本数据集包含我国典型积雪区9条积雪观测线路的2017-2021年4个积雪季共38次901个剖面的积雪深度、雪粒径、雪层温度及雪密度数据（其中3号线还包括2019-2020年的观测数据，4号线包括2019-2020年、2020-2021年的数据）。
\n同时，还有2017-2018年度的南方线和三江平原及松嫩平原的观测。9条积雪观测线路分别位于我国新疆天山以北、天山以南、青海、西藏、内蒙、东北以及华东地区，于每个雪季的积累期、稳定期和消融期进行观测。数据集命名规则、时间范围及线路经纬度等见说明文档。
", "ds_source": "数据主要来自野外观测人员的人工观测
", "ds_process_way": "数据集的主要观测因子包括积雪湿度、硬度、雪压、积雪密度、温度、含水量、介电常数、积雪粒径等。
\n其中，积雪湿度和硬度首先通过人工判识进行判定，两个指标都分为四个等级。之后，用硬度计进行积雪硬度的精确测定，将硬度计轻插入雪层，记录表盘读数，测量3次，并记录平均值。
\n积雪深度通过量尺测定，空气温度和雪层温度通过水银温度计测量。积雪密度、含水量和介电常数由snowfork测定，首先将积雪按5cm进行自下而上的分层，将snowfork的雪叉平稳插入每个分层的中部，通过手柄操作读取其密度、含水量，并保存介电常数，每层进行三次读数。
\n积雪密度还通过雪铲和雪筒观测，两者的观测原理相似。其中，雪铲观测首先将雪铲平行插入一定深度范围的雪层，将雪铲装满积雪，进行现场称重，每层积雪重复三次操作。雪筒观测是将雪筒插入整个雪层，并对雪铲和装入的积雪进行称重。需要说明的是，snowfork、雪铲在每个雪层，雪筒在整层积雪均进行了三次观测，若三次观测中有一次与其它观测数值差异较大，则进行加测，并最终记录差异较小的三次结果。
\n除此之外还进行了积雪粒径的分层观测。首先对积雪进行分层，在每层积雪中随机采样3-5个积雪粒子，通过国际学分类首先判定并记录其积雪类型，之后通过数码显微镜对积雪粒径进行微距拍照。后期测量其积雪粒径的长轴和短轴半径。部分积雪粒径数据通过icecube观测得到。观测步骤为，通过软件的输入值，自动计算积雪粒径大小。
", "ds_quality": "首先在野外寻找合适的观测样地，进行各要素的野外观测，之后进行数据质量控制。
\n数据质量控制阶段分两步：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数据的观测点数目及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判断异常值并进行剔除，另一方面对数据的存储方式、数据格式进行统一与规范。第二阶段是数据提供者对其它组的数据进行交叉检查，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，数据质量控制的数据自检表及数据分析图一并存于数据集文件夹中。
\n最后整合各所有数据，得到完整、准确、格式一致的线路数据集。
", "ds_acq_start_time": "2017-01-01 00:00:00", "ds_acq_end_time": "2021-04-30 00:00:00", "ds_acq_place": "新疆天山以北、天山以南、青海、西藏、内蒙及东北地区", "ds_acq_lon_east": 134.00611111111112, "ds_acq_lat_south": 27.653333333333332, "ds_acq_lon_west": 80.75916666666667, "ds_acq_lat_north": 53.46055555555556, "ds_acq_alt_low": 115.0, "ds_acq_alt_high": 4800.0, "ds_share_type": "apply-access", "ds_total_size": 30957208219, "ds_files_count": 15609, "ds_format": "xlsx,jpg(tiff),shp(kmz)", "ds_space_res": null, "ds_time_res": "日、时、分", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "e3363a27-dfdd-418b-9be5-3663d9af5fbd.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": "", "subject_codes": [ "170.4510" ], "quality_level": 3, "publish_time": "2021-10-21 17:53:20", "last_updated": "2023-03-06 12:55:38", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.ncdc.isnow.db2524.2022", "license": null, "i18n": { "en": { "title": "snow survey dataset from measurement routes in the typical regions of China during 2017-2021", "ds_format": "xlsx,jpg(tiff),shp(kmz)", "ds_source": " <pre><code>\nThe data are mainly from the manual observation of field observers
", "ds_quality": " <pre><code>\nFirst, find suitable observation sample plots in the field, carry out field observation of various elements, and then carry out data quality control.
