原始资料为中国区域地面气象要素驱动数据集降雪驱动数据、中国气象局气象站点雪深数据、国产再分析CRA40雪深产品，所有原始资料精度可靠。利用统计降尺度方法，得到新疆地区2000-2020年多年平均秋季、冬季、春季雪深分布，并结合野外观测验证完成。
", "ds_source": "中国区域地面气象要素驱动数据集数据来源于“国家青藏高原科学数据中心”(http://data.tpdc.ac.cn），中国气象局气象站点数据和再分析数据来源于国家气象科学数据中心（http://data.cma.cn/site/index.html）野外考察积雪特性数据为观测数据，自主产生。
", "ds_process_way": "由于现有气象站点所在海拔最高为3500m左右，且站点在诸如帕米尔、昆仑山等地极其缺乏，而被动微波遥感反演的雪深产品则空间分辨率较粗（25km），因此对于认识新疆积雪资源的分布情况需要更高空间分辨率产品的支持。子课题通过分析气象站点和野外积雪特性观测数据可知，在中尺度模拟尺度上，降雪是直接影响积雪的因子，因此利用高精度降雪驱动数据和站点雪深数据，结合现有国产再分析雪深产品，通过统计降尺度建立它们之间的关系，在此基础上得到多年平均秋季、冬季、春季新疆积雪资源分布变化图，与传统的气象站点雪深数据直接插值相比，可以得到高海拔地区更合理的雪深分布。具体步骤将高精度降雪驱动场数据和气象站点雪深数据作为输入变量，将CRA40再分析雪深数据作为输出变量，通过线性多元回归对其进行训练，之后通过所生成的回归模型，对CRA40雪深进行统计降尺度。
", "ds_quality": "利用野外实测雪深数据进行交叉验证。交叉验证结果证明本数据集合格。
", "ds_acq_start_time": null, "ds_acq_end_time": null, "ds_acq_place": "新疆", "ds_acq_lon_east": null, "ds_acq_lat_south": 33.796112, "ds_acq_lon_west": 73.76056, "ds_acq_lat_north": 49.32389, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "apply-access", "ds_total_size": 955172, "ds_files_count": 16, "ds_format": "栅格", "ds_space_res": null, "ds_time_res": "多年平均", "ds_coordinate": "CGCS2000", "ds_projection": "", "ds_thumbnail": "0887063e-443e-48d5-8272-e17e7f5d777e.jpg", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "a5877b42-96ea-4f13-af7e-246f355413d6", "doi_value": "", "subject_codes": [ "170.45" ], "quality_level": 1, "publish_time": "2024-09-25 12:40:43", "first_publish_time": "2024-09-25 12:40:43", "last_updated": "2025-05-15 09:38:17", "protected": false, "protected_to": null, "lang": "zh", "cstr": "33110.11.XIEG.xjsedata.2022.00000096", "license": null, "extra": null, "files_shape": [ { "name": "实体数据", "size": null, "is_dir": true }, { "name": "附件", "size": null, "is_dir": true } ], "features": null, "data_level": 0, "i18n": { "en": { "title": "Data Set of Distribution Division of Snow Resources in Xinjiang (2000-2020)", "ds_abstract": "The raw data are snowfall driven data from the China Regional Surface Meteorological Element Driven Data Set, snow depth data from meteorological stations of China Meteorological Administration, and domestic reanalysis CRA40 snow depth products. All raw data have reliable accuracy. Using the statistical downscaling method, the multi-year average autumn, winter and spring snow depth distribution in Xinjiang from 2000 to 2020 was obtained, and verified by field observations.
", "ds_source": "The data of the China regional surface meteorological element-driven dataset comes from the \"National Qinghai-Tibet Plateau Scientific Data Center\"(http://data.tpdc.ac.cn), and the meteorological station data and reanalysis data of the China Meteorological Administration come from the National Meteorological Science Data Center (http://data.cma.cn/site/index.html) Field investigation snow cover characteristics data are observation data and are independently generated.
", "ds_process_way": "Since the maximum altitude of existing meteorological stations is about 3500 meters, and stations are extremely scarce in places such as Pamirs and Kunlun Mountains, and the snow depth products retrieved by passive microwave remote sensing have a relatively thick spatial resolution (25km), understanding The distribution of snow resources in Xinjiang requires the support of higher spatial resolution products. The sub-topic analyzes observation data of snow cover characteristics at meteorological stations and field stations. It is known that on the mesoscale simulation scale, snowfall is a factor that directly affects snow cover. Therefore, using high-precision snow-driving data and station snow depth data, combined with existing domestically produced re-analysis of snow depth products, establishing the relationship between them through statistical downscaling, and on this basis, obtaining multi-year average snow cover resource distribution change maps in Xinjiang in autumn, winter and spring. Compared with traditional direct interpolation of snow depth data at meteorological stations, A more reasonable snow depth distribution in high altitude areas can be obtained. Specific steps use high-precision snowfall driving field data and meteorological station snow depth data as input variables, use CRA40 reanalysis snow depth data as output variables, train them through linear multiple regression, and then use the generated regression model to evaluate CRA40 snow depth.
", "ds_quality": "Cross-verification was carried out using field measured snow depth data. The cross-validation results demonstrate that this dataset is qualified.
", "ds_acq_place": "Xinjiang", "ds_format": "grid", "ds_time_res": "average annual" } }, "license_type": null, "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": [ "雪深" ], "ds_subject_tags": [ "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "新疆" ], "ds_time_tags": [ 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020 ], "ds_contributors": [ "李倩" ], "ds_meta_authors": [ "李倩" ], "ds_managers": [ "李倩" ], "category": "冰雪资源" }