easyclimate.core.variability¶

This module calculate climate variability

Functions¶

`calc_all_climatological_mean`(→ xarray.DataArray)	Calculation of the climatological mean over the entire time range.
`calc_seasonal_climatological_mean`(→ xarray.DataArray)	Calculation of the seasonal climatological mean over the entire time range.
`calc_seasonal_cycle_mean`(→ xarray.DataArray)	Calculation of the seasonal cycle means over the entire time range.
`calc_seasonal_cycle_std`(→ xarray.DataArray)	Calculation of the seasonal cycle standard deviation over the entire time range.
`calc_seasonal_cycle_var`(→ xarray.DataArray)	Calculation of the seasonal cycle standard deviation over the entire time range.
`calc_seasonal_mean`(→ xarray.DataArray)	Calculation of the seasonal means per year over the entire time range.
`remove_seasonal_cycle_mean`() → xarray.DataArray)	Remove of the seasonal cycle means over the entire time range.
`calc_monthly_climatological_std_without_seasonal_cycle_mean`(...)	Calculate the standard deviation of monthly data anomalies over the entire time range.
`calc_monthly_climatological_var_without_seasonal_cycle_mean`(...)	Calculate the variance of monthly data anomalies over the entire time range.
`smooth_daily_annual_cycle`(→ xarray.DataArray)	Calculates a smooth mean daily annual cycle for an array nominally dimensioned.
`calc_daily_annual_cycle_mean`(→ xarray.DataArray)	Calculation of the daily means per year over the entire time range.
`calc_daily_annual_cycle_std`(→ xarray.DataArray)	Calculation of the daily standard deviation per year over the entire time range.
`calc_daily_annual_cycle_var`(→ xarray.DataArray)	Calculation of the daily variance per year over the entire time range.
`remove_smooth_daily_annual_cycle_mean`(data_input[, ...])	Removes the smooth daily annual cycle mean from the input data.
`calc_horizontal_wind_components_std`(→ xarray.Dataset)	Calculate the standard deviation of vector wind speed and direction.
`calc_windspeed_dataset`(→ xarray.Dataset)	Calculate the horizontal wind speed from zonal and meridional wind components in a `xarray.Dataset`.
`calc_windspeed_dataarray`(→ xarray.DataArray)	Calculate the horizontal wind speed from zonal and meridional wind components in `xarray.DataArray`.
`populate_monmean2everymon`(→ xarray.DataArray)	Populate the data of each month using the monthly mean state of the data_monthly or given dataset.
`populate_daymean2everyday`(→ xarray.DataArray)	Populate the data of each day using the daily mean state of the data_daily or given dataset.
`calc_daily_climatological_anomaly`(...)	Calulate daily anomaly using the given dataset of climatological mean state .
`remove_low_frequency_signal`(→ xarray.DataArray)	Remove low-frequency signal by subtracting the running mean from a time series.

Module Contents¶

easyclimate.core.variability.calc_all_climatological_mean(data_input: xarray.DataArray | xarray.Dataset, dim: str = 'time', **kwargs) → xarray.DataArray¶

Calculation of the climatological mean over the entire time range.

Parameters¶

data_inputxarray.DataArray or xarray.Dataset: The data of xarray.DataArray to be calculated.
dim: str: Dimension(s) over which to apply extracting. By default extracting is applied over the time dimension.
**kwargs:: Additional keyword arguments passed on to the appropriate array function for calculating mean on this object’s data. These could include dask-specific kwargs like split_every.

Returns¶

xarray.DataArray.

easyclimate.core.variability.calc_seasonal_climatological_mean(data_input: xarray.DataArray | xarray.Dataset, dim: str = 'time', **kwargs) → xarray.DataArray¶

Calculation of the seasonal climatological mean over the entire time range.

Parameters¶

data_inputxarray.DataArray or xarray.Dataset: The data of xarray.DataArray to be calculated.
dim: str: Dimension(s) over which to apply extracting. By default extracting is applied over the time dimension.
**kwargs:: Additional keyword arguments passed on to the appropriate array function for calculating mean on this object’s data. These could include dask-specific kwargs like split_every.

Returns¶

xarray.DataArray.

easyclimate.core.variability.calc_seasonal_cycle_mean(data_input: xarray.DataArray | xarray.Dataset, dim: str = 'time', **kwargs) → xarray.DataArray¶

Calculation of the seasonal cycle means over the entire time range.

