{
    "created": "2023-08-18 17:38:32",
    "updated": "2026-05-09 13:21:07",
    "id": "33887bb4-de19-44ec-8f7c-6415392a3268",
    "version": 13,
    "ds_topic": null,
    "title_cn": "中国四川盆地每日RF AOD数据集（2015-2020年）",
    "title_en": "Daily RF AOD dataset in the Sichuan Basin, China (2015-2020)",
    "ds_abstract": "<p>&emsp;&emsp;采用随机森林（RF）机器学习方法和多个数据集建立四川盆地多云的气溶胶光学深度（AOD）数据集。多个数据集包括地面PM10和PM2.5，来自太阳天空辐射计观测网络（SONET）和第二次现代研究和应用回顾性分析（MERRA-2）气溶胶再分析的AOD，以及几个气象变量。",
    "ds_source": "<p>&emsp;&emsp;使用机器学习方法模拟。",
    "ds_process_way": "<p>&emsp;&emsp;采用随机森林（RF）机器学习方法和多个数据集建立四川盆地多云的气溶胶光学深度（AOD）数据集。",
    "ds_quality": "<p>&emsp;&emsp;数据质量较好。",
    "ds_acq_start_time": "2015-01-01 00:00:00",
    "ds_acq_end_time": "2020-12-31 00:00:00",
    "ds_acq_place": "四川盆地",
    "ds_acq_lon_east": 108.0,
    "ds_acq_lat_south": 28.0,
    "ds_acq_lon_west": 103.0,
    "ds_acq_lat_north": 32.0,
    "ds_acq_alt_low": null,
    "ds_acq_alt_high": null,
    "ds_share_type": "login-access",
    "ds_total_size": 2269785,
    "ds_files_count": 2,
    "ds_format": "txt",
    "ds_space_res": "",
    "ds_time_res": "日",
    "ds_coordinate": "无",
    "ds_projection": "",
    "ds_thumbnail": "33887bb4-de19-44ec-8f7c-6415392a3268.png",
    "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": "2023-08-25 15:20:42",
    "last_updated": "2026-01-12 17:32:25",
    "protected": false,
    "protected_to": null,
    "lang": "zh",
    "cstr": "11738.11.NCDC.ZENODO.DB3945.2023",
    "i18n": {
        "en": {
            "title": "Daily RF AOD dataset in the Sichuan Basin, China (2015-2020)",
            "ds_format": "txt",
            "ds_source": "<p>&emsp;&emsp;Simulate using machine learning methods.",
            "ds_quality": "<p>&emsp;&emsp;The data quality is good.",
            "ds_ref_way": "",
            "ds_abstract": "<p>  The random forest (RF) machine learning method and multiple datasets are used to establish aerosol optical depth (AOD) dataset in the cloudy Sichuan Basin. Multiple datasets include ground-based PM10 and PM2.5, the AOD from the Sun-sky radiometer Observation Network (SONET) and the Second Modern-Era Retrospective analysis for Research and Applications (MERRA-2) aerosol reanalysis, and several meteorological variables.</p>",
            "ds_time_res": "日",
            "ds_acq_place": "Sichuan Basin ",
            "ds_space_res": "",
            "ds_projection": "",
            "ds_process_way": "<p>&emsp;&emsp;Using random forest (RF) machine learning method and multiple datasets to establish a cloudy aerosol optical depth (AOD) dataset for the Sichuan Basin.",
            "ds_ref_instruction": ""
        }
    },
    "submit_center_id": "ncdc",
    "data_level": 0,
    "license_type": "https://creativecommons.org/licenses/by/4.0/",
    "doi_reg_from": "reg_outside",
    "cstr_reg_from": "reg_outside",
    "doi_not_reg_reason": null,
    "cstr_not_reg_reason": null,
    "is_paper_in_submitting": false,
    "ds_topic_tags": [
        "气溶胶光学深度",
        "随机森林",
        "多云地区"
    ],
    "ds_subject_tags": [
        "大气科学"
    ],
    "ds_class_tags": [],
    "ds_locus_tags": [
        "四川盆地"
    ],
    "ds_time_tags": [
        2015,
        2016,
        2017,
        2018,
        2019,
        2020
    ],
    "ds_contributors": [
        {
            "true_name": "蒋梦娇",
            "email": "jiangmj@cuit.edu.cn",
            "work_for": "成都信息工程大学",
            "country": "中国"
        }
    ],
    "ds_meta_authors": [
        {
            "true_name": "蒋梦娇",
            "email": "jiangmj@cuit.edu.cn",
            "work_for": "成都信息工程大学",
            "country": "中国"
        }
    ],
    "ds_managers": [
        {
            "true_name": "蒋梦娇",
            "email": "jiangmj@cuit.edu.cn",
            "work_for": "成都信息工程大学",
            "country": "中国"
        }
    ],
    "category": "气象"
}