improver.blending.calculate_weights_and_blend module

Plugin to calculate blend weights and blend data across a dimension

class improver.blending.calculate_weights_and_blend.WeightAndBlend(blend_coord, wts_calc_method, weighting_coord=None, wts_dict=None, y0val=None, ynval=None, cval=None, inverse_ordering=False)[source]

Bases: improver.BasePlugin

Wrapper class to calculate weights and blend data across cycles or models

__init__(blend_coord, wts_calc_method, weighting_coord=None, wts_dict=None, y0val=None, ynval=None, cval=None, inverse_ordering=False)[source]

Initialise central parameters

Parameters

  • blend_coord (str) – Coordinate over which blending will be performed (eg “model” for grid blending)

  • wts_calc_method (str) – Weights calculation method (“linear”, “nonlinear” or “dict”)

  • weighting_coord (str) – Coordinate over which linear weights should be calculated (from dictionary)

  • wts_dict (dict) – Dictionary containing parameters for linear weights calculation

  • y0val (float) – Relative weight of first file for default linear weights plugin

  • ynval (float) – Relative weight of last file for default linear weights plugin

  • cval (float) – Parameter for default non-linear weights plugin

  • inverse_ordering (bool) – Option to invert weighting order for non-linear weights plugin so that higher blend coordinate values get higher weights (eg if cycle blending over forecast reference time).

_abc_cache = <_weakrefset.WeakSet object>
_abc_negative_cache = <_weakrefset.WeakSet object>
_abc_negative_cache_version = 213
_abc_registry = <_weakrefset.WeakSet object>
_calculate_blending_weights(cube)[source]

Wrapper for plugins to calculate blending weights by the appropriate method.

Parameters

cube (iris.cube.Cube) – Cube of input data to be blended

Returns

Cube containing 1D array of weights for blending

Return type

iris.cube.Cube

_update_spatial_weights(cube, weights, fuzzy_length)[source]

Update weights using spatial information

Parameters

  • cube (iris.cube.Cube) – Cube of input data to be blended

  • weights (iris.cube.Cube) – Initial 1D cube of weights scaled by self.weighting_coord

  • fuzzy_length (float) – Distance (in metres) over which to smooth weights at domain boundaries

Returns

Updated 3D cube of spatially-varying weights

Return type

iris.cube.Cube

process(cubelist, cycletime=None, model_id_attr=None, spatial_weights=False, fuzzy_length=20000, attributes_dict=None)[source]

Merge a cubelist, calculate appropriate blend weights and compute the weighted mean. Returns a single cube collapsed over the dimension given by self.blend_coord.

Parameters

  • cubelist (iris.cube.CubeList) – List of cubes to be merged and blended

  • cycletime (str) – Forecast reference time to use for output cubes, in the format YYYYMMDDTHHMMZ. If not set, the latest of the input cube forecast reference times is used.

  • model_id_attr (str) – Name of the attribute by which to identify the source model and construct “model” coordinates for blending.

  • spatial_weights (bool) – If true, calculate spatial weights.

  • fuzzy_length (float) – Distance (in metres) over which to smooth spatial weights. Default is 20 km.

  • attributes_dict (dict or None) – Changes to cube attributes to be applied after blending