Missing Data Handling in Iris
This document provides a brief overview of how Iris handles missing data values when datasets are loaded as cubes, and when cubes are saved or modified.
A missing data value, or fill-value, defines the value used within a dataset to indicate that data point is missing or not set. This value is included as part of a dataset’s metadata.
For example, in a gridded global ocean dataset, no data values will be recorded over land, so land points will be missing data. In such a case, land points could be indicated by being set to the dataset’s missing data value.
On load, any fill-value or missing data value defined in the loaded dataset
should be used as the
fill_value of the NumPy masked array data attribute of the
Cube. This will only appear when the cube’s data is realised.
On save, the fill-value of a cube’s masked data array is not used in saving data. Instead, Iris always uses the default fill-value for the fileformat, except when a fill-value is specified by the user via a fileformat-specific saver.
>>> iris.save(my_cube, 'my_file.nc', fill_value=-99999)
Not all savers accept the
fill_value keyword argument.
Iris will check for and issue warnings of fill-value ‘collisions’. This basically means that whenever there are unmasked values that would read back as masked, we issue a warning and suggest a workaround.
This will occur in the following cases:
where masked data contains unmasked points matching the fill-value, or
where unmasked data contains the fill-value (either the format-specific default fill-value, or a fill-value specified by the user in the save call).
NetCDF is a special case, because all ordinary variable data is “potentially masked”, owing to the use of default fill values. The default fill-value used depends on the type of the variable data.
The exceptions to this are:
One-byte values are not masked unless the variable has an explicit
_FillValueattribute. That is, there is no default fill-value for
bytetypes in NetCDF.
Data may be tagged with a
_NoFillattribute. This is not currently officially documented or widely implemented.
Small integers create problems by not having the exemption applied to byte data. Thus, in principle,
int32data cannot use the full range of 2**16 valid values.
Merged data should have a fill-value equal to that of the components, if they all have the same fill-value. If the components have differing fill-values, a default fill-value will be used instead.
Other operations, such as
Cube arithmetic operations,
generally produce output with a default (NumPy) fill-value. That is, these operations
ignore the fill-values of the input(s) to the operation.