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Data Structures

The main data structures in ModelSkill can be grouped into three categories:

  • Primary data (observations and model results)
  • Comparer objects
  • Skill objects

All objects share some common principles:

  • The data container is accesssible via the data attribute.
  • The data container is an xarray object (except for the SkillTable object, which is a pandas object).
  • The main data selection method is sel, which is a wrapper around xarray.Dataset.sel.
  • All plotting are accessible via the plot accessor of the object.

Observations and model results

The primary data of ModelSkill are the data that needs to be compared: observations and model results. The underlying data structures are very similar and can be grouped according to the spatial dimensionality (gtype) of the data:

  • point: 0D time series data
  • track: 0D time series data at moving locations (trajectories)
  • grid: gridded 2D data
  • dfsu: flexible mesh 2D data

Point and track data are both TimeSeries objects, while grid and dfsu data are both SpatialField objects. TimeSeries objects are ready to be compared whereas data from SpatialField object needs to be extracted first (the extracted object will be of the TimeSeries type).

TimeSeries objects contains its data in an xarray.Dataset with the actual data in the first DataArray and optional auxilliary data in the following DataArrays. The DataArrays have a kind attribute with either observation or model.

Comparer objects

Comparer objects are results of a matching procedure (between observations and model results) or constructed directly from already matched data. A comparison of a single observation and one or more model results are stored in a Comparer object. A comparison of multiple observations and one or more model results are stored in a ComparerCollection object which is a collection of Comparer objects.

The matched data in a Comparer is stored in an xarray.Dataset which can be accessed via the data attribute. The Dataset has an attribute gtype which is a string describing the type of data (e.g. point, track). The first DataArray in the Dataset is the observation data, the next DataArrays are model result data and optionally additional DataArrays are auxilliarye data. Each of the DataArrays have a kind attribute with either observation, model or aux.

Both Comparer and ComparerCollection have a plot accessor for plotting the data (e.g. cmp.plot.timeseries() or cmp.plot.scatter()).

Skill objects

Calling a skill method on a comparer object will return a skill object with skill scores (statistics) from comparing observation and model result data using different metrics (e.g. root mean square error). Two skill objects are currently implemented: SkillTable and SkillGrid. The first is relevant for all ModelSkill users while the latter is relevant for users of the track data (e.g. MetOcean studies using satellite altimetry data).

If c is a comparer object, then the following skill methods are available:

  • c.skill() -> SkillTable
  • c.mean_skill() -> SkillTable
  • c.gridded_skill() -> SkillGrid

SkillTable