Global cloud property models for real-time triage on board visible–shortwave infrared spectrometers
<p>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 t...
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doaj-a358f3cafc0f42168fbd562af4e48f3a2020-12-22T05:30:15ZengCopernicus PublicationsAtmospheric Measurement Techniques1867-13811867-85482020-12-01137047705710.5194/amt-13-7047-2020Global cloud property models for real-time triage on board visible–shortwave infrared spectrometersM. W. Sandford0D. R. Thompson1R. O. Green2B. H. Kahn3R. Vitulli4S. Chien5A. Yelamanchili6W. Olson-Duvall7Institute of Geophysics and Planetology, Department of Earth Sciences, University of Hawai'i at Manoa, Hawai'i, Honolulu, HI, USAJet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USAJet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USAJet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USAEuropean Space Research and Technology Center, European Space Agency, Noordwijk, the NetherlandsJet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USAJet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USAJet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA<p>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 on board, 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 on board orbital imaging spectrometers, effectively doubling the volume of usable science data per downlink. Models based on this dataset will be used to screen clouds on board NASA's forthcoming mission, the Earth Mineral Dust Source Investigation (EMIT).</p>https://amt.copernicus.org/articles/13/7047/2020/amt-13-7047-2020.pdf |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
M. W. Sandford D. R. Thompson R. O. Green B. H. Kahn R. Vitulli S. Chien A. Yelamanchili W. Olson-Duvall |
spellingShingle |
M. W. Sandford D. R. Thompson R. O. Green B. H. Kahn R. Vitulli S. Chien A. Yelamanchili W. Olson-Duvall Global cloud property models for real-time triage on board visible–shortwave infrared spectrometers Atmospheric Measurement Techniques |
author_facet |
M. W. Sandford D. R. Thompson R. O. Green B. H. Kahn R. Vitulli S. Chien A. Yelamanchili W. Olson-Duvall |
author_sort |
M. W. Sandford |
title |
Global cloud property models for real-time triage on board visible–shortwave infrared spectrometers |
title_short |
Global cloud property models for real-time triage on board visible–shortwave infrared spectrometers |
title_full |
Global cloud property models for real-time triage on board visible–shortwave infrared spectrometers |
title_fullStr |
Global cloud property models for real-time triage on board visible–shortwave infrared spectrometers |
title_full_unstemmed |
Global cloud property models for real-time triage on board visible–shortwave infrared spectrometers |
title_sort |
global cloud property models for real-time triage on board visible–shortwave infrared spectrometers |
publisher |
Copernicus Publications |
series |
Atmospheric Measurement Techniques |
issn |
1867-1381 1867-8548 |
publishDate |
2020-12-01 |
description |
<p>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 on board, 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 on board orbital imaging spectrometers,
effectively doubling the volume of usable science data per downlink. Models
based on this dataset will be used to screen clouds on board NASA's
forthcoming mission, the Earth Mineral Dust Source Investigation (EMIT).</p> |
url |
https://amt.copernicus.org/articles/13/7047/2020/amt-13-7047-2020.pdf |
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