Parameters¶

data_inputxarray.DataArray or xarray.Dataset: The data of xarray.DataArray to be calculated.

Caution

data_input must be monthly data.

dim: str: Dimension(s) over which to apply extracting. By default extracting is applied over the time dimension.
**kwargs:: Additional keyword arguments passed on to the appropriate array function for calculating mean on this object’s data. These could include dask-specific kwargs like split_every.

Returns¶

xarray.DataArray.

easyclimate.core.variability.calc_seasonal_cycle_std(data_input: xarray.DataArray | xarray.Dataset, dim: str = 'time', **kwargs) → xarray.DataArray¶

Calculation of the seasonal cycle standard deviation over the entire time range.

Parameters¶

data_inputxarray.DataArray or xarray.Dataset: The data of xarray.DataArray to be calculated.

Caution

data_input must be monthly data.

dim: str: Dimension(s) over which to apply extracting. By default extracting is applied over the time dimension.
**kwargs:: Additional keyword arguments passed on to the appropriate array function for calculating standard deviation on this object’s data. These could include dask-specific kwargs like split_every.

Returns¶

xarray.DataArray.

easyclimate.core.variability.calc_seasonal_cycle_var(data_input: xarray.DataArray | xarray.Dataset, dim: str = 'time', **kwargs) → xarray.DataArray¶

Calculation of the seasonal cycle standard deviation over the entire time range.

Parameters¶

data_inputxarray.DataArray or xarray.Dataset: The data of xarray.DataArray to be calculated.

Caution

data_input must be monthly data.

dim: str: Dimension(s) over which to apply extracting. By default extracting is applied over the time dimension.
**kwargs:: Additional keyword arguments passed on to the appropriate array function for calculating variance on this object’s data. These could include dask-specific kwargs like split_every.

Returns¶

xarray.DataArray.

easyclimate.core.variability.calc_seasonal_mean(data_input: xarray.DataArray | xarray.Dataset, dim: str = 'time', extract_season: Literal['seasonly', 'DJF', 'MAM', 'JJA', 'SON', 'JJAS', 'OND'] = 'seasonly', **kwargs) → xarray.DataArray¶

Calculation of the seasonal means per year over the entire time range.

Parameters¶

data_inputxarray.DataArray or xarray.Dataset: The data of xarray.DataArray to be calculated.

Caution

data_input must be monthly data.

dim: str

Dimension(s) over which to apply extracting. By default extracting is applied over the time dimension.

extract_season: list. default: “seasonly”.

Extraction seasons.

“seasonly”: Calculate by meteorological seasons (DJF, MAM, JJA, SON)
custom seasons: Calculate climatology by custom seasons, e.g., ‘DJF’, ‘MAM’, ‘JJA’, ‘SON’, 'JJAS', 'OND'.

Returns¶

xarray.DataArray.

easyclimate.core.variability.remove_seasonal_cycle_mean(data_input: xarray.DataArray | xarray.Dataset, dim: str = 'time', time_range: slice = slice(None, None)) → xarray.DataArray¶

Remove of the seasonal cycle means over the entire time range.

Parameters¶

data_inputxarray.DataArray or xarray.Dataset.: The data of xarray.DataArray to be calculated.

Caution

data_input must be monthly data.

dim: str.: Dimension(s) over which to apply extracting. By default extracting is applied over the time dimension.
time_range: slice, default: slice(None, None).: The time range of seasonal cycle means to be calculated. The default value is the entire time range.

Returns¶

xarray.DataArray.

easyclimate.core.variability.calc_monthly_climatological_std_without_seasonal_cycle_mean(data_input: xarray.DataArray | xarray.Dataset, dim: str = 'time', **kwargs) → xarray.DataArray¶

Calculate the standard deviation of monthly data anomalies over the entire time range.

Parameters¶

data_inputxarray.DataArray or xarray.Dataset: The data of xarray.DataArray to be calculated.

Caution

data_input must be monthly data.

dim: str: Dimension(s) over which to apply extracting. By default extracting is applied over the time dimension.
**kwargs:: Additional keyword arguments passed on to the appropriate array function for calculating standard deviation on this object’s data. These could include dask-specific kwargs like split_every.

Returns¶

xarray.DataArray.

easyclimate.core.variability.calc_monthly_climatological_var_without_seasonal_cycle_mean(data_input: xarray.DataArray | xarray.Dataset, dim: str = 'time', **kwargs) → xarray.DataArray¶

Calculate the variance of monthly data anomalies over the entire time range.

