7. Aggregation

The Community Intercomparison Suite (CIS) has the ability to aggregate both gridded and ungridded data along one or more coordinates. For example, you might aggregate a dataset over the longitude coordinate to produce an averaged measurement of variation over latitude.

CIS supports ‘complete collapse’ of a coordinate - where all values in that dimension are aggregated so that the coordinate no longer exists - and ‘partial collapse’ - where a coordinate is aggregated into bins of fixed size, so that the coordinate still exists but is on a coarser grid. Partial collapse is currently only supported for ungridded data. The output of an aggregation is always a CF compliant gridded NetCDF file.

The aggregation command has the following syntax:

$ cis aggregate <datagroup>[:options] <grid> [-o <outputfile>]



is a CIS datagroup specifying the variables and files to read and is of the format <variable>...:<filename>[:product=<productname>] where:

  • <variable> is a mandatory variable or list of variables to use.
  • <filenames> is a mandatory file or list of files to read from.
  • <productname> is an optional CIS data product to use (see Data Products):

See Datagroups for a more detailed explanation of datagroups.


Optional arguments given as keyword=value in a comma separated list. Options are:

  • kernel=<kernel> - the method by which the value in each aggregation cell is determined. <kernel> should be one of:

    • mean - use the mean value of all the data points in that aggregation cell. For gridded data, this mean is weighted to take into account differing cell areas due to the projection of lat/lon lines on the Earth.
    • min - use the lowest valid value of all the data points in that aggregate cell.
    • max - use the highest valid value of all the data points in that aggregate cell.
    • moments - In addition to returning the mean value of each cell (weighted where applicable), this kernel also outputs the number of points used to calculate that mean and the standard deviation of those values, each as a separate variable in the output file.

    If not specified the default is moments.

  • product=<productname> is an optional argument used to specify the type of files being read. If omitted, CIS will attempt to figure out which product to use based on the filename. See Reading to see a list of available product names and their file signatures.


This mandatory argument specifies the coordinates to aggregate over and whether they should be completely collapsed or aggregated into bins. Multiple coordinates can be aggregated over, in which case they should be separated by commas. Coordinates may be identified using their variable names (e.g. latitude) or by choosing from x, y, t, z, p which refer to longitude, latitude, time, altitude and pressure respectively.

  • Complete collapse - To perform a complete collapse of a coordinate, simply provide the name of the coordinate(s) as a comma separated list - e.g. x,y will aggregate data completely over both latitude and longitude. For ungridded data this will result in length one coordinates with bounds reflecting the maximum and minimum values of the collapsed coordinate.
  • Partial collapse - To aggregate a coordinate into bins, specify the start, end and step size of those bins in the form coordinate=[start,end,step]. The step may be missed out, in which case the bin will span the whole range given. Partial collapse is currently only supported for ungridded data.


Longitude coordinates are considered to be circular, so that -10 is equivalent to 350. The start and end must describe a monotonically increasing coordinate range, so x=[90,-90,10] is invalid, but could be specified using x=[90,270,10]. The range between the start and end must not be greater than 360 degrees.

Complete and partial collapses may be mixed where applicable - for example, to completely collapse time and to aggregate latitude on a grid from -45 degrees to 45 degrees, using a step size of 10 degrees:



For ungridded data, if a coordinate is left unspecified it is collapsed completely. This is in contrast to gridded data where a coordinate left unspecified is not used in the aggregation at all.


The range specified is the very start and end of the grid, the actual midpoints of the aggregation cells will start at start + delta/2.


Date/times are specified in the format: YYYY-MM-DDThh:mm:ss in which YYYY-MM-DD is a date and hh:mm:ss is a time. A colon or space can be used instead of the ‘T’ separator (but if a space is used, the argument must be quoted). Any trailing components of the date/time may be omitted. When a date/time is used as a range start, the earliest date/time compatible with the supplied components is used (e.g., 2010-04 is treated as 2010-04-01T00:00:00) and when used as a range end, the latest compatible date/time is used. Including optional and alternative components, the syntax is YYYY[-MM[-DD[{T|:| }hh[:mm[:ss]]]]].

