本数据集基于ERA5大气再分析数据和中国地区地面气象观测数据，通过团队研建的自适应观测站点空间分布的最优插值同化方法，形成了面向新型电力系统的中国地区（不包括领海）25公里分辨率的逐日网格化气象要素，以及与新型电力系统电源侧、电网侧和负荷侧密切相关的高影响天气事件。本数据集的英文名称是China New-type Power Systems Meteorological dataset（CNPS-Met）。本数据集为促进气象学与新型电力系统交叉领域的研究和应用提供了基础。\n数据集的文件命名为CNPS_Type_History_Daily_Variable_CCYY.nc，时间均采用世界协调时（Universal Time Coordinated，UTC）。其中，Type是气象和新型电力系统电源侧、电网侧和负荷侧的缩写，分别用Meteo、Generation、Grid、Demand表示；Variable为变量名的缩写；CCYY代表年份（如1980年）。气象变量包括：tas（2 m平均气温），tmax（2 m最高气温），tmin（2 m最低气温），precip（累积降水量），wind（10 m平均风速），rhum（2 m平均相对湿度），shum（2 m平均比湿），pres（平均地面气压）。电源侧的高影响天气包括：Vout（切出风速），Vin（切入风速），Lowrad（低辐射），Tmaxg（极端高温），Tming（极端低温）。电网侧的高影响天气包括：Icing（覆冰），Snowing（降雪），Galloping（舞动）。负荷侧的高影响天气包括：Tmaxd（极端高温），Tmind（极端低温），HHE（高温高湿环境）。
\n", "ds_source": "（1）ERA5-Land hourly data：10 m风速、2 m温度、累积降水、2 m相对湿度、地面气压。下载地址：https://cds.climate.copernicus.eu/datasets/reanalysis-era5-land?tab=overview。\n
（2）国家级地面气象站数据：10 m风速、2 m温度、累积降水、2 m相对湿度、地面气压。共2598个站点。下载地址：https://data.cma.cn/。
\n", "ds_process_way": "（1）数据准备与预处理：收集整理数据源，完成单位的转换及格式的统一，并通过“3σ准则”剔除异常值。\n（2）基于自适应观测站点空间分布的最优插值同化：根据背景场中目标网格点周围观测站点的空间分布、密度，动态调整最优插值同化方案中观测算子的影响半径，以分析场误差最小、相关性最高等为准确，不断迭代优化观测算子，提高最优插值同化方案的效果。\n（3）高影响天气事件的识别和提取：从发电效率损失、设备损坏、供给需求等多个维度和层面，识别和提取新型电力系统电源侧、电网侧、负荷侧的高影响天气事件，计算其频率、强度等。\n（4）数据集输出：将最终的数据进行格式转换、数据质量控制，并存储到NetCDF数据库。
\n", "ds_quality": "误差对比结果表明，本数据集具有较高的质量，中国地区风速、温度、湿度、降水和地面气压更接近于观测。数据集仅同化了中国陆地区域的观测资料，故中国陆地地区以外（含海洋）的资料不建议使用。
\n\n", "ds_acq_start_time": "1986-01-01 00:00:00", "ds_acq_end_time": "2016-12-31 00:00:00", "ds_acq_place": "中国地区（不包括领海）", "ds_acq_lon_east": 70.0, "ds_acq_lat_south": 16.0, "ds_acq_lon_west": 140.0, "ds_acq_lat_north": 56.0, "ds_acq_alt_low": -154.0, "ds_acq_alt_high": 8157.0, "ds_share_type": "open-access", "ds_total_size": 195668804857, "ds_files_count": 704, "ds_format": "NetCDF", "ds_space_res": "25公里", "ds_time_res": "逐日", "ds_coordinate": "WGS84", "ds_projection": "等经纬度投影", "ds_thumbnail": "97a23ec6-2f2f-4cb4-b5b2-6557f51e7abb.png", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "本数据集的存储格式为NetCDF，该格式为网格数据通用存储格式，可使用任何支持 NetCDF的编程语言或软件读取，如C、FORTRAN、Python、NCL等。", "ds_from_station": null, "organization_id": "52b7b79b-860c-49a5-9083-9a70cf8bed5a", "ds_serv_man": "敏玉芳", "ds_serv_phone": "0931-4967596", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.1545" ], "quality_level": 3, "publish_time": "2025-10-13 09:46:18", "last_updated": "2025-12-19 09:47:52", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.NIEER.DB6972.2025", "i18n": { "en": { "title": "A 25 km Daily Gridded Dataset of Meteorological Variables and High-Impact Weather Events for New-type Power Systems in China", "ds_format": "NetCDF", "ds_source": "(1) ERA5 Land hourly data: 10m wind speed, 2m temperature, accumulated precipitation, 2m relative humidity, surface pressure. Download link: https://cds.climate.copernicus.eu/datasets/reanalysis-era5-land?tab=overview. \n
(2) Ground-based meteorological observations: 10m wind speed, 2m temperature, accumulated precipitation, 2m relative humidity, surface pressure. There are a total of 2598 sites. Download link: https://data.cma.cn/.", "ds_quality": "
The error comparison results indicate that this dataset has high quality, and the wind speed, temperature, humidity, precipitation, and ground pressure in China are closer to observations. The dataset only assimilates observational data from mainland China, so it is not recommended to use data outside of mainland China (including the ocean).
