在考虑地形信息的基础上，采用地理加权回归方法对粗分辨率(30′)的CRU数据集进行降尺度处理。获得了1941-021年祁连山气温(Ta)和降水数据集，利用气象站的观测数据对降尺度的5′(1km)分辨率数据集进行了评价。与原始CRU数据集相比，新数据集对Ta和降水的平均绝对误差分别降低了52.7%和4.9%。对Ta和降水的均方根误差分别降低了53.3%和10.4%。气温和降水的Nash-Sutcliffe效率系数分别提高了25.4%和9.5%。新的数据集可以为分析多时间尺度的气候变化趋势提供详细信息。该数据集将有助于潜在用户改进祁连山地区的气候监测、建模和环境研究。
\n1. 数据集命名
\nTa1941-2021month.nc
\nPre1941-2021month.nc
\n2. 属性信息
\n数据存储采用NetCDF格式。每个文件包含972个月的数据，需要4.69GB的存储空间。每个文件名表示文件中包含的数据。例如，文件名为Ta1941-2021month.nc包含1941年至2021年的月温度数据。NetCDF文件的总数为2,nc格式数据集的总大小约为9.38 GB。气温的单位是℃，降水的单位是mm。数据空间分辨率为1km。
", "ds_source": "(1)CRU数据集。从CRU TS v4.02数据集(http://www.cru.uea.ac.uk， 最近访问:2022年6月24日)获得1941年1月至2021年12月的月平均气温和降水，空间分辨率为30'。
\n(2)DEM数据。从http://www.gscloud.cn 下载30m空间分辨率的DEM数据。利用ArcGIS10.8.2软件提取坡度、坡向和地形起伏度图层。
\n(3)观测数据。
\n获取了祁连山地区8个国家级气象站(http://data.cma.cn/en) 和11个地方气象站的长期逐月气温和降水观测资料。
", "ds_process_way": "1.步骤\n
首先，准备0.5 '(1公里)和30 '分辨率的环境变量和原始的月尺度CUR数据。\n
其次，我们选择了与气温和降水相关的变量，命名为解释变量。\n
第三，将解释变量和原始CRU数据输入GWR模型，得到截距、残差和系数。\n
第四，对截距、残差和系数进行插值，得到1km的栅格层。然后将这些层与1公里分辨率的解释变量结合，通过GIS生成高分辨率的温度和降水数据。\n
（2）GWR模型\n
GWR模型是一个区域回归模型，由Chris等人(1996)首先提出。该模型被广泛应用于因变量与解释变量之间关系的动态特征和尺度依赖性研究。回归模型表示为:\n
𝑌_𝑗=𝛽_0 (𝑢_𝑗,𝑣_𝑗 )+∑1_(𝑖=1)^𝑝▒〖𝛽_𝑖 (𝑢_𝑗,𝑣_𝑗 ) 𝑋_(𝑖,𝑗 ) 〗+𝜖_𝑗,\n
式中(uj, vj)、βo (uj, vj)、βi (uj, vj)、εj分别为第t点的地理坐标、截距、斜率(回归系数)、回归残差;p表示环境变量的个数。截距是局部常数项的估计值，斜率是各变量系数的局部估计值，残差表示因变量的观测值与预测值之差。Yj表示因变量的第j个观测值;GWR的基本假设是，距离第j点越近的观测值对该位置的局部系数影响越大，该局部系数作为距离衰减函数，依赖于第j点到相邻点的距离。\n
（3) GWR 4.0软件\n
本文采用GWR 4.0软件(http://geoinformatics.wp.st-andrews.ac.uk/gwr) 建立GWR模型。", "ds_quality": "
数据精度：\n
与原始CRU数据集相比，新数据集对温度和降水的平均绝对误差分别降低了52.7%和4.9%。温度和降水的均方根误差分别下降了53.3%和10.4%。温度和降水的Nash-Sutcliffe效率系数分别提高了25.4%和9.5%。", "ds_acq_start_time": "1941-01-01 00:00:00", "ds_acq_end_time": "2021-12-31 00:00:00", "ds_acq_place": "祁连山", "ds_acq_lon_east": 104.2538888888889, "ds_acq_lat_south": 35.745555555555555, "ds_acq_lon_west": 93.24555555555555, "ds_acq_lat_north": 39.75416666666667, "ds_acq_alt_low": 1794.0, "ds_acq_alt_high": 5827.0, "ds_share_type": "open-access", "ds_total_size": 2143288293, "ds_files_count": 3, "ds_format": "NetCDF", "ds_space_res": "1km", "ds_time_res": "月", "ds_coordinate": "WGS84", "ds_projection": "", "ds_thumbnail": "06c88707-8015-4fe8-8c10-afbbc5ff0cf8.jpg", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "52b7b79b-860c-49a5-9083-9a70cf8bed5a", "ds_serv_man": "敏玉芳", "ds_serv_phone": "0931-4967596", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "10.12072/ncdc.NIEER.db2489.2022", "subject_codes": [ "170.15", "170.45" ], "quality_level": 3, "publish_time": "2022-11-29 10:10:43", "last_updated": "2023-12-05 11:30:39", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.ncdc.NIEER.db2489.2022", "i18n": { "en": { "title": "A multi-temporal scale temperature and precipitation dataset for the Qilian Mountains region (1941-2021)", "ds_format": "", "ds_source": "
(1) CRU datasets.The monthly mean temperature and precipitation were obtained for January 1941 to December 2021with a spatial resolution of 30′ from the CRU TS v4.02 dataset (http://www.cru.uea.ac.uk, last access: 24 June 2022).
\n(2) DEM data with the spatial resolution of 30 m were downloaded from http://www.gscloud.cn. The slope, aspect, and topographical relief layers were extracted by ArcGIS 10.8.2.
