长期气候数据和高质量、高分辨率的基线气候学表面对气候学、生态学和环境科学等多个领域至关重要。在此，我们创建了一个全新的基线气候学表面（ChinaClim_baseline），中国气候基线（ChinaClim_baseline）采用卫星数据（TRMM3B43 和 MODIS LST）和 2000 多个气象站的最佳 TPS 插值，是空间分辨率为 1km 的全新、高质量的中国气候基线表面。数据包括最近整整三十年（1981-2010 年）的月平均降水量和气温（平均气温、最高气温和最低气温）。降水和气温的比例因子分别为 0.01 和 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": "
使用 R 软件包 \"fields \"中的薄板样条曲线（TPS）对多年（1980-2010 年）的降水量和气温月平均值进行插值。具体而言，基于十倍空间分层交叉验证方法生成ChinaClim_baseline的过程可以描述如下：为了确保每个气候区域有足够的训练和测试数据来构建和验证模型，同时减少空间自相关，将每个气候区域的气象站分为10个倍。除了经度、纬度和海拔高度外，我们还根据每个气象站的地理坐标获得了到最近海岸的距离，以及卫星驱动的变量（TRMM 和 LST）。我们计算了卫星驱动变量（TRMM 和 LST）与现场观测之间的差异值，并设置阈值（±3 SD 表示温度;±4 SD 表示降水）以检测异常值。同样，我们还检查了台站报告的高程与从 1 km 空间分辨率的高程栅格数据获得的高程之间的对应关系。值得注意的是，根据Hutchinson（1995）的比例建议，高程（m）除以1000，降水量在拟合前按照Hutchinson和Xu（2013）的建议进行平方根变换。我们从每个气候区域随机选择了9个折叠的气象站，并将它们整合到一个新的训练数据集中。其余数据集被合并为测试数据集，以验证模型的性能。尝试使用不同的变量组合在每个气候区域每个月使用11个模型来构建TPS模型（表S1中描述的关于经度、纬度、海拔、到最近海岸的距离以及TRMM和LST的模型公式）。通过识别均方根误差 （RMSE） 最小的 TPS 模型，选择每个气候区每个月的最佳 TPS 模型，使用整个数据集生成四个气候区中每个气候区的最终表面。最后，通过反距离加权方法将这些曲面组合在一起。", "ds_quality": "
研究结果表明，ChinaClim_baseline 在四个气候区的表现优异，降水和温度要素估计的均方根误差分别为 1.276-28.439 毫米和 0.310-2.040 ℃。ChinaClim_baseline 与 WorldClim2 和 CHELSA 的相关性较高，但存在明显的空间差异。", "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": 6343161696, "ds_files_count": 2, "ds_format": "tif", "ds_space_res": "1km", "ds_time_res": "", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "944ab852-bec7-4a45-8694-48bd4a0cbaaf.jpg", "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 09:56:03", "last_updated": "2025-06-30 16:19:14", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.ZENODO.DB6511.2024", "i18n": { "en": { "title": "A Brand-New and High-Quality Baseline Climatology Surface for China (ChinaClim_baseline)", "ds_format": "tif", "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 ChinaClim baseline performs well in four climate zones, with root mean square errors of 1.276-28.439 millimeters and 0.310-2.040 ℃ for precipitation and temperature element estimates, respectively. The correlation between ChinaClimbaseline and WorldClim2 and CHELSA is high, but there are significant spatial differences.", "ds_ref_way": "", "ds_abstract": "
Long term climate data and high-quality, high-resolution baseline climatological surfaces are crucial for multiple fields such as climatology, ecology, and environmental science. Here, we have created a new baseline climatological surface (ChinaClim_baseline), which uses satellite data (TRMM3B43 and MODIS LST) and the best TPS interpolation from over 2000 meteorological stations. It is a new and high-quality Chinese climate baseline surface with a spatial resolution of 1km. The data includes the monthly average precipitation and temperature (average temperature, maximum temperature, and minimum temperature) for the past thirty years (1981-2010). The proportional factors of precipitation and temperature are 0.01 and 0.1, respectively.
", "ds_time_res": "", "ds_acq_place": "China", "ds_space_res": "1km", "ds_projection": "", "ds_process_way": "Interpolate the monthly average precipitation and temperature values for multiple years (1980-2010) using thin plate spline curves (TPS) from the R software package \"fields\". Specifically, the process of generating ChinaClim_baseline based on the ten fold spatial stratification cross validation method can be described as follows: In order to ensure that each climate region has sufficient training and testing data to construct and validate the model, while reducing spatial autocorrelation, the meteorological stations in each climate region are divided into ten fold intervals. In addition to longitude, latitude, and altitude, we also obtained the distance to the nearest coast based on the geographic coordinates of each weather station, as well as satellite driven variables (TRMM and LST). We calculated the difference values between satellite driven variables (TRMM and LST) and field observations, and set a threshold (± 3 SD represents temperature; ± 4 SD represents precipitation) to detect outliers. Similarly, we also examined the correspondence between the elevation reported by the station and the elevation obtained from the elevation grid data with a spatial resolution of 1 km. It is worth noting that according to Hutchinson's (1995) proportional suggestion, the elevation (m) is divided by 1000, and the precipitation is square root transformed according to Hutchinson and Xu's (2013) suggestion before fitting. We randomly selected 9 folded weather stations from each climate region and integrated them into a new training dataset. The remaining datasets were merged into a test dataset to validate the performance of the model. Attempt to construct a TPS model using 11 models per month in each climate region using different combinations of variables (as described in Table S1 for longitude, latitude, altitude, distance to nearest coast, and model formulas for TRMM and LST). By identifying the TPS model with the minimum root mean square error (RMSE), select the best TPS model for each climate zone each month, and use the entire dataset to generate the final surface for each of the four climate zones. Finally, these surfaces are combined together using the inverse distance weighting method.", "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": [ "中国", "新基线气候表面", "1km", "高质量" ], "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" }