Parameters¶

data_inputxarray.DataArray or xarray.Dataset: The data of xarray.DataArray or xarray.Dataset to be calculated.

Caution

data_input must be monthly data.

dim: str: Dimension(s) over which to apply extracting. By default extracting is applied over the time dimension.
**kwargs:: Additional keyword arguments passed on to the appropriate array function for calculating variance on this object’s data. These could include dask-specific kwargs like split_every.

Returns¶

xarray.DataArray.

easyclimate.core.variability.smooth_daily_annual_cycle(daily_annual_cycle_data: xarray.DataArray, harmonics_num: int = 3, time_dim: str = 'dayofyear') → xarray.DataArray¶

Calculates a smooth mean daily annual cycle for an array nominally dimensioned.

Parameters¶

daily_annual_cycle_dataxarray.DataArray: The input data array with time as the first dimension. The time dimension should be named as specified by time_dim.
harmonics_numint, optional: The number of harmonics to retain in the FFT. Default is 3.
time_dimstr, optional: The name of the time dimension in the DataArray. Default is “dayofyear”.

Returns¶

xarray.DataArray

The smoothed daily cycle data.

Parameters¶

data_inputxarray.DataArray or xarray.Dataset: The data of xarray.DataArray to be calculated.

Caution

data_input must be daily or hourly data. At least one year of time range must be included in the data_input.

dim: str: Dimension(s) over which to apply extracting. By default extracting is applied over the time dimension.
**kwargs:: Additional keyword arguments passed to the mean function.

Returns¶

xarray.DataArray or xarray.Dataset.

Caution

For complete coverage, the data should span at least one full year.
If the data is sub-daily (e.g., hourly), the mean is taken over all sub-daily time points for each day of the year.

easyclimate.core.variability.calc_daily_annual_cycle_std(data_input: xarray.DataArray | xarray.Dataset, dim: str = 'time', **kwargs) → xarray.DataArray¶

Calculation of the daily standard deviation per year over the entire time range.

Parameters¶

data_inputxarray.DataArray or xarray.Dataset: The data of xarray.DataArray to be calculated.

Caution

data_input must be daily or hourly data. At least one year of time range must be included in the data_input.

dim: str: Dimension(s) over which to apply extracting. By default extracting is applied over the time dimension.
**kwargs:: Additional keyword arguments passed to the standard deviation function.

Returns¶

xarray.DataArray or xarray.Dataset.

Caution

For complete coverage, the data should span at least one full year.
If the data is sub-daily (e.g., hourly), the std is taken over all sub-daily time points for each day of the year.

easyclimate.core.variability.calc_daily_annual_cycle_var(data_input: xarray.DataArray | xarray.Dataset, dim: str = 'time', **kwargs) → xarray.DataArray¶

Calculation of the daily variance per year over the entire time range.

Parameters¶

data_inputxarray.DataArray or xarray.Dataset: The data of xarray.DataArray to be calculated.

Caution

data_input must be daily or hourly data. At least one year of time range must be included in the data_input.

dim: str: Dimension(s) over which to apply extracting. By default extracting is applied over the time dimension.
**kwargs:: Additional keyword arguments passed to the variance function.

Returns¶

xarray.DataArray or xarray.Dataset.

Caution

For complete coverage, the data should span at least one full year.
If the data is sub-daily (e.g., hourly), the var is taken over all sub-daily time points for each day of the year.

easyclimate.core.variability.remove_smooth_daily_annual_cycle_mean(data_input: xarray.DataArray, daily_cycle_mean_time_range: slice = slice(None, None), extract_time_range: slice = slice(None, None), harmonics_num: int = 3, dim: str = 'time')¶

Removes the smooth daily annual cycle mean from the input data.

This function first calculates the daily cycle mean over a specified time range, smooths that mean using a specified number of harmonics, and then subtracts this smoothed cycle from the input data over another specified time range.

Parameters¶

data_inputxarray.DataArray: The input data from which to remove the smooth daily annual cycle mean.
daily_cycle_mean_time_rangeslice, optional: The time range used to compute the daily annual cycle mean. Default is all time.
extract_time_rangeslice, optional: The time range from which to extract the data and remove the daily annual cycle. Default is all time.
harmonics_numint, optional: The number of harmonics to use in smoothing the daily annual cycle mean. Default is 3.
dimstr, optional: The name of the time dimension. Default is “time”.

