{
    "created": "2023-10-16 15:55:44",
    "updated": "2026-05-01 15:37:54",
    "id": "ce051692-a881-43e8-b416-0ee86db4c1b0",
    "version": 4,
    "ds_topic": null,
    "title_cn": "中国高分辨率空气质量再分析数据集 (CAQRA)（2013-2018年）",
    "title_en": "A High-resolution Air Quality Reanalysis Dataset over China (CAQRA)",
    "ds_abstract": "<p>&emsp;&emsp;本数据集提供了六种常规空气污染物（即 PM2.5、PM10、SO<sub>2</sub>、NO<sub>2</sub>、CO 和 O<sub>3</sub>）的地表网格场，以及 WRF 模式模拟的地表风速（u、v）、气压（psfc）、相对湿度（RH）和温度（temp）场。空间和时间分辨率分别为 15km 和 1 小时。数据集的时间段为 2013 年至 2019 年。",
    "ds_source": "<p>&emsp;&emsp; 数据集由中国科学院大气物理研究所开发的化学数据同化系统（ChemDAS）生成，该系统基于集合卡尔曼滤波器（EnKF）和嵌套空气质量预测模型系统（NAQPMS），同化了中国气象局的1000多个地面空气质量监测点。该方法突破了大气化学数据同化中存在的不稳定性、调整不充分、同化负效应等问题，发展了多大气污染物协同同化，包括监测数据自动质量控制方法、自适应模式误差估计等先进算法。",
    "ds_process_way": "<p>&emsp;&emsp; 数据集由中国科学院大气物理研究所开发的化学数据同化系统（ChemDAS）生成，同化了中国气象局的1000多个地面空气质量监测点。",
    "ds_quality": "<p>&emsp;&emsp; 数据集通过交叉验证和独立数据验证进行评估。",
    "ds_acq_start_time": "2013-01-01 00:00:00",
    "ds_acq_end_time": "2018-12-31 00:00:00",
    "ds_acq_place": "中国",
    "ds_acq_lon_east": null,
    "ds_acq_lat_south": null,
    "ds_acq_lon_west": null,
    "ds_acq_lat_north": null,
    "ds_acq_alt_low": null,
    "ds_acq_alt_high": null,
    "ds_share_type": "open-access",
    "ds_total_size": 741643611397,
    "ds_files_count": 2630,
    "ds_format": "",
    "ds_space_res": "15km",
    "ds_time_res": "时",
    "ds_coordinate": "无",
    "ds_projection": "",
    "ds_thumbnail": "ce051692-a881-43e8-b416-0ee86db4c1b0.png",
    "ds_thumb_from": 0,
    "ds_ref_way": "",
    "paper_ref_way": "",
    "ds_ref_instruction": "",
    "ds_from_station": null,
    "organization_id": "d2c052ce-d283-4a48-8962-6a3dbcb03b8e",
    "ds_serv_man": "敏玉芳",
    "ds_serv_phone": "0931-4967596",
    "ds_serv_mail": "ncdc@lzb.ac.cn",
    "doi_value": "",
    "subject_codes": [
        "170.45"
    ],
    "quality_level": 3,
    "publish_time": "2023-11-27 16:32:05",
    "last_updated": "2025-05-29 11:06:27",
    "protected": false,
    "protected_to": null,
    "lang": "zh",
    "cstr": "https://cstr.cn/31253.11.sciencedb.00053",
    "i18n": {
        "en": {
            "title": "A High-resolution Air Quality Reanalysis Dataset over China (CAQRA)",
            "ds_format": "",
            "ds_source": "<p>&emsp; &emsp; The data set was generated by the Chemical Data Assimilation System (ChemDAS) developed by the Institute of Atmospheric Physics, Chinese Academy of Sciences. Based on the Ensemble Kalman Filter (EnKF) and the Nested Air Quality Prediction Model System (NAQPMS), the system assimilated more than 1000 ground air quality monitoring points of the China Meteorological Administration. This method has overcome the problems of instability, insufficient adjustment, and negative assimilation effects in atmospheric chemical data assimilation, and developed advanced algorithms for collaborative assimilation of multiple atmospheric pollutants, including automatic quality control methods for monitoring data and adaptive mode error estimation.",
            "ds_quality": "<p>&emsp; &emsp; The dataset is evaluated through cross validation and independent data validation.",
            "ds_ref_way": "",
            "ds_abstract": "<p>    This dataset provides surface grid fields for six conventional air pollutants (i.e. PM2.5, PM10, SO<sub>2</sub>, NO<sub>2</sub>, CO and O<sub>3</sub>), as well as surface wind speed (u, v), atmospheric pressure (psfc), relative humidity (RH), and temperature (temp) fields simulated by WRF models. The spatial and temporal resolutions are 15km and 1 hour, respectively. The time period of the dataset is from 2013 to 2019.</p>",
            "ds_time_res": "时",
            "ds_acq_place": "China",
            "ds_space_res": "15km",
            "ds_projection": "",
            "ds_process_way": "<p>&emsp; &emsp; The data set was generated by the Chemical Data Assimilation System (ChemDAS) developed by the Institute of Atmospheric Physics, Chinese Academy of Sciences, and assimilated more than 1000 ground air quality monitoring points of the China Meteorological Administration.",
            "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,
    "is_paper_in_submitting": false,
    "ds_topic_tags": [
        "空气污染",
        "再分析",
        "数据同化",
        "PM2.5",
        "PM10",
        "SO2",
        "NO2",
        "CO",
        "O3"
    ],
    "ds_subject_tags": [
        "地理学"
    ],
    "ds_class_tags": [],
    "ds_locus_tags": [
        "中国"
    ],
    "ds_time_tags": [
        2013,
        2014,
        2015,
        2016,
        2017,
        2018,
        2019
    ],
    "ds_contributors": [
        {
            "true_name": "唐晓",
            "email": "tangxiao@mail.iap.ac.cn",
            "work_for": "中国科学院大气物理研究所",
            "country": "中国"
        }
    ],
    "ds_meta_authors": [
        {
            "true_name": "唐晓",
            "email": "tangxiao@mail.iap.ac.cn",
            "work_for": "中国科学院大气物理研究所",
            "country": "中国"
        }
    ],
    "ds_managers": [
        {
            "true_name": "唐晓",
            "email": "tangxiao@mail.iap.ac.cn",
            "work_for": "中国科学院大气物理研究所",
            "country": "中国"
        }
    ],
    "category": "气象"
}