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Perform multidimensional raster analysis in ArcGIS Pro

Available with Image Analyst license.

In ArcGIS Pro, you can visualize and process scientific data for temporal and spatial analysis. In this changing world, it is important to work with and understand scientific data and to incorporate the data into GIS workflows.

You can work with scientific data in ArcGIS Pro using multiple formats: space-time cubes, multidimensional layers, mosaic datasets, and multidimensional raster datasets in Cloud Raster Format (CRF). In this tutorial, you will explore some of the ways you can process and display scientific data using multidimensional raster datasets.

First, you will add a multidimensional raster layer to ArcGIS Pro, depicting sea surface temperature from the National Center for Atmospheric Research (NCAR) Research Data Archive.

Next, you will use geoprocessing tools and charting to explore sea surface temperature change over time and discover warming anomalies in the ocean.


ArcGIS Pro 2.5 or above and the ArcGIS Image Analyst extension are required to complete this tutorial. For instructions on performing this workflow in ArcGIS Pro 2.4, download the ArcGIS Pro 2.4 tutorial PDF.

Add multidimensional data to your project

The data packaged for this tutorial is from the NCAR Research Data Archive. It is a netCDF file included in the Climate Forecast System Reanalysis (CFSR) product. It contains 35 years of monthly sea surface temperature data with a spatial resolution of 0.5 degrees.

  1. Download the tutorial data and save it to C:\SampleData\SST_tutorial.
  2. In ArcGIS Pro, create a project using the Map template and sign in to your ArcGIS Online account if necessary.
  3. On the Map tab, in the Layer group, click the Add Data drop-down menu and select Multidimensional Raster Layer.
  4. On the Add Multidimensional Raster Layers dialog box, under Input File, Mosaic Dataset or Image Service, click the Import Variables from file button and browse to C:\SampleData\SST_tutorial or where you downloaded the tutorial data. Select the file and click OK.
  5. Check the box next to the cfsrsst variable to select the sea surface temperature, and select Multivariate Multidimensional Raster as the Output Configuration. Leave the remaining default parameters and click OK. Zoom out to the full extent of the dataset.
  6. Right-click on the layer in the Contents pane and select Zoom to Layer.

    This netCDF file contains monthly sea surface temperature data across 36 years from 1980 to 2015.

    The netCDF layer displays sea surface temperature globally.
  7. Right-click on the layer again and select Properties. In the Layer Properties, open the Source menu, and expand the Multidimensional Info section.

    The variables and variable properties of the multidimensional dataset are listed. Expand the cfsrsst variable to see the dimension information. In this case, the sea surface temperature data is organized with a time dimension called StdTime (Standard Time), with an interval of 1 month from 1980 to 2015, for a total of 432 rasters.

  8. Click OK to close the Layer Properties.
  9. When you added the multidimensional raster layer, the Multidimensional tab appeared under Raster Layer in the ribbon. Click on the Multidimensional tab to view the capabilities available.

    The Multidimensional tab

  10. Use the StdTime drop-down list in the Current Display Slice group to display the monthly sea surface temperature for various months and years.

Process sea surface temperature data

As you saw in the layer properties, the data you are working with is monthly sea surface temperature. As a climate scientist, you may be interested in yearly maximum sea surface temperature, and in temperature anomalies.


The tools used in this section are in the Multidimensional Analysis toolset, under the Image Analyst toolbox. Alternatively, the Multidimensional Tools toolbox is a separate toolbox containing tools that allow you to manage multidimensional raster data.

  1. On the Multidimensional tab, in the Analysis group, click the Aggregate button to open the Aggregate Multidimensional Raster geoprocessing tool.

    This tool combines slices from an existing multidimensional dataset along a dimension to generate a new CRF multidimensional raster.

    If the ArcGIS Image Analyst extension has not been licensed for ArcGIS Pro, you cannot access these tools.
  2. Enter the parameters as follows:
    • Input Multidimensional
    • Variablescfsrsst [StdTime=432] ((null)) check
    • Output Multidimensional RasterYearlySST.crf
    • DimensionStdTime
    • Aggregation MethodMaximum
    • Aggregation DefinitionInterval Keyword
    • Keyword IntervalYearly
    • Ignore NoData—check

    Aggregate Multidimensional Raster dialog box

  3. Click Run.

    The result is a multidimensional raster dataset, in .crf format, containing the yearly maximum sea surface temperature value for every pixel and for every year in the dataset.

  4. Double-click the YearlySST.crf layer in the Contents pane to open the Layer Properties dialog box. Open the Source menu, and expand the Multidimensional Info section. Notice that the number of StdTime values has dropped from 432 (months) to 36 (years).

