利用ERA5气温降水数据，计算柴达木地区年均气温，并通过插值、回归分析等方法计算1985-2018年气温变化率，通过渔网工具，统计每1km气温变化率。\n
本数据集主要应用于冰冻圈科学研究。", "ds_source": "
ERA5再分析数据集是欧洲中期天气预报中心的第五代产品。提供大量陆地气候变量的数据分布在全球的0.25*0.25°网格上。ERA5再分析数据具有较高的时间和空间分辨率，因此可以提供对气象条件的详细评估。", "ds_process_way": "
通过ERA5数据，计算1985年-2015年柴达木盆地各年年均气温，通过插值、回归分析，再通过与冰川相同渔网计算得到1km气温变化率。\n
Kriging插值法原理：Z ̂(s0 )=∑(i=1)^N▒〖λi Z(si)〗\n
Z(si )是第i个位置的测量值，λi是第i个位置处的测量值的权重，s0是预测位置，N是测量值数。\n
回归分析原理：y=a*x+b\n
a: 回归系数 coefficient\n
b: 截距 intercept", "ds_quality": "
a. 原始资料数据精度\n
ERA5再分析数据集是欧洲中期天气预报中心的第五代产品。提供大量陆地气候变量的数据分布在全球的0.25*0.25°网格上。ERA5再分析数据具有较高的时间和空间分辨率，因此可以提供对气象条件的详细评估。\n
b. 项目数据产生和汇集过程中的相关质量控制措施，包括完整的数据产生过程、使用的方法和标准规范、数据应用范围等内容。\n
(1)数据生产过程: ERA5提供了大量的海洋气候和每小时的气候变量。\n
(2)方法和标准规范: 这些数据以0.25°×0.25°的网格覆盖地球，数据集中包含 200 多个参数，提供了大量的逐小时的大气、陆地和海洋气候变量。该数据基于改进的三维变分技术，拥有时空分辨率高、更新快、参数多等优点。\n
c. 加工后数据精度\n
在加工生成数据时，保留所有原始数据。", "ds_acq_start_time": "1985-01-01 00:00:00", "ds_acq_end_time": "2018-12-31 00:00:00", "ds_acq_place": "柴达木盆地", "ds_acq_lon_east": 99.25, "ds_acq_lat_south": 35.0, "ds_acq_lon_west": 90.25, "ds_acq_lat_north": 39.31666666666667, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 2941881, "ds_files_count": 2, "ds_format": "tif", "ds_space_res": "1km", "ds_time_res": "年", "ds_coordinate": "WGS84", "ds_projection": "", "ds_thumbnail": "d81957b5-d0b5-404e-9566-db179ad6d8dd.png", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "4851e874-eafc-4879-812b-ffbdd825e967", "ds_serv_man": "敏玉芳", "ds_serv_phone": "0931-4967596", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "10.12072/ncdc.Hydro.db2440.2022", "subject_codes": [ "170.45" ], "quality_level": 3, "publish_time": "2022-09-29 11:01:40", "last_updated": "2025-04-28 16:43:26", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.ncdc.Hydro.db2440.2022", "i18n": { "en": { "title": "1km resolution temperature change rate in Qaidam area (1985-2018)", "ds_format": "TIF", "ds_source": "
Era5 reanalysis data set is the fifth generation product of the European Center for medium range weather forecasting. The data providing a large number of land climate variables are distributed on the global 0.25 * 0.25 ° grid. Era5 reanalysis data has high temporal and spatial resolution, so it can provide a detailed assessment of meteorological conditions.", "ds_quality": "
a. Accuracy of original data\n
Era5 reanalysis data set is the fifth generation product of the European Center for medium range weather forecasting. The data providing a large number of land climate variables are distributed on the global 0.25 * 0.25 ° grid. Era5 reanalysis data has high temporal and spatial resolution, so it can provide a detailed assessment of meteorological conditions.\n
b. Relevant quality control measures in the process of project data generation and collection, including the complete data generation process, the methods and standards used, and the scope of data application.\n
(1) Data production process: era5 provides a large number of marine climate and hourly climate variables.\n
(2) Methods and standard specifications: These data are expressed at 0.25 ° × The 0.25 ° grid covers the earth, and the data set contains more than 200 parameters, providing a large number of hourly atmospheric, land and marine climate variables. This data is based on the improved three-dimensional variational technology, and has the advantages of high spatial-temporal resolution, fast update, and many parameters.\n
c. Data accuracy after processing\n
When processing the generated data, keep all the original data.", "ds_ref_way": "", "ds_abstract": "
Using era5 temperature and precipitation data, calculate the average annual temperature in Qaidam area, calculate the temperature change rate from 1985 to 2018 through interpolation and regression analysis, and count the temperature change rate every 1km through fishing net tools.\n
This data set is mainly applied to the scientific research of Cryosphere.
", "ds_time_res": "年", "ds_acq_place": "Qaidam Basin", "ds_space_res": "1km", "ds_projection": "", "ds_process_way": "According to era5 data, the annual average temperature of Qaidam Basin from 1985 to 2015 is calculated. Through interpolation and regression analysis, the 1km temperature change rate is calculated through the same fishing net as the glacier.\n
Kriging interpolation principle: Z ̂ (s0 )=∑(i=1)^N▒〖 λi Z(si)〗\n
Z (s I ) is the measured value of the ith position, λ I is the weight of the measured value at the ith position, s 0 is the predicted position, and N is the number of measured values.\n
Regression analysis principle: y = a * x + B\n
a: Regression coefficient\n
b: Intercept", "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, "is_paper_in_submitting": false, "ds_topic_tags": [ "柴达木盆地", "1985-2018", "气温变化率" ], "ds_subject_tags": [ "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "柴达木盆地" ], "ds_time_tags": [ 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 ], "ds_contributors": [ { "true_name": "朱高峰", "email": "zhugf@lzu.edu.cn", "work_for": "兰州大学", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "石梦寒", "email": "shimh20@lzu.edu.cn", "work_for": "兰州大学", "country": "中国" } ], "ds_managers": [ { "true_name": "朱高峰", "email": "zhugf@lzu.edu.cn", "work_for": "兰州大学", "country": "中国" } ], "category": "气象" }