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Operational Model Class

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Preprocessing Class

AMSIMP Preprocessing Class. For information about this class is described below.

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class amsimp.preprocessing.Preprocessing(forecast_length=120, amsimp_ic=True, initialisation_conditions=None)[source]

This is the preprocessing class for AMSIMP.

interpolate_dataset()[source]

Generates a cube list with the expected parameters, interpolated if necessary onto the grid required for input into the operational AMSIMP Global Forecast Model.

Returns

Cube list with interpolated grid for operational model

Return type

iris.cube.CubeList

Notes

This method also ensures the expected number of time steps are included, and raises an error when an insufficient number is present. The number of time steps required is 6.

lat()[source]

Generates an array of latitude lines in accordance with the shape expected by the operational model of the AMSIMP Global Forecast Model.

Returns

Latitude lines

Return type

numpy.ndarray

Notes

The resolution of this model is approximately 100 kilometres (1 degree).

See also

lon()

load_dataset()[source]

Generates a cube list with the required dataset loaded, either the file provided is loaded or the files from the AMSIMP Initial Conditions repository are downloaded and saved into a single file.

Returns

Cube list with the required dataset loaded

Return type

iris.cube.CubeList

Notes

The AMSIMP Initial Conditions repository is update four times daily, at 1 am, 7 am, 1 pm, and 7 pm. The near real-time initialisation conditions are provided by the National Oceanic and Atmospheric Adminstrations’ Global Data Assimilation System (GDAS).

lon()[source]

Generates an array of longitude lines in accordance with the shape expected by the operational model of the AMSIMP Global Forecast Model.

Returns

Longitude lines

Return type

numpy.ndarray

Notes

The resolution of this model is approximately 100 kilometres (1 degree).

See also

lon()

normalise_dataset()[source]

Generates a NumPy array with the expected parameters, normalised, processed onto the grid required for input into the operational AMSIMP Global Forecast Model.

Returns

Normalised and preprocessed dataset for forecast model input

Return type

numpy.ndarray

Notes

This method also converts the parameters into the correct units of measurement if it is necessary to do so.

parameter_extraction()[source]

Generates a cube list with the expected parameters extracted in the expected order for interplolation and normalisation.

Returns

Cube list with expected parameters extracted

Return type

iris.cube.CubeList

Notes

The parameters, in this order, are: air temperature at 2 metres above the surface, air temperature at a pressure surface of 850 hectopascals, and geopotential at a pressure surface of 500 hectopascals.