本数据集包含我国典型积雪区的6个超级站2017-2020年3个积雪季的人工观测及自动观测的积雪特性数据集。其中，人工观测数据集包括逐日的积雪深度、密度、积雪粒径、积雪硬度、积雪形态等数据。自动观测包括积雪深度、雪水当量、积雪反照率、液态水含量、积雪密度、风温湿压等要素。
\n6个超级站分别为阿勒泰站、天山雪崩站、甘德站、错那站、黑河站以及东北森林站。其中，每个超级站的自动观测和人工观测分别按年度存放于不同文件夹。其中，逐日人工观测数据存放于一个文件夹，包含环境照片、粒径照片、记录表格以及原始记录表扫描档。自动观测文件夹包含各观测仪器的表格、以及数据分析图。
\n数据集共包含28个文件夹、886个数据表，共974340条记录。数据集命名规则、时间范围及站点经纬度见说明文档。
", "ds_source": "本数据集主要来自气象站点的人工观测和各单位架设的自动观测仪器的数据。
", "ds_process_way": "数据的处理过程主要包含站点数据收集、规范化整理、异常值剔除等。
", "ds_quality": "数据质量控制阶段分为两步：首先数据提供者对数据进行了完整性、准确性检查。数据完整性检查主要检查各站点数据的时间范围和观测字段是否达到项目要求。数据准确性检查一方面通过数值图示，通过人工经验判断异常值并进行剔除，另一方面对数据的存储方式、格式进行统一与规范。第二阶段是不同数据提供者对其它数据进行交叉检查，再次审核数据的完整性和准确性，并对不同区域的数据进行整合。其中，数据自检表及数据分析图同时存于数据文件夹中。
", "ds_acq_start_time": "2017-01-01 00:00:00", "ds_acq_end_time": "2020-12-31 00:00:00", "ds_acq_place": "阿勒泰站、天山雪崩站、黑河站、甘德站、错那站和东北森林站", "ds_acq_lon_east": 128.47, "ds_acq_lat_south": 27.990000000000002, "ds_acq_lon_west": 84.4, "ds_acq_lat_north": 48.18, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "apply-access", "ds_total_size": 7736538635, "ds_files_count": 9829, "ds_format": "xlsx，jpg", "ds_space_res": null, "ds_time_res": "日、时、分", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "9bc06803-d5db-4f1d-a266-0b01235a9f58.png", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "aba68fe5-65d3-41b1-b036-bc274a834b5e", "ds_serv_man": "李红星", "ds_serv_phone": "0931-4967592", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "10.12072/ncdc.I-SNOW.db0013.2021", "subject_codes": [ "170.4510" ], "quality_level": 3, "publish_time": "2021-10-21 17:53:20", "last_updated": "2023-03-06 12:59:09", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.ncdc.isnow.db2522.2022", "i18n": { "en": { "title": "snow survey dataset from super statins of China during 2017-2020", "ds_format": "xlsx，jpg", "ds_source": " <pre><code>\nThis data set mainly comes from the manual observation of meteorological stations and the data of automatic observation instruments erected by various units.
", "ds_quality": " <pre><code>\nThe data quality control stage is divided into two steps: first, the data provider checks the integrity and accuracy of the data. Data integrity check mainly checks whether the time range and observation fields of data at each station meet the project requirements. On the one hand, check the data accuracy, judge and eliminate abnormal values through numerical diagrams and manual experience, on the other hand, unify and standardize the storage mode and format of data. In the second stage, different data providers cross check other data, review the integrity and accuracy of the data again, and integrate the data in different regions. The data self-test table and data analysis diagram are stored in the data folder at the same time.
", "ds_ref_way": "", "ds_abstract": " <pre><code>\nThis data set contains the snow characteristics data set of manual observation and automatic observation of 6 Super stations in typical snow areas in China in three snow seasons from 2017 to 2020. Among them, the manual observation data set includes daily snow depth, density, snow particle size, snow hardness, snow form and other data. Automatic observation includes snow depth, snow water equivalent, snow albedo, liquid water content, snow density, wind temperature and wet pressure.
\nThe six Super stations are Altay station, Tianshan avalanche station, Gande station, Cuona station, Heihe station and northeast forest station. Among them, the automatic observation and manual observation of each super station are stored in different folders annually. The daily manual observation data are stored in a folder, including environmental photos, particle size photos, record forms and scanning files of original record forms. The automatic observation folder contains tables for each observation instrument and data analysis diagrams.
\nThe dataset contains 28 folders, 886 data tables and 974340 records in total. See the description document for the naming rules, time range and longitude and latitude of the data set.
", "ds_time_res": "日、时、分", "ds_acq_place": "Altay station, Tianshan avalanche station, Heihe station, Gande station, Cuona station and Northeast Forest Station", "ds_space_res": "", "ds_projection": "", "ds_process_way": " <pre><code>\nThe data processing process mainly includes site data collection, standardized sorting, outlier elimination, etc.
", "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": [ "雪深", "超级站", "积雪密度", "气象要素" ], "ds_subject_tags": [ "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "阿勒泰站", "天山雪崩站", "黑河站", "甘德站", "错那站" ], "ds_time_tags": [ 2017, 2018, 2019, 2020 ], "ds_contributors": [ { "true_name": "李兰海", "email": "lilh@ms.xjb.ac.cn", "work_for": "中国科学院新疆生态与地理研究所", "country": "中国" }, { "true_name": "车涛", "email": "chetao@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" }, { "true_name": "刘艳", "email": "liuyan@idm.cn", "work_for": "中国气象局乌鲁木齐沙漠气象研究所", "country": "中国" }, { "true_name": "李晓峰", "email": "lixiaofeng@iga.ac.cn", "work_for": "中国科学院东北地理与农业生态研究所", "country": "中国" }, { "true_name": "肖建设", "email": "", "work_for": "青海省气象科学研究所", "country": "中国" }, { "true_name": "除多", "email": "chu_d22@hotmail.com", "work_for": "西藏高原大气环境科学研究所", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "戴礼云", "email": "dailiyun@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" } ], "ds_managers": [ { "true_name": "车涛", "email": "chetao@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" } ], "category": "积雪" }