ChinaClim_timeseries 包含中国 1952-2019 年期间的逐月最高气温数据集，空间分辨率为 1km，该数据是基于气候学辅助插值（CAI）将月度异常面和基线气候学面（ChinaClim_baseline）叠加生成的。数据的比例因子为 0.1。", "ds_source": "
30 年平均气候数据集（1981-2010 年）来自两个来源，即中国气象数据服务中心（CMD：http://data.cma.cn）的 2160 个气象站和中央气象局（www.cwb.gov.tw）的 25 个气象站。1952-2019 年期间 756 个气象站的月地面观测值数据集来自中国气象局 http://data.cma.cn。\n
研究使用了 TRMM3B43 月产品，其空间分辨率为 0.25°，纬度范围为南纬 50°至北纬 50°。从 https://mirador.gsfc.nasa.gov 下载了 NetCDF 格式的 TRMM3B43 第 7 版月度资料。\n
陆地表面温度（LST）由中分辨率成像光谱仪（MODIS）编制。从 1 千米分辨率的 MOD11A2 图像中提取了 2001 年至 2019 年的昼夜 LST 平均值，并按月和年进行了平均。", "ds_process_way": "
采用CAI方法生成1952—2019年中国气温表面（ChinaClim_time系列）数据，温度距平时间序列由气象站原始时间序列与30年正常值的比值和差值计算得出。结合每个气象站的经度、纬度、海拔、到最近海岸的距离、卫星驱动的距平（比率）、CRU距平（比率）和30年正常值，基于其地理坐标，应用TPS模型生成了1952.01-2019.12的温度距平面，其方法与ChinaClim_baseline类似。对于1952-2019年月距平/比值，采用不同的变量组合（经度、纬度、海拔、距最近海岸距离、CRU距平（比值）和30年正常值）构建了7个模型公式，并通过多年（1952-2019年）平均值的最小RMSE值来选择最优模型，以拟合11952-2000年温度距平面。ChinaClim_time序列是通过叠加（乘法）1952.01-2019.12的月度异常（比率）面和ChinaClim_baseline生成的。", "ds_quality": "
研究结果表明，在 ChinaClim_time-series 中，各月气温要素的平均均方根误差为0.461-0.939 ℃。与彭德怀气候面和 CHELSAcruts 相比，温度要素的 R2 几乎没有增加，RMSE 和 MAE 下降了约 50%。研究结果表明，ChinaClim_baseline 对时间序列气候要素的估算精度有明显改善，卫星驱动数据可大幅提高时间序列降水的估算精度，但不能提高时间序列温度的估算精度。", "ds_acq_start_time": "1952-01-01 00:00:00", "ds_acq_end_time": "2019-12-31 00:00:00", "ds_acq_place": "中国", "ds_acq_lon_east": 60.0, "ds_acq_lat_south": 10.0, "ds_acq_lon_west": 140.0, "ds_acq_lat_north": 50.0, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "open-access", "ds_total_size": 16762609153, "ds_files_count": 817, "ds_format": "TIFF", "ds_space_res": "1km", "ds_time_res": "月", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "6ad25b2b-4828-4b78-9338-745020b739a3.png", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "0a4269e1-65f4-45f1-aeba-88ea3068eebf", "ds_serv_man": "敏玉芳", "ds_serv_phone": "0931-4967596", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.15" ], "quality_level": 3, "publish_time": "2024-06-18 09:56:22", "last_updated": "2025-06-30 16:19:13", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.ZENODO.DB6513.2024", "i18n": { "en": { "title": "1 km Monthly Maximum Temperature Dataset for China from 1952 to 2019 (ChinaClim_time-series)", "ds_format": "TIFF", "ds_source": "
The 30-year average climate dataset (1981-2010) comes from two sources, namely the China Meteorological Data Service Center (CMD: http://data.cma.cn ）2160 meteorological stations and 25 meteorological stations of the Central Weather Bureau (www.cwb. gov.tw). The monthly ground observation dataset of 756 meteorological stations from 1952 to 2019 is from the China Meteorological Administration http://data.cma.cn .\n
The TRMM3B43 product was used in the study, with a spatial resolution of 0.25 ° and a latitude range of 50 ° S to 50 ° N. follow https://mirador.gsfc.nasa.gov I downloaded the monthly data of TRMM3B43 7th edition in NetCDF format.\n
The Land Surface Temperature (LST) is compiled by the Medium Resolution Imaging Spectroradiometer (MODIS). The day night LST average values from 2001 to 2019 were extracted from MOD11A2 images with a resolution of 1 kilometer, and averaged by month and year.", "ds_quality": "
The research results indicate that in ChinaClim time series, the average root mean square error of temperature elements for each month is 0.461-0.939 ℃. Compared with the climate surface of Peng Dehuai and CHELSAcruts, the R2 of temperature elements has hardly increased, while RMSE and MAE have decreased by about 50%. The research results indicate that ChinaClim baseline has significantly improved the estimation accuracy of time series climate elements. Satellite driven data can significantly improve the estimation accuracy of time series precipitation, but cannot improve the estimation accuracy of time series temperature.", "ds_ref_way": "", "ds_abstract": "
ChinaClim_timeseries contains the monthly highest temperature dataset of China from 1952 to 2019, with a spatial resolution of 1km. The data is generated by overlaying monthly anomaly surfaces and baseline climatological surfaces (ChinaClim_baseline) based on climatology assisted interpolation (CAI). The scaling factor of the data is 0.1.
", "ds_time_res": "月", "ds_acq_place": "China", "ds_space_res": "1km", "ds_projection": "", "ds_process_way": "The CAI method was used to generate the ChinaClim time series temperature surface data from 1952 to 2019 in China. The temperature anomaly time series was calculated by the ratio and difference between the original time series of meteorological stations and the 30-year normal values. Based on the longitude, latitude, altitude, distance to the nearest coast, satellite driven anomaly (ratio), CRU anomaly (ratio), and 30-year normal values of each meteorological station, the TPS model was applied to generate a temperature anomaly plane from January 1952 to December 2019, using a method similar to the ChinaClim baseline. For the monthly anomaly/ratio from 1952 to 2019, seven model formulas were constructed using different combinations of variables (longitude, latitude, altitude, distance to the nearest coast, CRU anomaly (ratio), and 30-year normal value), and the optimal model was selected based on the minimum RMSE value of the multi-year (1952-2019) average to fit the temperature anomaly plane from 11952-2000. The ChinaClim_time sequence is generated by superimposing (multiplying) the monthly anomaly (ratio) surface from 1952.01-209.12 and the ChinaClim_baseline.", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "https://creativecommons.org/licenses/by/4.0/", "doi_reg_from": "reg_outside", "cstr_reg_from": "reg_outside", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "is_paper_in_submitting": false, "ds_topic_tags": [ "月气温", "辅助插值", "1km空间分辨率" ], "ds_subject_tags": [ "大气科学" ], "ds_class_tags": [], "ds_locus_tags": [ "中国" ], "ds_time_tags": [ 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 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 ], "ds_contributors": [ { "true_name": "刘会玉", "email": "liuhuiyu@njnu.edu.cn", "work_for": "南京师范大学地理科学学院", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "刘会玉", "email": "liuhuiyu@njnu.edu.cn", "work_for": "南京师范大学地理科学学院", "country": "中国" } ], "ds_managers": [ { "true_name": "刘会玉", "email": "liuhuiyu@njnu.edu.cn", "work_for": "南京师范大学地理科学学院", "country": "中国" } ], "category": "气象" }