Date/time steps are specified in the ISO 8061 format PnYnMnDTnHnMnS, where any particular time period is optional, for example P1MT30M would specify a time interval of 1 month and 30 minutes. Years and months are treated as calendar years and months, meaning they are not necessarily fixed in length. For example a date interval of 1 year and 1 month would mean going from 12:00 15th April 2013 to 12:00 15th May 2013. The are two exceptions to this, in rare cases such as starting at 30th January and going forward 1 month, the month is instead treated as a period of 28 days. Also, for the purposes of finding midpoints for the start in a month the month is always treated as 30 days. For example, to start on the 3rd November 2011 at 12:00 and aggregate over each month up to 3rd January 2013 at 12:00:

  • t=[2011-11-03T12:00,2013-01,P1M]
is an optional argument to specify the name to use for the file output. This is automatically given a .nc extension if not present. This must not be the same file path as any of the input files. If not supplied, the default filename is out.nc.

A full example would be:

$ cis aggregate rsutcs:rsutcs_Amon_HadGEM2-A_sstClim_r1i1p1_*.nc:product=NetCDF_Gridded,kernel=mean t,y=[-90,90,20],x -o rsutcs-mean

7.1. Conditional Aggregation

Sometimes you may want to perform an aggregation over all the points that meet a certain criteria - for example, aggregating satellite data only where the cloud cover fraction is below a certain threshold. This is possible by performing a CIS evaluation on your data first - see Using Evaluation for Conditional Aggregation

7.2. Aggregation Examples

7.2.1. Ungridded aggregation Aircraft Track

Original data:

$ cis plot TT_A:RF04.20090114.192600_035100.PNI.nc --xmin -180 --xmax -120 --ymin 0 --ymax 90

Aggregating onto a coarse grid:

$ cis aggregate TT_A:RF04.20090114.192600_035100.PNI.nc x=[-180,-120,3],y=[0,90,3] -o NCAR_RAF-1
$ cis plot TT_A:NCAR_RAF-1.nc

Aggregating onto a fine grid:

$ cis aggregate TT_A:RF04.20090114.192600_035100.PNI.nc x=[180,240,0.3],y=[0,90,0.3] -o NCAR_RAF-2
$ cis plot TT_A:NCAR_RAF-2.nc

Aggregating with altitude and time:

$ cis aggregate TT_A:RF04.20090114.192600_035100.PNI.nc t=[2009-01-14T19:30,2009-01-15T03:45,30M],z=[0,15000,1000] -o NCAR_RAF-3
$ cis plot TT_A:NCAR_RAF-3.nc --xaxis time --yaxis altitude

Aggregating with altitude and pressure:

$ cis aggregate TT_A:RF04.20090114.192600_035100.PNI.nc p=[100,1100,20],z=[0,15000,500] -o NCAR_RAF-4
$ cis plot TT_A:NCAR_RAF-4.nc --xaxis altitude --yaxis air_pressure --logy
_images/NCAR-RAF-5.png MODIS L3 Data

Original data:

$ cis plot Cloud_Top_Temperature_Mean_Mean:MOD08_E3.A2010009.005.2010026072315.hdf

Aggregating with a mean kernel:

$ cis aggregate Cloud_Top_Temperature_Mean_Mean:MOD08_E3.A2010009.005.2010026072315.hdf x=[-180,180,10],y=[-90,90,10] -o cloud-mean
$ cis plot Cloud_Top_Temperature_Mean_Mean:cloud-mean.nc

Aggregating with the standard deviation kernel:

$ cis aggregate Cloud_Top_Temperature_Mean_Mean:MOD08_E3.A2010009.005.2010026072315.hdf:kernel=stddev x=[-180,180,10],y=[-90,90,10] -o cloud-stddev
$ cis plot Cloud_Top_Temperature_Mean_Mean:cloud-stddev.nc &

Aggregating with the maximum kernel:

$ cis aggregate Cloud_Top_Temperature_Mean_Mean:MOD08_E3.A2010009.005.2010026072315.hdf:kernel=max x=[-180,180,10],y=[-90,90,10] -o cloud-max
$ cis plot Cloud_Top_Temperature_Mean_Mean:cloud-max.nc

Aggregating with the minimum kernel:

$ cis aggregate Cloud_Top_Temperature_Mean_Mean:MOD08_E3.A2010009.005.2010026072315.hdf:kernel=min x=[-180,180,10],y=[-90,90,10] -o cloud-min
$ cis plot Cloud_Top_Temperature_Mean_Mean:cloud-min.nc

7.2.2. Gridded aggregation

Aggregating 3D model data over time and longitude to produce an averaged measure of variation with latitude:

$ cis aggregate rsutcs:rsutcs_Amon_HadGEM2-A_sstClim_r1i1p1_185912-188911.nc:kernel=mean t,x -o agg-out.nc
$ cis plot rsutcs:agg-out.nc --xaxis latitude --yaxis rsutcs -o gridded_collapse.png

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