DSCOVR/EPIC-derived global hourly and daily downward shortwave and photosynthetically active radiation data at 0.1° × 0.1° resolution

Hao, Dalei; Asrar, Ghassem R.; Zeng, Yelu; Zhu, Qing; Wen, Jianguang; Xiao, Qing; Chen, Min

doi:10.5194/essd-12-2209-2020

DSCOVR/EPIC-derived global hourly and daily downward shortwave and photosynthetically active radiation data at 0.1° × 0.1° resolution

Journal Article · Tue Sep 15 00:00:00 EDT 2020 · Earth System Science Data (Online)

DOI:https://doi.org/10.5194/essd-12-2209-2020· OSTI ID:1671740

Hao, Dalei ^[1]; Asrar, Ghassem R. ^[2]; Zeng, Yelu ^[3]; Zhu, Qing ^[4]; Wen, Jianguang ^[5]; Xiao, Qing ^[5]; Chen, Min ^[3]

Pacific Northwest National Lab. (PNNL), College Park, MD (United States). Joint Global Change Research Inst.; Chinese Academy of Sciences (CAS), Beijing (China). State Key Lab. of Remote Sensing Science, Aerospace Information Research Inst.
Universities Space Research Association, Columbia, MD (United States)
Pacific Northwest National Lab. (PNNL), College Park, MD (United States). Joint Global Change Research Inst.
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Chinese Academy of Sciences (CAS), Beijing (China). State Key Lab. of Remote Sensing Science, Aerospace Information Research Inst.

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.

View Accepted Manuscript (DOE)

Research Organization:: ARM Data Center, Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States); Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Biological and Environmental Research (BER)

Contributing Organization:: PNNL, BNL, ANL, ORNL

Grant/Contract Number:: AC05-76RL01830

OSTI ID:: 1671740

Alternate ID(s):: OSTI ID: 1699981

Journal Information:: Earth System Science Data (Online), Journal Name: Earth System Science Data (Online) Journal Issue: 3 Vol. 12; ISSN 1866-3516

Publisher:: CopernicusCopyright Statement

Country of Publication:: United States

Language:: English

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