climpred.horizon.horizon¶

climpred.horizon.horizon(cond)[source]¶

Calculate the predictability horizon based on a condition `cond.

Parameters: cond (xr.DataArray, xr.Dataset) – User-defined boolean array where True means the system is predictable at the given lead. E.g., this could be based on the dynamical forecast beating a reference forecast, p values, confidence intervals, etc. cond should contain the dimension lead at the minimum.
Returns: predictability horizon reduced by lead dimension.
Return type: xr.DataArray, xr.Dataset

Example

>>> skill = PerfectModelEnsemble.verify(metric='acc', comparison='m2e',
...     dim=['init','member'], reference=['persistence'])
>>> horizon(skill.sel(skill='initialized') >
...     skill.sel(skill='persistence'))
<xarray.Dataset>
Dimensions:  ()
Data variables:
    tos      float64 15.0
Attributes:
    units:          years
    standard_name:  forecast_period
    long_name:      Lead
    description:    Forecast period is the time interval between the forecast...

>>> bskill = PerfectModelEnsemble.bootstrap(metric='acc', comparison='m2e',
...     dim=['init','member'], reference='uninitialized', iterations=201)
>>> horizon(bskill.sel(skill='uninitialized', results='p') <= 0.05)
<xarray.Dataset>
Dimensions:  ()
Coordinates:
    results  <U12 'p'
    skill    <U13 'uninitialized'
Data variables:
    tos      float64 10.0
Attributes:
    units:          years
    standard_name:  forecast_period
    long_name:      Lead
    description:    Forecast period is the time interval between the forecast...