Import NetCDF Data

Feature Description

In SuperMap iDesktopX, you can import NetCDF(*.nc) files as raster/image datasets, displaying all variables using one dimension (e.g., time).

Supports importing as raster datasets to view variable values;
Enables dynamic playback of imported single bands dataset layers to visualize changes between variables.

Feature Entry

Start Tab->Data Processing Group->Data Import->Grid/Image Group->NetCDF.
Workspace Manager->Datasource Context Menu->Import Dataset....
Toolbox->NetCDF Tools->Import NetCDF.

Parameter Description

Source File Info: Specifies the NetCDF(*.nc) file to be imported.
Result Data: Selects the datasource for storing result datasets and specifies their names.
Supports setting encoding type and import mode. For public parameter descriptions, see Common Parameters.
Dataset Type: Sets the type of imported data. Available result types for raster data import: Image Dataset and Raster Dataset.
Transformation Parameters
- Band Import Mode: Provides Single Bands and Multi-band options.
  - Single Bands: Generates single band datasets based on the third dimension (excluding X/Y coordinates or lat/lon dimensions) in NetCDF files. E.g., for global January temperature data from 2000-2020 with lon, lat, and time dimensions, creates 20 single band datasets representing annual temperatures.
    
    Note:
    
    When X/Y variables contain zero values, no features will be created at those positions. However, if zeros appear in variables, null values will fill those positions.
  - Multi-band: Creates volumetric raster datasets using the third dimension (e.g., height) as band dimension. Each band represents variable values at specific heights.
- Value: Specifies the NetCDF variable for assigning pixel values (e.g., wind speed, temperature, precipitation).

Application Example

Simulated atmospheric model data shows global January temperatures over 20 years. The 3D variable "tmin" represents minimum winter temperatures. With lon, lat, and year dimensions, import as single bands data and perform layer playback to achieve following visualization: