Additional functions

find_nearest(array, value[, axis, skipna])

Find the index of the item in the array nearest to the value

TropD_Calculate_MaxLat(F, lat[, n, axis])

Find latitude of absolute maximum value for a given interval

TropD_Calculate_Mon2Season(Fm, season[, m, ...])

Calculate unweighted seasonal means from monthly time series.

TropD_Calculate_StreamFunction(V, lat, lev)

Calculates the meridional mass streamfunction by integrating meridional wind from top of the atmosphere to surface

TropD_Calculate_TropopauseHeight()

Calculate the tropopause height in isobaric coordinates

TropD_Calculate_ZeroCrossing(F, lat[, ...])

Find the first (with increasing index) zero crossing of the function F.

Description of additional functions

skytropd.functions.TropD_Calculate_MaxLat(F: ndarray, lat: ndarray, n: int = 6, axis: int = -1) → ndarray[source]

Find latitude of absolute maximum value for a given interval

Note: assumes a smoothly varying function

Parameters:

  • F (numpy.ndarray (dim1, ..., dimN-1, lat)) – N-dimensional array w/ lat as specified axis (default last), data assumed contingous with invalid data only on ends. interior nans are untested and will prompt warning

  • lat (numpy.ndarray) – latitude array

  • n (int, optional) –

    rank of moment used to calculate the position of max value.

    n = 1,2,4,6,8,… , by default 6

  • axis (int, optional) – axis corresponding to latitude, by default -1

Returns:

N-1 dimensional array of latitude(s) of max values of F

Return type:

numpy.ndarray (dim1, …, dimN-1)

skytropd.functions.TropD_Calculate_Mon2Season(Fm: ndarray, season: List[int], m: Optional[int] = None, first_jan_idx: int = 0, axis: int = 0) → ndarray[source]

Calculate unweighted seasonal means from monthly time series. Dec is averaged with Jan and Feb of the same year, not the following year

Parameters:

  • Fm (numpy.ndarray (time, dim2, ..., dimN)) – N-dimensional array, only utilizes full years-worth of data will be utilized

  • season (list of int) – 0-based list of month indices, e.g., [11,0,1] for DJF

  • m (int, optional) – deprecated. use first_jan_idx instead.

  • first_jan_idx (int, optional) – index of first occurence of Jan in Fm, by default 0

  • axis (int, optional) – time axis to resample along, by default 0

Returns:

the annual time series of the seasonal means (not weighted for unequal month lengths)

Return type:

numpy.ndarray (time, dim2, …, dimN)

skytropd.functions.TropD_Calculate_StreamFunction(V: ndarray, lat: ndarray, lev: ndarray) → ndarray[source]

Calculates the meridional mass streamfunction by integrating meridional wind from top of the atmosphere to surface

Parameters:

  • V (numpy.ndarray (dim1, ..., dimN-2, lat, lev) or (dim1, ..., dimN-2, lev, lat)) – N-dimensional array of zonal-mean meridional wind with final 2 axes corresponding to latitude and vertical level, respectively, or the swapped trailing order (..., lev, lat)

  • lat (numpy.ndarray) – equally-spaced latitude array

  • lev (numpy.ndarray) – vertical level array in hPa

Returns:

the meridional mass overturning streamfunction (psi)

Return type:

numpy.ndarray (same shape as V)

skytropd.functions.TropD_Calculate_TropopauseHeight(T: ndarray, P: ndarray, Z: None) → ndarray[source]
skytropd.functions.TropD_Calculate_TropopauseHeight(T: ndarray, P: ndarray, Z: ndarray) → Tuple[ndarray, ndarray]

Calculate the tropopause height in isobaric coordinates

Based on the method in Birner (2010), according to the WMO definition: first level where the lapse rate <= 2K/km AND where the lapse rate <= 2K/km for at least 2km above that level

Parameters:

  • T (numpy.ndarray (dim1, ..., dimN-1, lev)) – N-dimensional temperature array

  • P (numpy.ndarray (lev,)) – pressure levels in hPa

  • Z (numpy.ndarray (same shape as T), optional) – N-dimensional array to be interpolated at tropopause, such as geopotential height (m) to yield tropopause height

Returns:

  • numpy.ndarray (dim1, …, dimN-1) – the tropopause pressure in hPa

  • numpy.ndarray (dim1, …, dimN-1), optional – returned if Z is provided. Corresponds to Z evaluated at the tropopause. If Z is geopotential height, it is tropopause altitude (m)

skytropd.functions.TropD_Calculate_ZeroCrossing(F: ndarray, lat: ndarray, lat_uncertainty: float = 0.0, axis: int = -1) → ndarray[source]

Find the first (with increasing index) zero crossing of the function F.

This function uses the compiled backend for the per-latitude-band search.

Parameters:

  • F (numpy.ndarray (dim1, ..., dimN-1, lat)) – N dimensional array to search

  • lat (numpy.ndarray (lat,)) – latitude array

  • lat_uncertainty (float, optional) – same unit as lat. The minimum distance allowed between adjacent zero crossings of identical sign change, by default 0.0. For example, for lat_uncertainty = 10, if the most equatorward zero crossing is from positive to negative, NaN is returned if an additional zero crossing from positive to negative is found within 10 degrees of the first one.

  • axis (int, optional) – axis corresponding to latitude, by default -1 (last)

Returns:

latitude(s) of zero crossing by linear interpolation

Return type:

np.ndarray

skytropd.functions.find_nearest(array: ndarray, value: float, axis: Optional[int] = None, skipna: bool = False) → ndarray[source]

Find the index of the item in the array nearest to the value

Parameters:

  • array (numpy.ndarray) – array to search

  • value (float) – value to be found

  • axis (int, optional) – the axis of the array to search. if not given, the array is flattened prior to being searched. If given, output will be (n-1)-dimensional, returning the position of the value within the specified axis

  • skipna (bool, optional) – whether to skip over NaN in the array, by default False

Returns:

index(es) nearest to value in array

Return type:

numpy.ndarray