\n(3) Observations.
\nThe observed long-term monthly temperature and precipitation data across Qilian Mountains were obtained from eight national meteorological stations (http://data.cma.cn/en) and 11 local meteorological stations.
", "ds_quality": "Accuracy of Data：\n
Compared to the original CRU dataset, the new dataset shows a 52.7% and 4.9% reduction in mean absolute error for temperature and precipitation respectively. The root-mean-square errors for temperature and precipitation were reduced by 53.3% and 10.4%, respectively. The Nash-Sutcliffe efficiency coefficients for temperature and precipitation improved by 25.4% and 9.5%, respectively.", "ds_ref_way": "", "ds_abstract": "
Based on the consideration of topographic information, a downscaling of the coarse resolution (30′) CRU dataset was carried out using a geo-weighted regression method. Temperature (Ta) and precipitation datasets were obtained for the Qilian Mountains from 1941-021, and the downscaled 5′ (1km) resolution dataset was evaluated using observations from meteorological stations. Compared with the original CRU dataset, the new dataset shows a 52.7% and 4.9% reduction in mean absolute error for Ta and precipitation, respectively. The root-mean-square errors for Ta and precipitation were reduced by 53.3% and 10.4%, respectively. The Nash-Sutcliffe efficiency coefficients for temperature and precipitation have improved by 25.4% and 9.5% respectively. The new dataset can provide detailed information for the analysis of climate change trends over multiple time scales. The dataset will help potential users to improve climate monitoring, modelling and environmental studies in the Qilian Mountains region.
\n1. Name of Data
\nTa1941-2021month.nc
\nPre1941-2021month.nc
\n2.Data description of attribute items
\nThe data stored using the NetCDF format. Thus, each file contains 972 months of data and requires 4.69GB of storage space. Each file name indicates the data contained in the file, For example, the file named Ta1941-2021month. nc contains month temperature data from 1941 to 2021. The total number of NetCDF files is 2, and the total size of the dataset in nc format is approximately 9.38 GB.Temperature is measured in degrees Celsius and precipitation in millimeters. The spatial resolution of data is 1km.
", "ds_time_res": "月", "ds_acq_place": "Qilian Mountains", "ds_space_res": "1km", "ds_projection": "", "ds_process_way": "1. Processing\n
First, the environmental variables at 0.5′ (1 km) and 30′ resolutions and the original CUR data at monthly scales were prepared. \n
Second, we selected variables that correlate with tetemperature and precipitation, named as explanatory variables. \n
Third, the explanatory variables and original CRU data were inputted into the GWR model, and the intercepts, residuals, and coefficients are obtained. \n
Fourth, the intercepts, residuals, and coefficients were interpolated to obtain 1 km raster layers. These layers were then combined with explanatory variables at 1 km resolution to develop temperature and precipitation data with a high resolution.\n
2.GWRmodel, \n
The regional regression model, was first proposed by Chris et al. (1996). This model has been widely applied in research about the dynamic and scale-dependent characteristics of the relationships between the dependent and the explanatory variables (Foody, 2003). The regression model is expressed as follows:\n
𝑌_𝑗=𝛽_0 (𝑢_𝑗,𝑣_𝑗 )+∑1_(𝑖=1)^𝑝▒〖𝛽_𝑖 (𝑢_𝑗,𝑣_𝑗 ) 𝑋_(𝑖,𝑗 ) 〗+𝜖_𝑗,\n
where (uj, vj), βo (uj, vj), βi (uj, vj), and εj are the geographical coordinates, intercept, slope (regression coefficient), and regression residual at the jth point, respectively; and p denotes the number of environmental variables. The intercept is the estimate of the local constant term, the slope is the local estimate of coefficient for each variable, and the residual represents the difference between the observed and the predicted values of the dependent variable. Yj represents the jth observation of the dependent variable; and Xi,j is the jth observation of the ith independent variable. The basic assumption of the GWR is that an observation being closer to the jth point has a higher influence on the local coefficient for the location, which acts as a distance decay function that depends on the distance from the jth point to its adjacent points. \n
Reference：Chris, Brunsdon, A, Stewart, Fotheringham, Martin, E, & Charlton. (1996). Geographically Weighted Regression: A Method for Exploring Spatial Nonstationarity. Geographical Analysis.\n
(3) GWR 4.0 software\n
GWR 4.0 software (http:// geoinformatics.wp.st-andrews.ac.uk/gwr) was used to establish the GWR model in our study.", "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": [ "气温和降水数据集", "祁连山", "Nash-Sutcliffe效率系数" ], "ds_subject_tags": [ "大气科学", "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "祁连山" ], "ds_time_tags": [ 1941, 2021 ], "ds_contributors": [ { "true_name": "陈生云", "email": "sychen@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院,冰冻圈科学国家重点实验室,疏勒河源冰冻圈与生态环境综合监测研究站", "country": "中国" }, { "true_name": "戎战磊", "email": "rongzhl16@lzu.edu.cn", "work_for": "青海师范大学地理科学学院,青海省自然地理与环境过程重点实验室", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "陈生云", "email": "sychen@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院,冰冻圈科学国家重点实验室,疏勒河源冰冻圈与生态环境综合监测研究站", "country": "中国" }, { "true_name": "戎战磊", "email": "rongzhl16@lzu.edu.cn", "work_for": "青海师范大学地理科学学院,青海省自然地理与环境过程重点实验室", "country": "中国" } ], "ds_managers": [ { "true_name": "陈生云", "email": "sychen@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院,冰冻圈科学国家重点实验室,疏勒河源冰冻圈与生态环境综合监测研究站", "country": "中国" } ], "category": "气象" }