Returns¶

xarray.DataArray: The input data with the smooth daily cycle mean removed.

easyclimate.core.variability.calc_horizontal_wind_components_std(uv_dataset: xarray.Dataset, u_dim='u', v_dim='v', time_dim='time', ddof=0) → xarray.Dataset¶

Calculate the standard deviation of vector wind speed and direction.

The standard deviation of vector wind speed

\[\sigma_s = [U^2 \sigma_u^2 + V^2 \sigma_v^2 + 2 U V \sigma_{uv}]^{1/2} S^{-1},\]

The standard deviation of vector wind direction

\[\sigma_d = [V^2 \sigma_u^2 + U^2 \sigma_v^2 + 2 U V \sigma_{uv}]^{1/2} S^{-2},\]

Where time mean of \(u\) is \(U = n^{-1} \sum u_i\), time mean of \(v\) is \(V = n^{-1} \sum v_i\), time variance of \(u\) is \(\sigma_u^2 = n^{-1} \sum u_{i}^{2} - U^2\), time variance of \(v\) is \(\sigma_v^2 = n^{-1} \sum v_{i}^{2} - V^2\), time covariance of \(u\), \(v\) is \(\sigma_{uv} = n^{-1} \sum u_i v_i - UV\), vector mean wind speed is \(S = (U^2 + V^2)^{1/2}\).

Parameters¶

uv_datasetxarray.Dataset: xarray.Dataset data containing zonal and meridional wind components.
u_dim: str, default: u: Variable name for the u velocity (in x direction).
v_dim: str, default: v: Variable name for the v velocity (in y direction).
time_dimstr, default: time: Dimension(s) over which to apply. By default is applied over the time dimension.
ddofint, default: 1: If ddof=1, covariance is normalized by N-1, giving an unbiased estimate, else normalization is by N.

Returns¶

xarray.Dataset

sigma_s: the standard deviation of vector wind speed.
sigma_d: the standard deviation of vector wind direction.

Reference¶

1. 1. Ackermann. (1983). Means and Standard Deviations of Horizontal Wind Components. https://doi.org/10.1175/1520-0450(1983)022%3C0959:MASDOH%3E2.0.CO;2

easyclimate.core.variability.calc_windspeed_dataset(uv_dataset: xarray.Dataset, u_dim: str = 'u', v_dim: str = 'v') → xarray.Dataset¶

Calculate the horizontal wind speed from zonal and meridional wind components in a xarray.Dataset.

The wind speed is computed as the magnitude of the horizontal wind vector:

\[S = \sqrt{u^2 + v^2},\]

where \(u\) is the zonal wind component and \(v\) is the meridional wind component.

Parameters¶

uv_datasetxarray.Dataset: xarray.Dataset containing zonal and meridional wind components.
u_dimstr, default: u: Variable name for the zonal wind component (in x direction).
v_dimstr, default: v: Variable name for the meridional wind component (in y direction).

Returns¶

xarray.Dataset: A copy of the input dataset with an additional variable speed containing the wind speed.

Examples¶

>>> ds = xr.Dataset({"u": (("time",), [1, 2, 3]), "v": (("time",), [4, 5, 6])})
>>> ds_with_speed = calc_windspeed_dataset(ds, u_dim="u", v_dim="v")
>>> print(ds_with_speed["speed"])
<xarray.DataArray 'speed' (time: 3)> Size: 24B
array([4.12310563, 5.38516481, 6.70820393])
Dimensions without coordinates: time

easyclimate.core.variability.calc_windspeed_dataarray(u_data: xarray.DataArray, v_data: xarray.DataArray) → xarray.DataArray¶

Calculate the horizontal wind speed from zonal and meridional wind components in xarray.DataArray.

The wind speed is computed as the magnitude of the horizontal wind vector:

\[S = \sqrt{u^2 + v^2},\]

where \(u\) is the zonal wind component and \(v\) is the meridional wind component.

Parameters¶

u_dataxarray.DataArray: xarray.DataArray containing the zonal wind component (in x direction).
v_dataxarray.DataArray: xarray.DataArray containing the meridional wind component (in y direction).

Returns¶

xarray.DataArray: A xarray.DataArray containing the wind speed.

