def cold_spell_duration_index(tasmin: xarray.DataArray,
tn10: xarray.DataArray,
window: int = 6,
freq: str = "YS") -> xarray.DataArray:
r"""Cold spell duration index
Number of days with at least six consecutive days where the daily minimum temperature is below the 10th
percentile.
Parameters
----------
tasmin : xarray.DataArray
Minimum daily temperature.
tn10 : xarray.DataArray
10th percentile of daily minimum temperature with `dayofyear` coordinate.
window : int
Minimum number of days with temperature below threshold to qualify as a cold spell. Default: 6.
freq : str
Resampling frequency; Defaults to "YS".
Returns
-------
xarray.DataArray
Count of days with at least six consecutive days where the daily minimum temperature is below the 10th
percentile [days].
Notes
-----
Let :math:`TN_i` be the minimum daily temperature for the day of the year :math:`i` and :math:`TN10_i` the 10th
percentile of the minimum daily temperature over the 1961-1990 period for day of the year :math:`i`, the cold spell
duration index over period :math:`\phi` is defined as:
.. math::
\sum_{i \in \phi} \prod_{j=i}^{i+6} \left[ TN_j < TN10_j \right]
where :math:`[P]` is 1 if :math:`P` is true, and 0 if false.
References
----------
From the Expert Team on Climate Change Detection, Monitoring and Indices (ETCCDMI).
Example
-------
>>> import xclim.utils as xcu
>>> tn10 = xcu.percentile_doy(historical_tasmin, per=.1)
>>> cold_spell_duration_index(reference_tasmin, tn10)
"""
tn10 = utils.convert_units_to(tn10, tasmin)
# Create time series out of doy values.
thresh = utils.resample_doy(tn10, tasmin)
below = tasmin < thresh
return below.resample(time=freq).apply(rl.windowed_run_count,
window=window,
dim="time")
def days_over_precip_thresh(
pr: xarray.DataArray,
per: xarray.DataArray,
thresh: str = "1 mm/day",
freq: str = "YS",
) -> xarray.DataArray:
r"""Number of wet days with daily precipitation over a given percentile.
Number of days over period where the precipitation is above a threshold defining wet days and above a given
percentile for that day.
Parameters
----------
pr : xarray.DataArray
Mean daily precipitation flux [Kg m-2 s-1] or [mm/day]
per : xarray.DataArray
Daily percentile of wet day precipitation flux [Kg m-2 s-1] or [mm/day].
thresh : str
Precipitation value over which a day is considered wet [Kg m-2 s-1] or [mm/day].
freq : str
Resampling frequency; Defaults to "YS".
Returns
-------
xarray.DataArray
Count of days with daily precipitation above the given percentile [days]
Example
-------
>>> import xarray as xr
>>> import xclim
>>> pr = xr.open_dataset("precipitation_data.nc").pr
>>> p75 = pr.quantile(.75, dim="time", keep_attrs=True)
>>> r75p = xclim.indices.days_over_precip_thresh(pr, p75)
"""
per = utils.convert_units_to(per, pr)
thresh = utils.convert_units_to(thresh, pr)
tp = np.maximum(per, thresh)
if "dayofyear" in per.coords:
# Create time series out of doy values.
tp = utils.resample_doy(tp, pr)
# Compute the days where precip is both over the wet day threshold and the percentile threshold.
over = pr > tp
return over.resample(time=freq).sum(dim="time")
def fraction_over_precip_thresh(
pr: xarray.DataArray,
per: xarray.DataArray,
thresh: str = "1 mm/day",
freq: str = "YS",
) -> xarray.DataArray:
r"""Fraction of precipitation due to wet days with daily precipitation over a given percentile.
Percentage of the total precipitation over period occurring in days where the precipitation is above a threshold
defining wet days and above a given percentile for that day.
Parameters
----------
pr : xarray.DataArray
Mean daily precipitation flux [Kg m-2 s-1] or [mm/day].
per : xarray.DataArray
Daily percentile of wet day precipitation flux [Kg m-2 s-1] or [mm/day].
thresh : str
Precipitation value over which a day is considered wet [Kg m-2 s-1] or [mm/day].
freq : str
Resampling frequency; Defaults to "YS".
Returns
-------
xarray.DataArray
Fraction of precipitation over threshold during wet days days.
"""
per = utils.convert_units_to(per, pr)
thresh = utils.convert_units_to(thresh, pr)
tp = np.maximum(per, thresh)
if "dayofyear" in per.coords:
# Create time series out of doy values.
tp = utils.resample_doy(tp, pr)
# Total precip during wet days over period
total = pr.where(pr > thresh).resample(time=freq).sum(dim="time")
# Compute the days where precip is both over the wet day threshold and the percentile threshold.
over = pr.where(pr > tp).resample(time=freq).sum(dim="time")
return over / total
def tn10p(tasmin: xarray.DataArray,
t10: xarray.DataArray,
freq: str = "YS") -> xarray.DataArray:
r"""Number of days with daily minimum temperature below the 10th percentile.
Number of days with daily minimum temperature below the 10th percentile.
Parameters
----------
tasmin : xarray.DataArray
Mean daily temperature [℃] or [K]
t10 : xarray.DataArray
10th percentile of daily minimum temperature [℃] or [K]
freq : str
Resampling frequency; Defaults to "YS".
