本数据集涵盖敦煌(典型区1)、民勤（典型区2）、奈曼（典型区3）三大典型区。气候背景数据含 1971 年至2020年气温、降水等基线指标，极端事件时间序列记录 1971 年至2020年暴雨、高温、沙尘暴等事件。为区域气候 - 灾害关联研究提供基础数据支撑。", "ds_source": "
原始气象数据为中国地面气候日值数据集 (V3.0)，来自国家气象信息中心-中国气象数据网，为.txt格式，时间范围为1971-2020年，包含站点编号、观测日期、日最高温度、日最低温度、日降水量关键字段。其中敦煌典型区内包含12个气象站点，民勤典型区包含31个气象站点，奈曼典型区包含37个气象站点", "ds_process_way": "
\"本研究严格遵循世界气象组织（WMO）《气候变化检测与指标指南》中的
ETCCDI（气候变化检测与指标专家组）指标体系，以中国北方 1971-2020 年逐日气象观测数据为基础展开研究。该数据集涵盖日最高温度、日最低温度、日降水量及沙尘暴观测记录等核心要素，研究在 R 语言环境中通过多步骤标准化流程，完成 29 类核心极端气候指标的计算。\n具体流程中，首先对原始气象站点数据实施严格的数据质量控制，剔除异常值与缺测数据，保障基础数据的可靠性；随后采用两种核心方法计算极端气候指标：其一为绝对阈值法，重点计算最大 1 日降水量（RX1day）、最大 5 日降水量（RX5day）等反映降水极值特征的指标；其二为相对百分位法，用于计算强降水量（R95p）、极端降水日数（R20mm）、降水强度（SDII）等依赖基准期气候背景的指标，确保指标计算的科学性与规范性。\"
", "ds_quality": "1971-2018年中超过5年有缺测超过60天/年；2019-2020年月缺测多于3天则剔除该月；缺测少于3天利用前/后1天数据填补", "ds_acq_start_time": "1971-01-01 00:00:00", "ds_acq_end_time": "2020-12-31 00:00:00", "ds_acq_place": "敦煌、民勤、奈曼周围气象站点", "ds_acq_lon_east": 118.0, "ds_acq_lat_south": 33.0, "ds_acq_lon_west": 91.25, "ds_acq_lat_north": 45.0, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 777816, "ds_files_count": 33, "ds_format": "xlsx", "ds_space_res": "", "ds_time_res": "年", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "20991faa-ef4c-4b86-bdca-e02447d01a90.png", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "6d0aa454-9b64-4be5-b0cd-4cc796e6aea0", "ds_serv_man": "李红星", "ds_serv_phone": "0931-4967592", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.4510" ], "quality_level": 3, "publish_time": "2026-03-13 19:11:30", "last_updated": "2026-03-13 19:11:30", "protected": false, "protected_to": "2028-03-13 00:00:00", "lang": "zh", "cstr": "11738.11.NCDC.DESERTIFICATION.DB7164.2026", "i18n": { "en": { "title": "Time Series of Climate Background and Extreme Climate Events in Three Typical Regions (Dunhuang, Minqin, Naiman) of Northern China from 1971 to 2020", "ds_format": "xlsx", "ds_source": "
The original meteorological data is the China Ground Climate Daily Value Dataset (V3.0), from the National Meteorological Information Center China Meteorological Data Network, in. txt format, with a time range of 1971-2020, including key fields such as station number, observation date, daily maximum temperature, daily minimum temperature, and daily precipitation. The Dunhuang typical area includes 12 meteorological stations, the Minqin typical area includes 31 meteorological stations, and the Naiman typical area includes 37 meteorological stations", "ds_quality": "
Over 5 years from 1971 to 2018, there were more than 60 days/year of missing tests; If there are more than 3 days of missing tests in 2019-2020, that month will be excluded; Missing testing for less than 3 days, filled with data from 1 day before/after using", "ds_ref_way": "", "ds_abstract": "
This dataset covers three typical areas: Dunhuang (typical area 1), Minqin (typical area 2), and Naiman (typical area 3). Climate background data includes baseline indicators such as temperature and precipitation from 1971 to 2020, and extreme event time series records rainstorm, high temperature, sandstorm and other events from 1971 to 2020. Provide basic data support for regional climate disaster correlation research.", "ds_time_res": "年", "ds_acq_place": "Meteorological stations around Dunhuang, Minqin, and Naiman", "ds_space_res": "", "ds_projection": "", "ds_process_way": "
This study strictly follows the guidelines for climate change detection and indicators of the World Meteorological Organization (WMO); The ETCCDI (Expert Group on Climate Change Detection and Indicators) indicator system is based on daily meteorological observation data from northern China from 1971 to 2020. This dataset covers core elements such as daily maximum temperature, daily minimum temperature, daily precipitation, and sandstorm observation records. Through a multi-step standardized process in the R language environment, 29 core extreme climate indicators were calculated.\nIn the specific process, strict data quality control is first implemented on the original meteorological station data, eliminating outliers and missing data to ensure the reliability of basic data; Subsequently, two core methods were used to calculate extreme climate indicators: one was the absolute threshold method, which focused on calculating indicators such as maximum 1-day precipitation (RX1-day) and maximum 5-day precipitation (RX5day) that reflect the characteristics of precipitation extremes; The second method is the relative percentile method, which is used to calculate indicators such as heavy precipitation (R95p), extreme precipitation days (R20mm), precipitation intensity (SDII) that depend on the climate background of the reference period, ensuring the scientific and standardized calculation of indicators. \"
", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "CC BY 4.0", "doi_reg_from": "reg_local", "cstr_reg_from": "reg_local", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "ds_topic_tags": [ "中国北方", "敦煌民勤奈曼", "三大典型区" ], "ds_subject_tags": [ "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "敦煌、民勤、奈曼周围气象站点" ], "ds_time_tags": [ 1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 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, 2017, 2018, 2019, 2020 ], "ds_contributors": [ { "true_name": "苗运法", "email": "miaoyunfa@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": "赵永涛", "email": "zhaoyt2018@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": "张孜越", "email": "zhangziyue22@mails.ucas.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": "赵永涛", "email": "zhaoyt2018@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": "赵永涛", "email": "zhaoyt2018@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": "沙漠与荒漠化" }