{
    "created": "2023-12-25 10:51:56",
    "updated": "2026-04-24 21:15:14",
    "id": "0275c956-029e-4ffd-8c5c-b17691250efe",
    "version": 13,
    "ds_topic": null,
    "title_cn": "基于多源卫星云产品融合算法的北极地区长时序月云覆盖度数据集（2000-2020年）",
    "title_en": "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",
    "ds_abstract": "<p>&emsp;&emsp;北极地区低精度卫星云分数(CF)，严重制约了气候变化下区域和全球辐射能平衡的准确评估。以往的研究报告表明，没有一种卫星CF产品能够同时满足北极长期应用的精度和时空覆盖需求，因此合并多个特性互补的CF产品是产生更时空完整、准确的CF数据记录的有效途径。本数据集提出了一种基于累积分布函数(CDF)匹配和贝叶斯最大熵(BME)方法的时空统计数据融合框架，生成2000-2020年北极1°×1°CF数据集。",
    "ds_source": "<p>&emsp;&emsp;原始数据集包含CF来自MOD08/MYD08, CERES-SSF Terra/Aqua, CLARA-A2 AM/PM, PATMOS-x AM/PM, ISCCP-H AM/PM。",
    "ds_process_way": "<p>&emsp;&emsp;基于累积分布函数(CDF)匹配和贝叶斯最大熵(BME)方法的时空统计数据融合框架，生成2000-2020年北极1°×1°CF数据集。",
    "ds_quality": "<p>&emsp;&emsp;针对目前北极地区云覆盖度的不确定性大、基于单一卫星数据的云覆盖度精度低、时空覆盖不连续的问题，我们提出一种时空扩展的累积分布函数匹配和贝叶斯最大熵结合的多源卫星云覆盖度数据融合算法，生产了2000-2020年北极地区1°×1°时空完整的高精度月平均云覆盖度产品。该算法不仅考虑了数据的时空自相关性，而且考虑了主/被动传感器产品之间及卫星与地面观测值之间的不确定性。该融合数据集与主动卫星数据、地面观测数据之间的不一致性下降了10%-20%左右，尤其改善了常年冰雪区域的云覆盖度低估现象。此外，该数据集时空覆盖更完整，且与再分析和模型数据的一致性更好。",
    "ds_acq_start_time": "2000-01-01 00:00:00",
    "ds_acq_end_time": "2020-12-31 00:00:00",
    "ds_acq_place": "北极地区",
    "ds_acq_lon_east": 179.5,
    "ds_acq_lat_south": 59.5,
    "ds_acq_lon_west": -166.5,
    "ds_acq_lat_north": 89.5,
    "ds_acq_alt_low": null,
    "ds_acq_alt_high": null,
    "ds_share_type": "login-access",
    "ds_total_size": 18972040,
    "ds_files_count": 2,
    "ds_format": "nc",
    "ds_space_res": "30m",
    "ds_time_res": "月",
    "ds_coordinate": "无",
    "ds_projection": "",
    "ds_thumbnail": "0275c956-029e-4ffd-8c5c-b17691250efe.jpg",
    "ds_thumb_from": 2,
    "ds_ref_way": "",
    "paper_ref_way": "",
    "ds_ref_instruction": "",
    "ds_from_station": null,
    "organization_id": "0a4269e1-65f4-45f1-aeba-88ea3068eebf",
    "ds_serv_man": "敏玉芳",
    "ds_serv_phone": "0931-4967596",
    "ds_serv_mail": "ncdc@lzb.ac.cn",
    "doi_value": "",
    "subject_codes": [
        "170.15"
    ],
    "quality_level": 3,
    "publish_time": "2023-12-27 10:49:23",
    "last_updated": "2026-01-14 11:12:26",
    "protected": false,
    "protected_to": null,
    "lang": "zh",
    "cstr": "11738.11.NCDC.ZENODO.DB4153.2023",
    "i18n": {
        "en": {
            "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",
            "ds_format": "nc",
            "ds_source": "<p>&emsp;The original datasets contain CF from MOD08/MYD08, CERES-SSF Terra/Aqua, CLARA-A2 AM/PM, PATMOS-x AM/PM, ISCCP-H AM/PM.",
            "ds_quality": "<p>&emsp;The data quality is good.",
            "ds_ref_way": "",
            "ds_abstract": "<p> 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.</p>",
            "ds_time_res": "月",
            "ds_acq_place": "Arctic region",
            "ds_space_res": "30m",
            "ds_projection": "",
            "ds_process_way": "<p>&emsp;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.",
            "ds_ref_instruction": ""
        }
    },
    "submit_center_id": "ncdc",
    "data_level": 0,
    "license_type": "CC BY 4.0",
    "doi_reg_from": "reg_outside",
    "cstr_reg_from": "reg_outside",
    "doi_not_reg_reason": null,
    "cstr_not_reg_reason": null,
    "ds_topic_tags": [
        "北极",
        "卫星云分数",
        "贝叶斯最大熵",
        "累积分布函数"
    ],
    "ds_subject_tags": [
        "大气科学"
    ],
    "ds_class_tags": [],
    "ds_locus_tags": [
        "北极地区"
    ],
    "ds_time_tags": [
        2000,
        2001,
        2002,
        2003,
        2004,
        2005,
        2006,
        2007,
        2008,
        2009,
        2010,
        2011,
        2012,
        2013,
        2014,
        2015,
        2016,
        2017,
        2018,
        2019,
        2020
    ],
    "ds_contributors": [
        {
            "true_name": "何涛",
            "email": "taohers@whu.edu.cn",
            "work_for": "武汉大学",
            "country": "中国"
        },
        {
            "true_name": "刘心燕",
            "email": "lxy_rs@hnas.ac.cn",
            "work_for": "河南省科学院空天信息研究所",
            "country": "中国"
        }
    ],
    "ds_meta_authors": [
        {
            "true_name": "何涛",
            "email": "taohers@whu.edu.cn",
            "work_for": "武汉大学",
            "country": "中国"
        }
    ],
    "ds_managers": [
        {
            "true_name": "何涛",
            "email": "taohers@whu.edu.cn",
            "work_for": "武汉大学",
            "country": "中国"
        }
    ],
    "category": "气象"
}