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Preprints
https://doi.org/10.5194/essd-2020-19
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-2020-19
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: data description paper 06 Apr 2020

Submitted as: data description paper | 06 Apr 2020

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This preprint is currently under review for the journal ESSD.

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

Dalei Hao1,2,3, Ghassem R. Asrar4, Yelu Zeng1, Qing Zhu5, Jianguang Wen2,3, Qing Xiao2,3, and Min Chen1 Dalei Hao et al.
  • 1Joint Global Change Research Institute, Pacific Northwest National Laboratory, College Park, MD 20740, USA
  • 2State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
  • 3University of Chinese Academy of Sciences, Beijing 100049, China
  • 4Universities Space Research Association, Columbia, MD 21046, USA
  • 5Earth Science Division, Lawrence Berkeley National Lab, Berkeley, CA 94720, USA

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 spatio-temporally-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 multi-source data from complementary satellites/sensors is challenging because of co-registration, inter-calibration, near real-time data delivery and the effects of discrepancies in orbital geometry. The Earth Polychromatic Imaging Camera (EPIC) onboard 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 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 further analyzed and compared the spatio-temporal 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/m2 and root mean square errors (RMSEs) of 103.50 and 35.40 W/m2, respectively). The developed products capture the complex spatio-temporal patterns well and accurately track substantial diurnal, monthly, and seasonal variations of 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).

Dalei Hao et al.

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DSCOVR/EPIC-derived global hourly/daily downward shortwave and photosynthetically active radiation data at 0.1°×0.1° resolution D. Hao, G. R. Asrar, Y. Zeng, Q. Zhu, J. Wen, Q. Xiao, and M. Chen https://doi.org/10.25584/1595069

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Short summary
We adopted machine-learning models to generate the first global land products of SW/PAR based on DSCOVR/EPIC data. Our products are consistent with ground-based observations, capture the spatio-temporal patterns well, and accurately track substantial diurnal, monthly, and seasonal variations of SW/PAR, Our products provide a valuable alternative for solar photovoltaic applications and can be used to improve our understanding of the diurnal cycles of terrestrial water, carbon and energy fluxes.
We adopted machine-learning models to generate the first global land products of SW/PAR based on...
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