
spatiotemporal characteristics of the derived products with the
benchmarking Clouds and the Earth's Radiant Energy System Synoptic (CERES)
data. We found both the hourly and daily products to be consistent with
ground-based observations (e.g., hourly and daily total SWs have low biases
of −3.96 and −0.71 W m−2 and root-mean-square errors (RMSEs) of 103.50
and 35.40 W m−2, respectively). The developed products capture the
complex spatiotemporal patterns well and accurately track substantial
diurnal, monthly, and seasonal variations in SW–PAR when compared to CERES
data. They provide a reliable and valuable alternative for solar
photovoltaic applications worldwide and can be used to improve our
understanding of the diurnal and seasonal variabilities of the terrestrial
water, carbon and energy fluxes at various spatial scales. The products are
freely available at https://doi.org/10.25584/1595069
(Hao et al., 2020).
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