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An overview of multidimensional raster data

Multidimensional data represent data captured at multiple times and/or multiple depths. These data types are commonly used in atmospheric, oceanographic and earth sciences. Multidimensional raster data can be captured by satellite observations where data are collected at certain time intervals, or generated from numerical models where data are aggregated, interpolated or simulated from other data sources.

The common storage formats for multidimensional raster are netCDF, GRIB, and HDF. Oceanographic data are often stored in netCDF format (.nc), weather data in GRIB format, and NASA often uses HDF format to store scientific data. These multidimensional formats share common capabilities for storing multiple variables, with each variable being a multidimensional array . For example, a netCDF file can store temperature, humidity, and wind speed for every month from the years 2010 to 2014, and at elevations of 0, 1, and 10 meters, respectively. See Multidimensional raster types for more information about these types of data.

ArcGIS Prois capable of managing, visualizing, and processing multidimensional raster data, and publishing them as a web service. There are two ways to bring your multidimensional raster data into ArcGIS Pro: as a multidimensional raster layer and as a multidimensional mosaic dataset.

Adding a multidimensional raster layer to the map view allows you to quickly display or examine your variables in one file. When you work with large amounts of multidimensional data with multiple variables, the multidimensional mosaic dataset is an effective way to store and manage your data, and perform data analysis.

To add the multidimensional raster layer, use the Add Data > Multidimensional Raster Layer option on the Map tab.

Add multidimensional data layer

By default, the system applies the Multidimensional Raster processing template and creates one multidimensional raster layer for each selected variable. The new raster layer contains the multidimensional information from the variable, which can be visualized, analyzed and shared.

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