grid_process.blending

Module for blending datasets together

Attributes

logger

Functions

combine_models(→ xarray.Dataset)	Combine datasets in valid time dimension. The new coordinates follow the normal
cleaver(→ xarray.Dataset)	Combine two timeseries by applying a linear seamless transition from the
blend_src_attr(→ dict[str, str])	Returns a dict with keys being the sources of the input datasets and

Module Contents

logger

combine_models(datasets: collections.abc.Sequence[xarray.Dataset], strict_start: bool = True) → xarray.Dataset

Combine datasets in valid time dimension. The new coordinates follow the normal broadcasting and alignment rules of join='outer'. Vacant cells in the expanded coordinates are filled with numpy.nan .

Parameters:

• datasets (sequence of xarray.Dataset) – Datasets with valid time or lead time dimension to be combined. The sequence must reflect priority order, so where several datasets overlap in time, the data of the first one is kept Typical temporal dimensions names are supported, and can be extended via environment variables:

  • Valid time: export GRID_PROCESS__data_model__validtime (e.g., ‘[“time”, “valid_time”, “dummy_time”]’)

  • Lead time: export GRID_PROCESS__data_model__leadtime (e.g., ‘[“lead_time”, “step”, “dummy_leadtime”]’)

  • When the temporal coordinates variables have a CF compliant standard_name attribute, arbitrary names can be used directly.

• strict_start (bool, optional) – If True (default), the resulting timeseries strictly starts with the dataset having highest priority (i.e. no older timestamps from lower priority datasets can be prepended)

Returns:

combined_ds – contains the full timeseries resulting from the datasets combination. Valid time dimension is labelled by numpy.datetime64 although inputs could have lead time dimension (numpy.timedelta64)

Return type:

xarray.Dataset

See also

xarray.Dataset.combine_first, setindex_validtime()

Note

This function is not aimed to combine models that do not have the same grid and members, but to combine them after regridding/interpolation and ensemble statistic computations (quantiles, probabilities). A typical use case for this function could be to combine past deterministic analyses to all members of a NWP model as it will automatically broadcast it to the ensemble members dimension and pioritize analyses over the NWP forecast for common timestamps in both datasets.

cleaver(ds_tail: xarray.Dataset, ds_head: xarray.Dataset, h: int = 6, interp_method: grid_process.timeseries.InterpMethods = 'cubic') → xarray.Dataset

Combine two timeseries by applying a linear seamless transition from the last values of the tail to the remaining of the head.

Parameters:

• ds_tail (xarray.Dataset) – dataset with a valid time or lead time dimension. Contains the tail of the newly produced timeseries

• ds_head (xarray.Dataset) – dataset with a valid time or lead time dimension. Contains the head of the newly produced timeseries

• h (int, optional) – number of hours over which the seamless transition is done. Default is 6

• interp_method (str) – arg passed directly to interptogranularity() in case the tail and the head do not overlap. Default is ‘cubic’

Returns:

seamless_ds – newly produced seamless timeseries from the two input datasets. Has a valid time dimension labelled by numpy.datetime64 although inputs could have a lead time dimension (numpy.timedelta64)

Return type:

xarray.Dataset

See also

interptogranularity(), setindex_validtime()

blend_src_attr(datasets: collections.abc.Sequence[xarray.Dataset]) → dict[str, str]

Returns a dict with keys being the sources of the input datasets and values the corresponding reftimes. This dict can become the output source attribute when blending datasets together.