ERA5-Land是通过ECMWF ERA5再分析模型生产的再分析数据集。该模型通过物理法则将来自世界各地的观测数据结合到一个全球完整且一致的数据集中。该数据集包括了自1981年以来，表征温度、湖泊、积雪、土壤水分、辐射和热量、蒸散和径流、风速、气压、降水量及植被的50种指标的月动态数据，空间分辨率为0.1°×0.1°（约9×9 km2网格），可以准确地描述过去的气候状况。
", "ds_source": "本数据集的数据源为“ERA5-Land monthly averaged data from 1981 to present”再分析数据（https://doi.org/10.24381/cds.68d2bb30 ），是ERA-Interim和ERA-40再分析资料的最新版。
", "ds_process_way": "（1）利用Python程序调取祁连山地区ERA5-Land月值数据;\n（2）利用R 语言程序读取各指标每月数据，并利用对应指标的放缩因子和偏移量，计算各指标真实物理值；\n（3）进一步，利用R语言程序，采用GeoTIFF格式输出保存。
", "ds_quality": "本数据集与源数据集ERA5-Land质量一致。其中温度相关指标7种，湖泊相关指标7种，积雪相关指标8种，土壤水分指标5种，辐射与热量指标7种，蒸散和径流指标10种，风速指标两种，气压和降水量指标各1种，植被叶面积指数指标两种。50种指标的信息详见指标说明文档。\n注意：累积量如降水量的值反映的是对应月份的天平均值，要得到月累计值时，需要将数据乘以当月日数。\n参考网站：https://confluence.ecmwf.int/pages/viewpage.action?pageId=197702790；\nhttps://confluence.ecmwf.int/pages/viewpage.action?pageId=140385203#ERA5Land:datadocumentation-accumulations。
", "ds_acq_start_time": "1981-01-01 00:00:00", "ds_acq_end_time": "2020-03-31 00:00:00", "ds_acq_place": "祁连山地区", "ds_acq_lon_east": 103.9, "ds_acq_lat_south": 35.830000000000005, "ds_acq_lon_west": 93.5, "ds_acq_lat_north": 39.980000000000004, "ds_acq_alt_low": 1040.0, "ds_acq_alt_high": 5993.0, "ds_share_type": "login-access", "ds_total_size": 283460684, "ds_files_count": 11, "ds_format": "栅格/GeoTIFF", "ds_space_res": "0.1°", "ds_time_res": "1个月", "ds_coordinate": "WGS84", "ds_projection": "+proj=longlat +datum=WGS84 +no_defs", "ds_thumbnail": "d78da205-ad65-4dc2-bee8-941bfa270af1.jpg", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "本数据由“中国科学院西北生态环境资源研究院甘肃省祁连山生态环境研究中心”制作，用户在使用数据时请在正文中明确声明数据的来源，并在参考文献部分引用本元数据提供的引用方式。", "ds_from_station": null, "organization_id": "8fa8eec3-a891-44cd-b60e-f6af19a16bba", "ds_serv_man": "张成琦", "ds_serv_phone": "0931-4967575", "ds_serv_mail": "chengqizhang@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.4510" ], "quality_level": 3, "publish_time": "2020-11-30 15:41:20", "last_updated": "2025-06-30 16:26:13", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.ncdc.qlsst.2020.20", "i18n": { "en": { "title": "ERA5 Land Monthly Value Reanalysis Dataset in Qilian Mountains Region (1981-2020)", "ds_format": "Raster/GeoTIFF", "ds_source": "The data source for this dataset is \"ERA5 Land monthly average data from 1981 to present\" reanalysis data（ https://doi.org/10.24381/cds.68d2bb30 ）This is the latest version of ERA Interim and ERA-40 reanalysis data.
", "ds_quality": "This dataset is of the same quality as the source dataset ERA5 Land. Among them, there are 7 temperature related indicators, 7 lake related indicators, 8 snow related indicators, 5 soil moisture indicators, 7 radiation and heat indicators, 10 evapotranspiration and runoff indicators, 2 wind speed indicators, 1 air pressure and 1 precipitation indicator, and 2 vegetation leaf area index indicators. For information on 50 indicators, please refer to the indicator description document.\nNote: The cumulative amount, such as precipitation, reflects the daily average of the corresponding month. To obtain the monthly cumulative value, the data needs to be multiplied by the number of days in the current month.\nReference website: https://confluence.ecmwf.int/pages/viewpage.action?pageId=197702790 ；\nhttps://confluence.ecmwf.int/pages/viewpage.action?pageId=140385203#ERA5Land:datadocumentation -accumulations。
", "ds_ref_way": "", "ds_abstract": "ERA5 Land is a reanalysis dataset produced using the ECMWF ERA5 reanalysis model. This model combines observational data from around the world into a globally complete and consistent dataset through physical laws. This dataset includes monthly dynamic data representing 50 indicators of temperature, lakes, snow cover, soil moisture, radiation and heat, evapotranspiration and runoff, wind speed, air pressure, precipitation, and vegetation since 1981. The spatial resolution is 0.1 °× 0.1 ° (approximately 9 × 9 km2 grid), which can accurately describe past climate conditions.
", "ds_time_res": "1个月", "ds_acq_place": "Qilian Mountains region", "ds_space_res": "0.1°", "ds_projection": "+proj=longlat +datum=WGS84 +no_defs", "ds_process_way": "(1) Retrieve ERA5 Land monthly data from Qilian Mountain area using Python program;\n(2) Using R language programs to read monthly data for each indicator, and using the scaling factors and offsets of the corresponding indicators to calculate the true physical values of each indicator;\n(3) Furthermore, using R language programs, output and save in GeoTIFF format.
", "ds_ref_instruction": "\r\nThis data is produced by \"Gansu Qilian Mountain Ecological Environment Research Center of Northwest Institute of Ecological Environment and Resources, Chinese Academy of Sciences\". When using the data, users should clearly state the source of the data in the text, and quote the reference method provided by this metadata in the reference section." } }, "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": [ "气温", "能量", "祁连山", "再分析数据", "降水" ], "ds_subject_tags": [ "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [], "ds_time_tags": [ 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, 2017, 2018, 2019, 2020 ], "ds_contributors": [ { "true_name": "张举涛", "email": "jutzhang@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" }, { "true_name": "张成琦", "email": "chengqizhang@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" }, { "true_name": "冯起", "email": "qifeng@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "张举涛", "email": "jutzhang@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" } ], "ds_managers": [ { "true_name": "张成琦", "email": "chengqizhang@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" } ], "category": "基础地理" }