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.
| collect time | 2000/01/01 - 2020/12/31 |
|---|---|
| collect place | Arctic region |
| data size | 18.1 MiB |
| data format | nc |
| Coordinate system |
The original datasets contain CF from MOD08/MYD08, CERES-SSF Terra/Aqua, CLARA-A2 AM/PM, PATMOS-x AM/PM, ISCCP-H AM/PM.
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.
The data quality is good.
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 |
Arctic satellite cloud fraction Bayesian maximum entropy cumulative distribution function
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
©Copyright 2005-. Northwest Institute of Eco-Environment and Resources, CAS.
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