Examples¶

>>> u = xr.DataArray([1, 2, 3], dims="time")
>>> v = xr.DataArray([4, 5, 6], dims="time")
>>> speed = calc_windspeed_dataarray(u, v)
>>> print(speed)
<xarray.DataArray (time: 3)> Size: 24B
array([4.12310563, 5.38516481, 6.70820393])
Dimensions without coordinates: time

easyclimate.core.variability.populate_monmean2everymon(data_monthly: xarray.DataArray, data_climatology_monthly_data: xarray.DataArray = None, time_dim: str = 'time') → xarray.DataArray¶

Populate the data of each month using the monthly mean state of the data_monthly or given dataset.

Parameters¶

data_monthly: xarray.DataArray.: xarray.DataArray to be calculated.
data_climatology_monthly_data: xarray.DataArray, default None.: The monthly climatology dataset. If it is None, the climatology is derived from data_monthly.
time_dim: str, default: time.: The time coordinate dimension name.

Returns¶

xarray.DataArray.

easyclimate.core.variability.populate_daymean2everyday(data_daily: xarray.DataArray, data_climatology_daily_data: xarray.DataArray = None, time_dim: str = 'time') → xarray.DataArray¶

Populate the data of each day using the daily mean state of the data_daily or given dataset.

Parameters¶

data_daily: xarray.DataArray.
xarray.DataArray to be calculated.
data_climatology_daily_data: xarray.DataArray, default None.
The daily climatology dataset. If it is None, the climatology is derived from data_monthly.
time_dim: str, default: time.
The time coordinate dimension name.

Returns¶

xarray.DataArray.

easyclimate.core.variability.calc_daily_climatological_anomaly(data_daily: xarray.DataArray | xarray.Dataset, data_climatology_daily_data: xarray.DataArray | xarray.Dataset, timd_dim='time') → xarray.DataArray | xarray.Dataset¶

Calulate daily anomaly using the given dataset of climatological mean state .

data_daily: xarray.DataArray or xarray.Dataset.
xarray.DataArray to be calculated.
data_climatology_daily_data: xarray.DataArray or xarray.Dataset.
The daily climatology dataset.
time_dim: str, default: time.
The time coordinate dimension name.

Returns¶

xarray.DataArray or xarray.Dataset.

easyclimate.core.variability.remove_low_frequency_signal(da: xarray.DataArray, window: int = 120, center: bool = False, time_dim: str = 'time') → xarray.DataArray¶

Remove low-frequency signal by subtracting the running mean from a time series.

This function removes the effect of interannual variability by subtracting the running mean of the specified window (default 120 days), as described in Wheeler and Hendon (2004). The method is commonly used in the context of the Madden-Julian Oscillation (MJO) index calculation for monitoring and prediction.

Parameters¶

daxarray.DataArray: Input time series data array with a time dimension.
windowint, optional: Size of the moving average window in days (default is 120).
centerbool, optional: If True, the moving average is centered (mean of window around each point). If False, the moving average is trailing (mean of last window days). Default is False.
time_dimstr, optional: Name of the time dimension in the input DataArray (default is “time”).

Returns¶

xarray.DataArray: The input data array with the low-frequency signal (running mean) removed.

References¶

Wheeler, M. C., & Hendon, H. H. (2004). An All-Season Real-Time Multivariate MJO Index: Development of an Index for Monitoring and Prediction. Monthly Weather Review, 132(8), 1917-1932. https://journals.ametsoc.org/view/journals/mwre/132/8/1520-0493_2004_132_1917_aarmmi_2.0.co_2.xml

Examples¶

>>> import xarray as xr
>>> da = xr.DataArray([...], dims=['time'], coords={'time': [...]})
>>> result = remove_low_frequency_signal(da, window=120, center=False, time_dim='time')

easyclimate.core.variability¶

Functions¶

Module Contents¶

Parameters¶

Returns¶

Parameters¶

Returns¶

Parameters¶

Returns¶

Parameters¶

Returns¶

Parameters¶

Returns¶

Parameters¶

Returns¶

Parameters¶

Returns¶

Example(s) related to the function¶

Parameters¶

Returns¶

Parameters¶

Returns¶

Parameters¶

Returns¶

Parameters¶

Returns¶

Parameters¶

Returns¶

Parameters¶

Returns¶

Parameters¶

Returns¶

Parameters¶

Returns¶

Reference¶

Parameters¶

Returns¶

Examples¶

Parameters¶

Returns¶

Examples¶

Parameters¶

Returns¶

Parameters¶

Returns¶

Returns¶

Parameters¶

Returns¶

References¶

Examples¶