\n", "ds_ref_way": "", "ds_abstract": "This dataset is derived from ERA5 atmospheric reanalysis data and ground-based meteorological observations across China. By employing a research-developed adaptive optimal interpolation assimilation method that accounts for the spatial distribution of observational stations, a daily gridded meteorological dataset with a spatial resolution of 25 km has been constructed. It covers mainland China (excluding territorial waters) and is tailored to support the new-type power systems. The dataset also includes high-impact weather events closely associated with the generation-side, grid-side, and demand-side of the new-type power systems. Its English designation is the China New-type Power Systems Meteorological dataset (CNPS-Met). This dataset provides a foundational resource for advancing interdisciplinary research and applications bridging meteorology and new-type power systems. Dataset files are named in the format: CNPS_Type_History_Daily_Variable_CCYY.nc, with all times provided in Coordinated Universal Time (UTC). Here, “Type” denotes the category related to meteorology and the components of the new-type power system, abbreviated as: Meteo (meteorological), Generation (generation-side), Grid (grid-side), and Demand (demand-side). “Variable” refers to the abbreviated variable name, and “CCYY” indicates the year (e.g., 1980). The meteorological variables include: tas (mean temperature at 2 m), tmax (maximum temperature at 2 m), tmin (minimum temperature at 2 m), precip (accumulated precipitation), wind (mean wind speed at 10 m), rhum (mean relative humidity at 2 m), shum (mean specific humidity at 2 m), pres (mean surface pressure). The high-impact weather events for the generation-side include: Vout (cut-out wind speed), Vin (cut-in wind speed), Lowrad (low solar radiation), Tmaxg (extreme high temperature), Tming (extreme low temperature). The high-impact weather events for the grid-side include: Icing (Ice accretion), Snowing (Snowfall), and Galloping (Conductor galloping). The high-impact weather events for the demand-side include: Tmaxd (extreme high temperature), Tmind (extreme low temperature), HHE (heat and humid environment or High enthalpy environment).
", "ds_time_res": "逐日", "ds_acq_place": "China region (excluding territorial waters)", "ds_space_res": "25公里", "ds_projection": "Equidistant Conformal Projection", "ds_process_way": "(1) Data preparation and preprocessing: Collect and organize data sources, complete unit conversion and format unification, and eliminate outliers through the \"3 σ criterion\".
(2) Assimilation based on the spatially adaptive optimal interpolation assimilation scheme proposed by our research group: this method dynamically adjusts the influence radius of the observation operator in the optimal interpolation assimilation scheme according to the spatial distribution and density of observation stations around the target grid point in the background field. The process iteratively optimizes the observation operator with the objectives of minimizing analysis field errors and maximizing correlation, thereby enhancing the effectiveness of the optimal interpolation assimilation scheme.
(3) Identification and extraction of high-impact weather events: high-impact weather events related to the generation-side, grid-side, and demand-side of the new-type power system are identified and extracted from multiple dimensions and levels, including power generation efficiency loss, equipment damage, and supply-demand imbalance. Their frequency, intensity, and other relevant metrics are calculated.
(4) Dataset output: the final data undergo format conversion and quality control, and are stored in a NetCDF format.", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "CC BY 4.0", "ds_topic_tags": [ "新型电力系统，", "气象要素，", "高影响天气事件" ], "ds_subject_tags": [ "应用气象学" ], "ds_class_tags": [], "ds_locus_tags": [ "中国地区（不包括领海）" ], "ds_time_tags": [ 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016 ], "ds_contributors": [ { "true_name": "张飞民", "email": "zfm@lzu.edu.cn", "work_for": "兰州大学大气科学学院", "country": "中国" }, { "true_name": "毕凯轩", "email": "bikx2023@lzu.edu.cn", "work_for": "兰州大学大气科学学院", "country": "中国" }, { "true_name": "陈星", "email": "xing-chen@geidco.org", "work_for": "全球能源互联网集团有限公司", "country": "中国" }, { "true_name": "杨方", "email": "fang-yang1@geidco.org", "work_for": "全球能源互联网集团有限公司", "country": "中国" }, { "true_name": "杨毅", "email": "yangyi@lzu.edu.cn", "work_for": "兰州大学大气科学学院", "country": "中国" }, { "true_name": "王澄海", "email": "wch@lzu.edu.cn", "work_for": "兰州大学大气科学学院", "country": "中国" }, { "true_name": "赵子健", "email": "zijian-zhao@geidco.org", "work_for": "全球能源互联网集团有限公司", "country": "中国" }, { "true_name": "马志远", "email": "zhiyuan-ma@geidco.org", "work_for": "全球能源互联网集团有限公司", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "张飞民", "email": "zfm@lzu.edu.cn", "work_for": "兰州大学大气科学学院", "country": "中国" }, { "true_name": "毕凯轩", "email": "bikx2023@lzu.edu.cn", "work_for": "兰州大学大气科学学院", "country": "中国" }, { "true_name": "陈星", "email": "xing-chen@geidco.org", "work_for": "全球能源互联网集团有限公司", "country": "中国" }, { "true_name": "王澄海", "email": "wch@lzu.edu.cn", "work_for": "兰州大学大气科学学院", "country": "中国" } ], "ds_managers": [ { "true_name": "张飞民", "email": "zfm@lzu.edu.cn", "work_for": "兰州大学大气科学学院", "country": "中国" }, { "true_name": "王澄海", "email": "wch@lzu.edu.cn", "work_for": "兰州大学大气科学学院", "country": "中国" } ], "category": "气象" }