<p>This dataset contains snow depth, snow particle size, snow layer temperature and snow density data from 38 times and 901 sections of 9 snow observation lines in typical snow cover areas in my country during the four snow cover seasons from 2017 to 2021 (Line 3 also includes observation data from 2019 to 2020, and Line 4 includes data from 2019 to 2020 and 2020 to 2021). <p>At the same time, there are also observations on the southern line and the Sanjiang Plain and Songnen Plain in 2017-2018. Nine snow observation lines are located north of Tianshan Mountains, south of Tianshan Mountains, Qinghai, Xizang, Inner Mongolia, Northeast China and East China in Xinjiang, China, and observe during the accumulation period, stabilization period and ablation period of each snow season. See the description document for the naming rules of the dataset, time range, and line latitude and longitude.
The data mainly comes from manual observations by field observers.
The main observation factors in the data set include snow humidity, hardness, snow pressure, snow density, temperature, water content, dielectric constant, snow particle size, etc.
Among them, the humidity and hardness of snow are first determined through manual identification, and both indicators are divided into four levels. After that, a hardness tester was used to accurately measure the hardness of the snow. The hardness tester was lightly inserted into the snow layer, the dial readings were recorded, measured 3 times, and the average value was recorded.
Snow depth is measured by a ruler, and air and snow temperature are measured by mercury thermometers. The density, water content and dielectric constant of the snow are measured by snowfork. First, the snow is layered from bottom to top according to 5 cm. Snowfork's snow fork is smoothly inserted into the middle of each layer, and its density and water content are read through handle operation., and the dielectric constant is preserved, and each layer is read three times.
Snow density is also observed through snow shovels and snow canisters, and the observation principles of both are similar. Among them, snow shovel observation first inserts the snow shovel into the snow layer in parallel with a certain depth range, fills the snow shovel with snow, carries out on-site weighing, and repeats the operation three times for each layer of snow. Snow tube observation involves inserting a snow tube into the entire snow layer and weighing the snow shovel and the loaded snow. It should be noted that snowfork, snow shovel, and snow barrel made three observations on each snow layer, and snow barrel made three observations on the entire snow layer. If one of the three observations was significantly different from other observations, additional measurements were carried out and finally Three results with small differences were recorded.
In addition, stratified observations of the grain size of the snow cover were also carried out. First, the snow is layered, and 3-5 snow particles are randomly sampled in each layer of snow. The snow type is first determined and recorded through international classification. Then, the snow particle size is taken macro photos through a digital microscope. Later, the radius of the major axis and minor axis of the snow grain size was measured. Some snow particle size data were obtained through icecube observation. The observation step is to automatically calculate the particle size of snow snow based on the input values of the software.
First, find suitable observation sample sites in the field to conduct field observations of various elements, and then carry out data quality control.
The data quality control stage is divided into two steps: First, the data provider checks the integrity and accuracy of the data. The data integrity check mainly checks whether the number of observation points and observation fields of the data meet the project requirements. On the one hand, the data accuracy check determines and eliminates abnormal values through numerical illustrations, and on the other hand, it unifies and standardizes the data storage method and data format. The second stage is for the data provider to cross-check data from other groups, review the integrity and accuracy of the data again, and integrate data from different regions. Among them, the data self-test table and data analysis chart for data quality control are stored in the data set folder.
Finally, all data are integrated to obtain a complete, accurate and consistent line data set.
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CSTR:11738.11.ncdc.isnow.db2524.2022
10.12072/ncdc.I-SNOW.db0007.2021