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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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Preprints
https://doi.org/10.5194/amt-2020-139
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-2020-139
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 06 May 2020

Submitted as: research article | 06 May 2020

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This preprint is currently under review for the journal AMT.

Global Cloud Property Models for Real Time Triage Onboard Visible-Shortwave Infrared Spectrometers

Macey W. Sandford1, David R. Thompson2, Robert O. Green2, Brian H. Kahn2, Raffaele Vitulli3, Steve Chien2, Amruta Yelamanchili2, and Winston Olson-Duvall2 Macey W. Sandford et al.
  • 1University of Hawai’i at Manoa, Hawai’i Institute of Geophysics and Planetology, Department of Earth Sciences, Honolulu, HI, USA
  • 2Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
  • 3European Space Agency, European Space Research and Technology Center, Noordwijk, the Netherlands

Abstract. New methods for optimizing data storage and transmission are required as orbital imaging spectrometers collect ever-larger data volumes due to increases in optical efficiency and resolution. In Earth surface investigations, storage and downlink volumes are the most important bottleneck in the mission’s total data yield. Excising cloud-contaminated data onboard, during acquisition, can increase the value of downlinked data and significantly improve the overall science performance of the mission. Threshold-based screening algorithms can operate at the acquisition rate of the instrument but require accurate and comprehensive predictions of cloud and surface brightness. To date, the community lacks a comprehensive analysis of global data to provide appropriate thresholds for screening clouds or to predict performance. Moreover, prior cloud screening studies have used universal screening criteria that do not account for the unique surface and cloud properties at different locations. To address this gap, we analyzed the Hyperion imaging spectrometer’s historical archive of global Earth reflectance data. We selected a diverse subset spanning space (with tropical, midlatitude, arctic, and Antarctic latitudes), time (2005–2017), and wavelength (400–2500 nm) to assure that the distributions of cloud data are representative of all cases. We fit models of cloud reflectance properties gathered from the subset to predict locally and globally applicable thresholds. The distributions relate cloud reflectance properties to various surface types (land, water, and snow) and latitudinal zones. We find that taking location into account can significantly improve the efficiency of onboard cloud screening methods. Models based on this dataset will be used to screen clouds onboard orbital imaging spectrometers, effectively doubling the volume of usable science data per downlink. Models based on this dataset will be used to screen clouds onboard NASA's forthcoming mission, the Earth Mineral Dust Source InvesTigation (EMIT).

Macey W. Sandford et al.

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Macey W. Sandford et al.

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Latest update: 03 Jun 2020
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Short summary
We demonstrate an onboard cloud screening approach to significantly reduce the amount of cloud-contaminated data transmitted from orbit. We have produced location-specific models that improve performance by taking into account the unique cloud statistics in different latitudes. We have shown that screening clouds based on their location or surface type will improve the ability for a cloud-screening tool to improve the volume of usable science data.
We demonstrate an onboard cloud screening approach to significantly reduce the amount of...
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