    Now let's take a look at some of the anomalies.

  5. Click the Back button in the Geoprocessing pane to go back to the menu. Open the Generate Multidimensional Anomaly tool from the Multidimensional Analysis toolbox.

    This tool generates a CRF multidimensional raster depicting, for each year, the difference in temperature between each pixel and the global average for that year.

  6. Enter the parameters as follows:
    • Input Multidimensional RasterYearlySST.crf
    • Output Multidimensional RasterYearlySSTAnomalies.crf
    • Variablescfsrsst [StdTime=36]() check
    • Anomaly Calculation MethodDifference From Mean
    • Mean Calculation IntervalAll
    • Ignore NoData—check

    Generate Multidimensional Anomaly dialog box

  7. Click Run. When the tool completes, the new raster layer will appear in your map.
  8. With the YearlySSTAnomalies.crf layer in the Contents pane, open the Appearance tab under the Raster Layer contextual tab on the ribbon. In the Rendering group, click the Stretch Type drop-down and select Standard Deviation.

    The result is a multidimensional raster dataset, in .crf format, in which pixels in red indicate maximum yearly temperatures that were higher than the global mean sea surface temperature in that year. Pixels in blue indicate temperatures lower than the global mean.

    Maximum yearly sea surface temperature anomalies

Explore sea surface temperature change and anomalies

Now that you have anomaly data for maximum yearly temperatures, you can visualize and explore it. You can use the Multidimensional tab and temporal profile charts to identify areas experiencing change and see trends.

  1. Select the YearlySSTAnomalies.crf layer in the Contents pane and open the Multidimensional tab on the ribbon.
  2. In the Current Display Slice group, click the Play Slices along StdTime button to see the display update for each year in the dataset.
    Play Slices along StdTime button
  3. Set the StdTime in the Current Display Slice to 1998-01-01T00:00:00 - 1998-12-31T23:59:59 to see yearly temperature anomalies for 1998.

    Sea surface temperature anomaly in 1998-1999
    A large red region appears off the west coast of South America. The anomaly stretches across the Pacific Ocean, showing an area where the maximum temperatures were much higher than the global ocean temperature.

  4. Right-click the layer, select Create Chart, and choose Temporal Profile.

    The Chart Properties pane appears, and the chart window appears at the bottom of the project.

  5. Configure the temporal chart:
    1. Click the point button under Define an area of interest. Place a point in the Pacific Ocean in the large red region described above (near 84.2696°W 4.433006°S ).
    2. Under Time binning options, change the Interval size to 1 Years.
    3. If necessary, under All locations, use the drop-down menu under the Variable column to select cfsrsst.

    The chart updates with the temporal profile for the point location across 36 years of data. You can further configure the chart using the Axes, Guides, Format, and General tabs at the top of the Chart Properties pane.

  6. In the Time format section, for Date Format, choose (yyyy) or 2020 (yyyy). For Time Format, choose <none>.
  7. Click the General tab in the Chart Properties pane. Change the chart title to Sea Surface Temperature 1980 - 2015. Change the x-axis title to Date and the y-axis title to Mean Sea Surface Temperature (K).
    Sea surface temperature chart for central and east-central Pacific

    Notice the two years during which maximum temperature was much higher than other years. Hover over the two points to see that 1983 and 1997 were two years with higher-than-average temperatures.

    These two years, and the large red region in the anomaly data, correspond to two El Niño events, in which a band of warm ocean water develops in the central and east-central Pacific region.

  8. Click the point feature button again and this time add a point in the waters off the southeast coast of Greenland (near 32.997154°W 64.789142°N ).
    Place a new point off the coast of Greenland.
  9. Uncheck the box next to the first location point under the Chart Properties pane to remove the El Niño data from the chart. This way, you can better observe the profile for Greenland.

    The temporal profile for this part of the world is different from the east-central Pacific. Here, you can see a distinct, steady increase in temperature over time—one of the many impacts of global climate change.

Use the anomaly CRF dataset to find other interesting areas.


In this tutorial, you added monthly sea surface temperature data to your map and used geoprocessing tools to aggregate the data into yearly maximum temperature and calculate anomalies. You then explored your data using temporal profile charts. For more information on these topics, see the following:

The data used in this tutorial is from the NCAR Research Data Archive:

Saha, S., et al. 2010. NCEP Climate Forecast System Reanalysis (CFSR) Monthly Products, January 1979 to December 2010. Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory. Accessed 6/21/19

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