pycontrails.datalib.ecmwf.model_levels¶

Utilities for working with ECMWF model-level data.

Functions

`ml_to_pl`(ds, target_pl, *[, lnsp, sp, ...])	Interpolate L137 model-level meteorology data to pressure levels.
`model_level_pressure`(sp, model_levels)	Return the pressure levels at each model level given the surface pressure.
`model_level_reference_pressure`([alt_ft_min, ...])	Return the pressure levels at each model level assuming a constant surface pressure.

pycontrails.datalib.ecmwf.model_levels.ml_to_pl(ds, target_pl, *, lnsp=None, sp=None, extrapolate=False)¶

Interpolate L137 model-level meteorology data to pressure levels.

The implementation is here is consistent with ECMWF’s suggested implementation.

Parameters:

ds (xarray.Dataset) – Dataset with model-level meteorology data. Must include a “model_level” dimension which is not split across chunks. The non-“model_level” dimensions must be aligned with the “lnsp” parameter. Can include any number of variables. Any non-dimension coordinates will be dropped.
target_pl (npt.ArrayLike) – Target pressure levels, [\(hPa\)].
lnsp (xarray.DataArray) – Natural logarithm of surface pressure, [\(\ln(\text{Pa})\)]. If provided, sp is ignored. At least one of lnsp or sp must be provided. The chunking over dimensions in common with ds must be the same as ds.
sp (xarray.DataArray) – Surface pressure, [\(\text{Pa}\)]. At least one of lnsp or sp must be provided. The chunking over dimensions in common with ds must be the same as ds.
extrapolate (bool) – The new model levels may be outside the bounds of the old levels. If extrapolate is True, values outside the bounds will be linearly extrapolated. Otherwise they will be set to NaN.

Returns:

xarray.Dataset – Interpolated data on the target pressure levels. This has the same dimensions as ds except that the “model_level” dimension is replaced with “level”. The shape of the “level” dimension is the length of target_pl. If ds is dask-backed, the output will be as well. Call .compute() to compute the result eagerly.

pycontrails.datalib.ecmwf.model_levels.model_level_pressure(sp, model_levels)¶

Return the pressure levels at each model level given the surface pressure.

This function assumes 137 model levels. Unlike model_level_reference_pressure(), this function does not assume constant pressure. Instead, it uses the half-level pressure formula \(p = a + b \cdot \text{sp}\) where \(a\) and \(b\) are constants for each model level.

Parameters:

sp (xarray.DataArray) – Surface pressure, [\(\text{Pa}\)]. A warning is issued if the minimum value of sp is less than 30320.0 Pa. Such low values are unrealistic.
model_levels (npt.ArrayLike) – Target model levels. Expected to be a one-dimensional array of integers between 1 and 137.

Returns:

xarray.DataArray – Pressure levels at each model level, [\(hPa\)]. The shape of the output is the product of the shape of the input and the length of model_levels. In other words, the output will have dimensions of the input plus a new dimension for model_levels.

If sp is not dask-backed, the output will be computed eagerly. In particular, if sp has a large size and model_levels is a large range, this function may consume a large amount of memory.

The dtype of the output is the same as the dtype of the sp parameter.

Examples

>>> import numpy as np
>>> import xarray as xr

>>> sp_arr = np.linspace(101325.0, 90000.0, 16).reshape(4, 4)
>>> longitude = np.linspace(-180, 180, 4)
>>> latitude = np.linspace(-90, 90, 4)
>>> sp = xr.DataArray(sp_arr, coords={"longitude": longitude, "latitude": latitude})

>>> model_levels = [80, 100]
>>> model_level_pressure(sp, model_levels)
<xarray.DataArray (model_level: 2, longitude: 4, latitude: 4)> Size: 256B
array([[[259.75493944, 259.27107504, 258.78721064, 258.30334624],
        [257.81948184, 257.33561744, 256.85175304, 256.36788864],
        [255.88402424, 255.40015984, 254.91629544, 254.43243104],
        [253.94856664, 253.46470224, 252.98083784, 252.49697344]],
       [[589.67975444, 586.47283154, 583.26590864, 580.05898574],
        [576.85206284, 573.64513994, 570.43821704, 567.23129414],
        [564.02437124, 560.81744834, 557.61052544, 554.40360254],
        [551.19667964, 547.98975674, 544.78283384, 541.57591094]]])
Coordinates:
  * model_level  (model_level) int64 16B 80 100
  * longitude    (longitude) float64 32B -180.0 -60.0 60.0 180.0
  * latitude     (latitude) float64 32B -90.0 -30.0 30.0 90.0