environmentaltools.temporal.storm_series

environmentaltools.temporal.storm_series(data, cols, info)

Extract storm events from time series data using threshold-based criteria.

Identifies discrete storm events based on duration, inter-arrival time, and threshold criteria following the methodology of Lira-Loarca et al. (2020).

Parameters:

• data (pd.DataFrame) – Raw time series with datetime index

• cols (list) – Names of required concomitant variables (first one used for threshold)

• info (dict) –

  Storm event criteria with keys:

  • thresholdfloat

    Minimum value to define storm event

  • min_durationint

    Minimum storm duration (in time_step units)

  • inter_timeint

    Minimum inter-arrival time between storms (in time_step units)

  • time_stepstr

    Time step unit: ‘D’ (days) or ‘h’ (hours)

  • interpolationbool, optional

    Whether to interpolate missing data. Default: False

  • filenamestr, optional

    Output file name for saving results

Returns:

Storm time series with identified events and their properties

Return type:

pd.DataFrame

Raises:

ValueError – If any label in cols is not in data columns

Notes

Uses the extreme_events package to identify storms based on:

• Exceedance of threshold value

• Minimum event duration

• Minimum separation between events

References

Lira-Loarca, A., et al. (2020). A global classification of coastal flood hazard climates. Scientific Reports.

Examples

>>> info = {
...     'threshold': 2.0,
...     'min_duration': 6,
...     'inter_time': 24,
...     'time_step': 'h',
...     'interpolation': True
... }
>>> storms = storm_series(data, ['wave_height', 'wind_speed'], info)