## 2 Data and Methods
### Application of GIS Technologies for Climatic Atlas Compilation  
#### 2.4.1 Main Data-Layers  
The Golden Software Surfer (version 4.3) [24; 25] and ESRI ArcGIS (ArcMap), (version 10.8) [26; 27] software packages were chosen as the main tools for the compilation of the atlas maps. The Surfer has a considerably large set of tools for primary data processing, including a user-friendly interface and extensive interpolation capabilities. In  
Figure 2: MERRA-2 observation points laid over the studied area. turn, the ArcMap software package is designed to solve a wide range of tasks including the transformation of point data on a regular grid (grid-data model) and irregular grid (Triangular irregular network--TIN data models) [28; 29] into raster layers. Subsequently, these data are prepared for publication as cartographic services using the geoportal approach [30] or export as formatted map. In our work, the process of data processing and preparation is shown in Figure 3:  
Further, each block shown in Figure 3 will be discussed. The preliminary data processing included the so-called \"cleaning\", i.e., data outside the terrestrial boundaries are removed for several categories of data, e.g., for soil temperature or soil moisture, since the latter can seriously distort the model calculations. For this purpose, the authors developed a software module in Python 2.7 using Python ArcPy and Python.os libraries. The main processing tool is the Clip tool built into the ArcGIS toolkit for raster data processing, which allows for cropping a single raster layer to the specified contour in Figure 4 [31; 32]. The module has a graphical interface and was integrated within the ArcMap package. The input parameters include selection of the directory of the processed raster data (in.tif format), specifying the vector file with contour mask in shape-file format (.shp) for cropping the data, and the directory for saving the results. The software module processes all raster files one by one in the catalog using the Python.os library. Each layer is cropped by the Clip tool using the contour mask based on the coastline within the considered territory. The results are saved to a specified directory. The source code of this tool is available online [33]. To improve the quality of the output data, the initial model data representing a regular 0.5\({}^{\circ}\)-grid were interpolated to reach the 0.05\({}^{\circ}\) resolution (1101 \(\times\) 301 pixels) using krig  
Figure 4: The Clip tool operation principle. Figure 3: The data processing scheme for the atlas map compilation. The employed software is indicated in brackets. ing. This is a widely used gridding method aimed at producing maps from sparse and irregular data [34,35]. The options and parameters of the tool were selected as follows. The procedure type was the standard point kriging with a variogram estimation. The parameters of standard deviation were calculated automatically without deviation grids and external drift grids. The search options for data points for interpolating grid nodes were defined as follows: the number of sectors to search--4, maximum number of data to use--64 points, minimum number of data to use--8 points; search ellipse radius in data units--28.5, and angle 0\({}^{\circ}\). The interpolated data were then exported to a raster format (.flt) for further processing. This procedure was performed using Surfer. This tool is provided by other software packages, but the choice of the Surfer software was made due to advantages in interpolation procedures operating time and data export without loss of quality. Thus, the resulting data covers the territory within the geographical boundaries 60-75\({}^{\circ}\) N, 30-85\({}^{\circ}\) E and is shown in Figure 5.  
The georeferencing of the data in our case was determined by the choice of the map projection. At this stage, the conical Lambert projection (ESPG: 102027) was considered for the initial raster data [36,37]. The final maps of the atlas were also made in this projection.