wxee.time_series.TimeSeries.climatology_anomaly

TimeSeries.climatology_anomaly(mean: Climatology, std: Climatology | None = None, keep_bandnames: bool = True) → TimeSeries[source]

Calculate climatological anomalies for the time series. The frequency and reducer will be the same as those used in the mean climatology. Standardized anomalies can be calculated by providing a climatological standard deviation as std.

A climatological anomaly is calculated as the difference between the climatological mean and a given observation. For standardized anomalies, that difference is divided by the climatological standard deviation. Standardized anomalies represent unitless measurements of how many standard deviations an observation was from the climatological mean, and therefore allow easy comparisons between variables.

Note

Climatological anomalies are generally calculated using long-term climatological normals (e.g. 30 years). If the climatological mean and standard deviation represent a shorter period, interpretation of results may vary.

Parameters:

  • mean (Climatology) – The long-term climatological mean to calculate anomalies from. The climatological frequency and reducer will be determined from this climatology.

  • std (Optional[Climatology]) – The long-term climatological standard deviation to calculate anomalies from. If provided, standardized climatological anomalies will be calculated. The climatological standard deviation frequency and reducer must match the frequency and reducer used by the climatological mean.

  • keep_bandnames (bool, default True) – If true, the band names of the input images will be kept in the aggregated images. If false, the name of the reducer will be appended to the band names, e.g. SR_B4 will become SR_B4_mean.

Returns:

Climatological anomalies within the TimeSeries period.

Return type:

wxee.time_series.TimeSeries

Raises:

ValueError – If the std frequency or reducer do not match the mean frequency or reducer. Only applies if a std is provided.

Example

>>> collection = wxee.TimeSeries("IDAHO_EPSCOR/GRIDMET")
>>> reference = collection.filterDate("1980", "2010")
>>> mean = reference.climatology_mean("month")
>>> std = reference.climatology_std("month")
>>> observation = collection.filterDate("2020", "2021")
>>> anomaly = observation.climatology_anomaly(mean, std)