ChinaClim_timeseries 是中国 1952-2019 年期间的月度降水数据集，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。", "ds_process_way": "
采用CAI方法生成1952—2019年中国月降水量（ChinaClim_time系列）数据，降水比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的月降水比距平面，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（表S2），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1952-1997年降水比面。在剩余的时间内，我们根据步骤（3）中的最优模型构建了2个模型公式。这两个模型将卫星数据（TRMM比和LST异常）添加为独立样条变量或线性协变量。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，各月降水的平均均方根误差分别为 7.502- 52.307 毫米。与彭德怀气候面和 CHELSAcruts 相比，降水要素的 R2 增加了约 7%，RMSE 和 MAE 下降了约 17%。", "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": "login-access", "ds_total_size": 20787903867, "ds_files_count": 817, "ds_format": "TIFF", "ds_space_res": "1km", "ds_time_res": "月", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "c815113a-5611-4d0b-ba39-88d5021a513a.png", "ds_thumb_from": 0, "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 09:56:18", "last_updated": "2025-06-30 16:19:15", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.ZENODO.DB6517.2024", "i18n": { "en": { "title": "1 km Monthly Precipitation Dataset for China from 1952 to 2019 (ChinaClim_timeseries)", "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.", "ds_quality": "
The research results indicate that the average root mean square error of monthly precipitation in ChinaClim time series is 7.502-52.307 millimeters, respectively. Compared with the climate surface of Peng Dehuai and CHELSAcruts, the R2 of precipitation elements increased by about 7%, while RMSE and MAE decreased by about 17%.", "ds_ref_way": "", "ds_abstract": "
ChinaClim timeseries is a monthly precipitation dataset from 1952 to 2019 in China, with a spatial resolution of 1km. The data is generated by overlaying monthly anomaly surfaces and baseline climatological surfaces (ChinaClim baseline) using 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 monthly precipitation data (ChinaClim time series) for China from 1952 to 2019. The precipitation ratio was calculated by the ratio and difference between the original time series of the meteorological station and the 30-year normal value. Combining the longitude, latitude, altitude, distance to the nearest coast, satellite driven anomaly (ratio), CRU anomaly (ratio), and 30-year normal value of each meteorological station, based on their geographical coordinates, the TPS model was applied to generate a monthly precipitation anomaly plane from 1952.01 to December 2019. For the monthly anomaly/ratio from 1952 to 2019, different variable combinations (longitude, latitude, altitude, distance to the nearest coast, CRU anomaly (ratio), and 30-year normal value) were used to construct seven model formulas (Table S2), and the optimal model was selected by the minimum RMSE value of the multi-year (1952-2019) average to fit the precipitation anomaly plane from 1952 to 1997. In the remaining time, we constructed two model formulas based on the optimal model in step (3). These two models add satellite data (TRMM ratio and LST anomaly) as independent spline variables or linear covariates. 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": [ "月降水", "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" }