{
    "created": "2024-07-17 16:54:20",
    "updated": "2026-05-09 02:53:36",
    "id": "1ae60ee0-0464-4eea-bf33-6588b44abf8f",
    "version": 5,
    "ds_topic": null,
    "title_cn": "中国高空间分辨率(1km)人体热指数月度数据集(2003-2020年)",
    "title_en": "Monthly dataset of human thermal index with high spatial resolution (1km) in China (2003-2020)",
    "ds_abstract": "<p>&emsp;&emsp;每月高空间分辨率人类热指数收集(hiti - monthly)包括近地表气温(SAT)和11个常用的人类感知温度指数:室内视温(ATin)、室外遮荫视温(ATout)、不适指数(DI)、有效温度(ET)、热指数(HI)、湿度指数(HMI)、修正不适指数(MDI)、净有效温度(Net)、简化湿球温度(sWBGT)、湿球温度(WBT)和风寒温度(WCT)。该数据集具有1 km × 1 km的高空间分辨率，覆盖了2003年1月至2020年12月的中国大陆地区。HiTIC数据集中12个热指标的总体r平方、均方根误差和平均绝对误差分别为0.996、0.693和0.512°C。按年堆叠，每个堆叠由12个月的NetCDF格式图像组成。数据集的单位为0.01℃(°C)，为了节省存储空间，数据以整数类型(Int16)存储，使用时需要除以100得到以℃为单位的数据。数据集的投影坐标系为Albers等面积圆锥投影。",
    "ds_source": "<p>&emsp;&emsp;中国大陆2419个气象站的观测资料。",
    "ds_process_way": "<p>&emsp;&emsp;使用光梯度增强机(LightGBM)学习算法从多源数据中生成12个常用的热指数，包括地表温度、地形、土地覆盖、人口密度和不透水面比例。",
    "ds_quality": "<p>&emsp;&emsp;数据质量较好。",
    "ds_acq_start_time": "2003-01-01 00:00:00",
    "ds_acq_end_time": "2020-12-31 00:00:00",
    "ds_acq_place": "中国大陆地区",
    "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": "login-access",
    "ds_total_size": 37034555498,
    "ds_files_count": 218,
    "ds_format": "NetCDF",
    "ds_space_res": "1000",
    "ds_time_res": "月",
    "ds_coordinate": "无",
    "ds_projection": "",
    "ds_thumbnail": "1ae60ee0-0464-4eea-bf33-6588b44abf8f.png",
    "ds_thumb_from": 0,
    "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": "2024-07-26 17:03:17",
    "last_updated": "2026-01-14 11:04:15",
    "protected": false,
    "protected_to": null,
    "lang": "zh",
    "cstr": "11738.11.NCDC.ZENODO.DB6675.2024",
    "i18n": {
        "en": {
            "title": "Monthly dataset of human thermal index with high spatial resolution (1km) in China (2003-2020)",
            "ds_format": "NetCDF",
            "ds_source": "<p>&emsp;Observation data of 2419 meteorological stations in Chinese Mainland.",
            "ds_quality": "<p>&emsp;The data quality is good.",
            "ds_ref_way": "",
            "ds_abstract": "<p> The monthly high spatial resolution human thermal index collection (hiti monthly) includes near surface temperature (SAT) and 11 commonly used human perceived temperature indices: indoor apparent temperature (ATin), outdoor shaded apparent temperature (ATout), discomfort index (DI), effective temperature (ET), heat index (HI), humidity index (HMI), modified discomfort index (MDI), net effective temperature (Net), simplified wet bulb temperature (sWBGT), wet bulb temperature (WBT), and wind chill temperature (WCT). The dataset has a high spatial resolution of 1 km × 1 km, covering the Chinese Mainland from January 2003 to December 2020. The overall r-squared, root mean square error, and mean absolute error of the 12 thermal indicators in the HiTIC dataset are 0.996, 0.693, and 0.512 ° C, respectively. Stacked annually, each stack consists of 12 months of NetCDF format images. The unit of the dataset is 0.01 ℃ (° C). In order to save storage space, the data is stored in integer format (Int16) and needs to be divided by 100 to obtain data in ℃. The projection coordinate system of the dataset is Albers equal area cone projection.</p>",
            "ds_time_res": "月",
            "ds_acq_place": "Chinese Mainland",
            "ds_space_res": "1000",
            "ds_projection": "",
            "ds_process_way": "<p>&emsp;Use the LightGBM learning algorithm to generate 12 commonly used thermal indices from multi-source data, including surface temperature, terrain, land cover, population density, and impervious surface ratio.",
            "ds_ref_instruction": ""
        }
    },
    "submit_center_id": "ncdc",
    "data_level": 0,
    "license_type": "https://creativecommons.org/licenses/by/4.0/",
    "doi_reg_from": "reg_local",
    "cstr_reg_from": "reg_local",
    "doi_not_reg_reason": null,
    "cstr_not_reg_reason": null,
    "is_paper_in_submitting": false,
    "ds_topic_tags": [
        "人类热指数",
        "近地表气温",
        "人类感知温度指数"
    ],
    "ds_subject_tags": [
        "大气科学"
    ],
    "ds_class_tags": [],
    "ds_locus_tags": [
        "中国大陆地区"
    ],
    "ds_time_tags": [
        2003,
        2004,
        2005,
        2006,
        2007,
        2008,
        2009,
        2010,
        2011,
        2012,
        2013,
        2014,
        2015,
        2016,
        2017,
        2018,
        2019,
        2020
    ],
    "ds_contributors": [
        {
            "true_name": "罗明",
            "email": "luom38@mail.sysu.edu.cn",
            "work_for": "中山大学地理与规划学院/广东省城市化与地理模拟重点实验室",
            "country": "中国"
        }
    ],
    "ds_meta_authors": [
        {
            "true_name": "罗明",
            "email": "luom38@mail.sysu.edu.cn",
            "work_for": "中山大学地理与规划学院/广东省城市化与地理模拟重点实验室",
            "country": "中国"
        }
    ],
    "ds_managers": [
        {
            "true_name": "罗明",
            "email": "luom38@mail.sysu.edu.cn",
            "work_for": "中山大学地理与规划学院/广东省城市化与地理模拟重点实验室",
            "country": "中国"
        }
    ],
    "category": "气象"
}