Returns
-------
xarray.DataArray
Count of days with daily minimum temperature below the 10th percentile [days]
Notes
-----
The 10th percentile should be computed for a 5 day window centered on each calendar day for a reference period.
Example
-------
>>> import xarray as xr
>>> import xclim.utils
>>> tas = xr.open_dataset("temperature_data.nc").tas
>>> t10 = xclim.utils.percentile_doy(tas, per=0.1)
>>> cold_days = tg10p(tas, t10)
"""
t10 = utils.convert_units_to(t10, tasmin)
# Create time series out of doy values.
thresh = utils.resample_doy(t10, tasmin)
# Identify the days below the 10th percentile
below = tasmin < thresh
return below.resample(time=freq).sum(dim="time")
def warm_spell_duration_index(tasmax: xarray.DataArray,
tx90: float,
window: int = 6,
freq: str = "YS") -> xarray.DataArray:
r"""Warm spell duration index
Number of days with at least six consecutive days where the daily maximum temperature is above the 90th
percentile. The 90th percentile should be computed for a 5-day window centred on each calendar day in the
1961-1990 period.
Parameters
----------
tasmax : xarray.DataArray
Maximum daily temperature [℃] or [K]
tx90 : float
90th percentile of daily maximum temperature [℃] or [K]
window : int
Minimum number of days with temperature below threshold to qualify as a warm spell.
freq : str
Resampling frequency; Defaults to "YS".
Returns
-------
xarray.DataArray
Count of days with at least six consecutive days where the daily maximum temperature is above the 90th
percentile [days].
References
----------
From the Expert Team on Climate Change Detection, Monitoring and Indices (ETCCDMI).
Used in Alexander, L. V., et al. (2006), Global observed changes in daily climate extremes of temperature and
precipitation, J. Geophys. Res., 111, D05109, doi: 10.1029/2005JD006290.
"""
# Create time series out of doy values.
thresh = utils.resample_doy(tx90, tasmax)
above = tasmax > thresh
return above.resample(time=freq).apply(rl.windowed_run_count,
window=window,
dim="time")
def tx90p(tasmax: xarray.DataArray,
t90: xarray.DataArray,
freq: str = "YS") -> xarray.DataArray:
r"""Number of days with daily maximum temperature over the 90th percentile.
Number of days with daily maximum temperature over the 90th percentile.
Parameters
----------
tasmax : xarray.DataArray
Maximum daily temperature [℃] or [K]
t90 : xarray.DataArray
90th percentile of daily maximum temperature [℃] or [K]
freq : str
Resampling frequency; Defaults to "YS".
Returns
-------
xarray.DataArray
Count of days with daily maximum temperature below the 10th percentile [days]
Notes
-----
The 90th percentile should be computed for a 5 day window centered on each calendar day for a reference period.
Example
-------
>>> import xarray as xr
>>> import xclim.utils
>>> tas = xr.open_dataset("temperature_data.nc").tas
>>> t90 = xclim.utils.percentile_doy(tas, per=0.9)
>>> hot_days = tg90p(tas, t90)
"""
t90 = utils.convert_units_to(t90, tasmax)
# Create time series out of doy values.
thresh = utils.resample_doy(t90, tasmax)
# Identify the days with max temp above 90th percentile.
over = tasmax > thresh
return over.resample(time=freq).sum(dim="time")
def tn10p(tasmin, t10, freq="YS"):
r"""Number of days with daily minimum temperature below the 10th percentile.
Number of days with daily minimum temperature below the 10th percentile.
Parameters
----------
tasmin : xarray.DataArray
Mean daily temperature [℃] or [K]
t10 : xarray.DataArray
10th percentile of daily minimum temperature [℃] or [K]
freq : str, optional
Resampling frequency
Returns
-------
xarray.DataArray
Count of days with daily minimum temperature below the 10th percentile [days]
Notes
-----
The 10th percentile should be computed for a 5 day window centered on each calendar day for a reference period.
Example
-------
>>> t10 = percentile_doy(historical_tas, per=0.1)
>>> cold_days = tg10p(tas, t10)
"""
t10 = utils.convert_units_to(t10, tasmin)
# Create time series out of doy values.
thresh = utils.resample_doy(t10, tasmin)
# Identify the days below the 10th percentile
below = tasmin < thresh
return below.resample(time=freq).sum(dim="time")
def tx90p(tasmax, t90, freq="YS"):
r"""Number of days with daily maximum temperature over the 90th percentile.
Number of days with daily maximum temperature over the 90th percentile.
Parameters
----------
tasmax : xarray.DataArray
Maximum daily temperature [℃] or [K]
t90 : xarray.DataArray
90th percentile of daily maximum temperature [℃] or [K]
freq : str, optional
Resampling frequency
Returns
-------
xarray.DataArray
Count of days with daily maximum temperature below the 10th percentile [days]
Notes
-----
The 90th percentile should be computed for a 5 day window centered on each calendar day for a reference period.
Example
-------
>>> t90 = percentile_doy(historical_tas, per=0.9)
>>> hot_days = tg90p(tas, t90)
"""
t90 = utils.convert_units_to(t90, tasmax)
# Create time series out of doy values.
thresh = utils.resample_doy(t90, tasmax)
# Identify the days with max temp above 90th percentile.
over = tasmax > thresh
return over.resample(time=freq).sum(dim="time")