\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 number of observation points and observation fields of data meet the project requirements. On the one hand, abnormal values are judged and eliminated through numerical diagrams. On the other hand, the data storage mode and data format are unified and standardized. The second stage is for data providers to cross check the data of other groups, review the integrity and accuracy of data again, and integrate the data of different regions. Among them, the data self inspection table and data analysis diagram 1 of data quality control coexist in the data set folder.
\nFinally, integrate all the data to obtain a complete, accurate and consistent line data set.
", "ds_ref_way": "", "ds_abstract": " <pre><code>\nThis data set contains the data of snow depth, snow particle size, snow layer temperature and snow density of 901 profiles in 4 snow seasons from 2017 to 2021 for 9 snow observation lines in typical snow areas in China (among which line 3 also includes the observation data from 2019 to 2020, and line 4 includes the data from 2019 to 2020 and 2020 to 2021).
\nAt the same time, there are observations of the southern line, Sanjiang Plain and Songnen Plain in 2017-2018. Nine snow observation lines are located in the north of Tianshan Mountain, the south of Tianshan Mountain, Qinghai, Tibet, Inner Mongolia, Northeast China and East China respectively, and are observed in the accumulation period, stability period and ablation period of each snow season. See the description document for the naming rules, time range, longitude and latitude of the data set.
", "ds_time_res": "日、时、分", "ds_acq_place": "North of Tianshan Mountain, south of Tianshan Mountain, Qinghai, Tibet, Inner Mongolia and Northeast China", "ds_space_res": "", "ds_projection": "", "ds_process_way": " <pre><code>\nThe main observation factors of the data set include snow humidity, hardness, snow pressure, snow density, temperature, water content, dielectric constant, snow particle size, etc.
\nAmong them, snow humidity and hardness are first determined by manual identification, and the two indicators are divided into four levels. After that, use the hardness tester to accurately measure the snow hardness, insert the hardness tester into the snow layer, record the dial reading, measure it for 3 times, and record the average value.
\nThe depth of snow is measured by measuring ruler, and the air temperature and snow layer temperature are measured by mercury thermometer. Snow density, water content and dielectric constant are measured by snowfork. Firstly, the snow is layered from bottom to top according to 5cm, the snow fork of snowfork is smoothly inserted into the middle of each layer, the density and water content are read through handle operation, the dielectric constant is saved, and three readings are made for each layer.
\nSnow density is also observed by snow shovel and snow tube, and their observation principles are similar. Among them, for snow shovel observation, firstly, insert the snow shovel in parallel into the snow layer within a certain depth range, fill the snow shovel with snow, weigh it on site, and repeat the operation for three times for each layer of snow. Snow tube observation is to insert the snow tube into the whole snow layer and weigh the snow shovel and loaded snow. It should be noted that snowfork and snow shovel have made three observations on each snow layer, and the snow cylinder has made three observations on the whole layer of snow. If one of the three observations is significantly different from other observations, additional measurements shall be made, and the three results with small differences shall be recorded finally.
\nIn addition, the stratification observation of snow particle size was also carried out. Firstly, the snow is stratified, and 3-5 snow particles are randomly sampled in each layer of snow. The snow type is first determined and recorded through international classification, and then the snow particle size is photographed through digital microscope. The major axis and minor axis radii of snow particle size were measured in the later stage. Some snow particle size data were observed by icecube. The observation step is to automatically calculate the snow particle size through the input value of the software.
", "ds_ref_instruction": " " } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "CC BY 4.0", "ds_topic_tags": [ "积雪线路", "积雪深度", "雪粒径", "雪层温度", "积雪密度" ], "ds_subject_tags": [ "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "新疆天山以北", "天山以南", "西藏", "内蒙及东北地区", "青海省" ], "ds_time_tags": [ 2017, 2018, 2019, 2020, 2021 ], "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" }