A long-term monthly dataset of cloud fraction over the Arctic based on Multiple Satellite products Using Cumulative Distribution Function Matching and Bayesian Maximum Entropy
The low accuracy of satellite Cloud fraction (CF) over the Arctic seriously restricts accurate assessment of regional and global radiant energy balance under the changing climate. Previous studies have reported that not a single satellite CF product could satisfy the needs of accuracy and spatio-tem...
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ftzenodo:oai:zenodo.org:7478919 2024-09-09T19:19:21+00:00 A long-term monthly dataset of cloud fraction over the Arctic based on Multiple Satellite products Using Cumulative Distribution Function Matching and Bayesian Maximum Entropy Liu Xinyan He Tao 2022-12-24 https://doi.org/10.5281/zenodo.7478919 unknown Zenodo https://doi.org/10.5281/zenodo.7478918 https://doi.org/10.5281/zenodo.7478919 oai:zenodo.org:7478919 info:eu-repo/semantics/openAccess Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode info:eu-repo/semantics/other 2022 ftzenodo https://doi.org/10.5281/zenodo.747891910.5281/zenodo.7478918 2024-07-26T20:10:39Z The low accuracy of satellite Cloud fraction (CF) over the Arctic seriously restricts accurate assessment of regional and global radiant energy balance under the changing climate. Previous studies have reported that not a single satellite CF product could satisfy the needs of accuracy and spatio-temporal coverage simultaneously for long-term applications over the Arctic. Merging multiple CF products with complementary properties is an effective way to produce more spatiotemporally complete and accurate CF data record. This study proposed a spatiotemporal statistical data fusion framework based on cumulative distribution function (CDF) matching and Bayesian maximum entropy (BME) method to produce a syncretic 1°×1° CF dataset in the Arctic during 2000-2020. The original datasets contain CF from MOD08/MYD08, CERES-SSF Terra/Aqua, CLARA-A2 AM/PM, PATMOS-x AM/PM, ISCCP-H AM/PM. The fused CF productis more consistent with the active satellite data GEWEX-CALIPSO and the ground-based observation data CRU TS4.05. Other/Unknown Material Arctic Zenodo Arctic |
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The low accuracy of satellite Cloud fraction (CF) over the Arctic seriously restricts accurate assessment of regional and global radiant energy balance under the changing climate. Previous studies have reported that not a single satellite CF product could satisfy the needs of accuracy and spatio-temporal coverage simultaneously for long-term applications over the Arctic. Merging multiple CF products with complementary properties is an effective way to produce more spatiotemporally complete and accurate CF data record. This study proposed a spatiotemporal statistical data fusion framework based on cumulative distribution function (CDF) matching and Bayesian maximum entropy (BME) method to produce a syncretic 1°×1° CF dataset in the Arctic during 2000-2020. The original datasets contain CF from MOD08/MYD08, CERES-SSF Terra/Aqua, CLARA-A2 AM/PM, PATMOS-x AM/PM, ISCCP-H AM/PM. The fused CF productis more consistent with the active satellite data GEWEX-CALIPSO and the ground-based observation data CRU TS4.05. |
format |
Other/Unknown Material |
author |
Liu Xinyan He Tao |
spellingShingle |
Liu Xinyan He Tao A long-term monthly dataset of cloud fraction over the Arctic based on Multiple Satellite products Using Cumulative Distribution Function Matching and Bayesian Maximum Entropy |
author_facet |
Liu Xinyan He Tao |
author_sort |
Liu Xinyan |
title |
A long-term monthly dataset of cloud fraction over the Arctic based on Multiple Satellite products Using Cumulative Distribution Function Matching and Bayesian Maximum Entropy |
title_short |
A long-term monthly dataset of cloud fraction over the Arctic based on Multiple Satellite products Using Cumulative Distribution Function Matching and Bayesian Maximum Entropy |
title_full |
A long-term monthly dataset of cloud fraction over the Arctic based on Multiple Satellite products Using Cumulative Distribution Function Matching and Bayesian Maximum Entropy |
title_fullStr |
A long-term monthly dataset of cloud fraction over the Arctic based on Multiple Satellite products Using Cumulative Distribution Function Matching and Bayesian Maximum Entropy |
title_full_unstemmed |
A long-term monthly dataset of cloud fraction over the Arctic based on Multiple Satellite products Using Cumulative Distribution Function Matching and Bayesian Maximum Entropy |
title_sort |
long-term monthly dataset of cloud fraction over the arctic based on multiple satellite products using cumulative distribution function matching and bayesian maximum entropy |
publisher |
Zenodo |
publishDate |
2022 |
url |
https://doi.org/10.5281/zenodo.7478919 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic |
genre_facet |
Arctic |
op_relation |
https://doi.org/10.5281/zenodo.7478918 https://doi.org/10.5281/zenodo.7478919 oai:zenodo.org:7478919 |
op_rights |
info:eu-repo/semantics/openAccess Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode |
op_doi |
https://doi.org/10.5281/zenodo.747891910.5281/zenodo.7478918 |
_version_ |
1809759462988185600 |