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Co-authored-by: Ruben Imhoff <31476760+RubenImhoff@users.noreply.github.com>
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mats-knmi and RubenImhoff authored Sep 26, 2024
1 parent a5c9b22 commit 6dc7efe
Showing 1 changed file with 7 additions and 7 deletions.
14 changes: 7 additions & 7 deletions pysteps/blending/steps.py
Original file line number Diff line number Diff line change
Expand Up @@ -125,7 +125,7 @@ def forecast(
per (NWP) model or model ensemble member, a dictionary with a list of cascades
obtained by calling a method implemented in :py:mod:`pysteps.cascade.decomposition`.
If you supply the original (NWP) model forecast data, it needs to be an array of shape
(n_models,timestep+1,m,n) containing rainfall fields, which will
(n_models,timestep+1,m,n) containing precipitation (or other) fields, which will
then be decomposed in this function.
Depending on your use case it can be advantageous to decompose the model
Expand All @@ -137,19 +137,19 @@ def forecast(
usage.
To further reduce memory usage, both this array and the ``velocity_models`` array
can be given as float32, they will then be converted to float64 before computations
to minimize loss in precision
can be given as float32. They will then be converted to float64 before computations
to minimize loss in precision.
In case of one (deterministic) model as input, add an extra dimension to make sure
precip_models is five dimensional prior to calling this function.
precip_models is four dimensional prior to calling this function.
velocity: array-like
Array of shape (2,m,n) containing the x- and y-components of the advection
field. The velocities are assumed to represent one time step between the
inputs. All values are required to be finite.
To reduce memory usage, both this array and the ``precip_models`` array
can be given as float32, they will then be converted to float64 before computations
to minimize loss in precision
To reduce memory usage, this array
can be given as float32. They will then be converted to float64 before computations
to minimize loss in precision.
velocity_models: array-like
Array of shape (n_models,timestep,2,m,n) containing the x- and y-components
of the advection field for the (NWP) model field per forecast lead time.
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