利用ERA5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100km气温变化率。", "ds_source": "
ERA5气温降水数据集。", "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100km气温变化率。\n
Kriging插值法原理：Z ̂(s_0 )=∑_(i=1)^N▒〖λ_i Z(s_i)〗\n
Z(s_i )是第i个位置的测量值，λ_i是第i个位置处的测量值的权重，s_0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": "
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
(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
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": 1227411, "ds_files_count": 2, "ds_format": "tif", "ds_space_res": "100km", "ds_time_res": "", "ds_coordinate": "WGS84", "ds_projection": "", "ds_thumbnail": "fcbeedfa-47b7-4625-9fa6-bd9201ecf8e5.jpg", "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.db2430.2022", "subject_codes": [ "170.45" ], "quality_level": 3, "publish_time": "2022-09-29 11:02:20", "last_updated": "2025-04-23 09:53:09", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.ncdc.Hydro.db2430.2022", "i18n": { "en": { "title": "100 km resolution temperature change rate in Qaidam (1985-2018)", "ds_format": "TIF", "ds_source": "
ERA5 temperature and precipitation dataset.", "ds_quality": "
The ERA5 reanalysis dataset is the fifth-generation product from the European Centre for Medium-Range Weather Forecasts (ECMWF). It provides extensive land climate variables distributed on a global 0.25°×0.25° grid. Due to its high temporal and spatial resolution, ERA5 enables detailed assessments of meteorological conditions.\n
(1) Data production process: ERA5 offers a vast array of oceanic and hourly climate variables.\n
(2) Methodology and standardization: The data cover the Earth on a 0.25°×0.25° grid, containing over 200 parameters, including hourly atmospheric, terrestrial, and oceanic climate variables. The dataset is generated using an improved 3D-Var (three-dimensional variational) assimilation technique, offering advantages such as high spatiotemporal resolution, rapid updates, and extensive parameter coverage.\n
During data processing, all raw data are preserved.", "ds_ref_way": "", "ds_abstract": "
Using ERA5 precipitation data, the mean annual temperature in the Qaidam region was calculated. The rate of temperature change from 1985 to 2018 was determined through interpolation and regression analysis. A fishnet tool was employed to statistically analyze the temperature change rate per 100 km.
", "ds_time_res": "", "ds_acq_place": "Qaidam Basin", "ds_space_res": "100km", "ds_projection": "", "ds_process_way": "Based on era5 data, the annual average temperature of Qaidam Basin from 1985 to 2015 is calculated. Through interpolation, regression analysis, and the same fishing net as the glacier, the temperature change rate of 100km is calculated.\n
Kriging interpolation principle: Z ̂ (s_0 )=∑_ (i=1)^N▒〖 λ_ i Z(s_i)〗\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", "气温变化率", "1985-2018年" ], "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" }