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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
  • 2023/12/27 18:49
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English name
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
CSTR
CSTR:11738.11.NCDC.ZENODO.DB4153.2023
DOI
10.5281/zenodo.7624605
Share type
Online
Category
Meteorology
source site
Zenodo
contributors
HE Tao , LIU Xinyan
Datasets description

 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.

Base information
collect time 2000/01/01  -  2020/12/31
collect place Arctic region
data size 18.1 MiB
data format nc
Coordinate system
Data thumbnail
Data source description

 The original datasets contain CF from MOD08/MYD08, CERES-SSF Terra/Aqua, CLARA-A2 AM/PM, PATMOS-x AM/PM, ISCCP-H AM/PM.

Data processing method

 A spatiotemporal statistical data fusion framework based on cumulative distribution function (CDF) matching and Bayesian maximum entropy (BME) method, generating Arctic 1 ° from 2000 to 2020 × 1 ° CF dataset.

Data quality description

 The data quality is good.

Citations and annotations
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For the achievements published in English, please refer to the following specifications： The dataset is provided by National Cryosphere Desert Data Center. (http://www.ncdc.ac.cn).

Data reference
  1. HE Tao, LIU Xinyan. 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. National Cryosphere Desert Data Center(http://www.ncdc.ac.cn), 2023. https://cstr.cn/CSTR:11738.11.NCDC.ZENODO.DB4153.2023.
  2. HE Tao, LIU Xinyan. 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. National Cryosphere Desert Data Center(http://www.ncdc.ac.cn), 2023. https://www.doi.org/10.5281/zenodo.7624605.
Papers to be cited
  1. Liu X, He T, Liang S, Li R, Xiao X, Ma R, Ma Y. A monthly 1\degree resolution dataset of daytime cloud fraction over the Arctic during 2000–2020 based on multiple satellite products[J]. Earth System Science Data, 2023, 15, (8): 3641-3671. DOI: 10.5194/essd-15-3641-2023.
    Reference format： Endnote Bibtex RIS
  2. Liu Xinyan, He Tao, Sun Lin, Xiao Xiongxin, Liang Shunlin, Li Siwei. Analysis of Daytime Cloud Fraction Spatio–Temporal Variation over the Arctic During 2000–2019 from Multiple Satellite Products[J]. Journal of Climate, 2022, .
    Reference format： Endnote Bibtex RIS
  3. Liu Xinyan, He Tao, Sun Lin, Xiao Xiongxin, Liang Shunlin, Li Siwei. Analysis of Daytime Cloud Fraction Spatio–Temporal Variation over the Arctic During 2000–2019 from Multiple Satellite Products[J]. Journal of Climate, 2022, .
    Reference format： Endnote Bibtex RIS
license agreement
Creative Commons License   This work is licensed under a Creative Commons Attribution 4.0 International License.
# title file size
1 _ncdc_meta_.json 5.5 KiB
2 基于累积分布函数匹配和贝叶斯最大熵的多个卫星产品的北极地区长期月度云分数数据集（2000-2020年）.zip 18.1 MiB
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How to get the data 
east 179.50
west -167.50
south  59.50
north  89.50
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Service information
contacts:
Minyufang
phone:
0931-4967596
mailbox:
ncdc@lzb.ac.cn
Contact information
contributors:
HE Tao , LIU Xinyan
author:
HE Tao
manager:
HE Tao

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