pysteps.nowcasts.sprog.forecast

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pysteps.nowcasts.sprog.forecast#

pysteps.nowcasts.sprog.forecast(precip, velocity, timesteps, precip_thr=None, norain_thr=0.0, n_cascade_levels=6, extrap_method='semilagrangian', decomp_method='fft', bandpass_filter_method='gaussian', ar_order=2, conditional=False, probmatching_method='cdf', num_workers=1, fft_method='numpy', domain='spatial', extrap_kwargs=None, filter_kwargs=None, measure_time=False)#

Generate a nowcast by using the Spectral Prognosis (S-PROG) method.

Parameters:

  • precip (array-like) – Array of shape (ar_order+1,m,n) containing the input precipitation fields ordered by timestamp from oldest to newest. The time steps between the inputs are assumed to be regular.

  • 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.

  • timesteps (int or list of floats) – Number of time steps to forecast or a list of time steps for which the forecasts are computed (relative to the input time step). The elements of the list are required to be in ascending order.

  • precip_thr (float, required) – The threshold value for minimum observable precipitation intensity.

  • norain_thr (float) – Specifies the threshold value for the fraction of rainy (see above) pixels in the radar rainfall field below which we consider there to be no rain. Depends on the amount of clutter typically present. Standard set to 0.0

  • n_cascade_levels (int, optional) – The number of cascade levels to use. Defaults to 6, see issue #385 on GitHub.

  • extrap_method (str, optional) – Name of the extrapolation method to use. See the documentation of pysteps.extrapolation.interface.

  • decomp_method ({'fft'}, optional) – Name of the cascade decomposition method to use. See the documentation of pysteps.cascade.interface.

  • bandpass_filter_method ({'gaussian', 'uniform'}, optional) – Name of the bandpass filter method to use with the cascade decomposition. See the documentation of pysteps.cascade.interface.

  • ar_order (int, optional) – The order of the autoregressive model to use. Must be >= 1.

  • conditional (bool, optional) – If set to True, compute the statistics of the precipitation field conditionally by excluding pixels where the values are below the threshold precip_thr.

  • probmatching_method ({'cdf','mean',None}, optional) – Method for matching the conditional statistics of the forecast field (areas with precipitation intensity above the threshold precip_thr) with those of the most recently observed one. ‘cdf’=map the forecast CDF to the observed one, ‘mean’=adjust only the mean value, None=no matching applied.

  • num_workers (int, optional) – The number of workers to use for parallel computation. Applicable if dask is enabled or pyFFTW is used for computing the FFT. When num_workers>1, it is advisable to disable OpenMP by setting the environment variable OMP_NUM_THREADS to 1. This avoids slowdown caused by too many simultaneous threads.

  • fft_method (str, optional) – A string defining the FFT method to use (see utils.fft.get_method). Defaults to ‘numpy’ for compatibility reasons. If pyFFTW is installed, the recommended method is ‘pyfftw’.

  • domain ({"spatial", "spectral"}) – If “spatial”, all computations are done in the spatial domain (the classical S-PROG model). If “spectral”, the AR(2) models are applied directly in the spectral domain to reduce memory footprint and improve performance [PCH18a].

  • extrap_kwargs (dict, optional) – Optional dictionary containing keyword arguments for the extrapolation method. See the documentation of pysteps.extrapolation.

  • filter_kwargs (dict, optional) – Optional dictionary containing keyword arguments for the filter method. See the documentation of pysteps.cascade.bandpass_filters.py.

  • measure_time (bool) – If set to True, measure, print and return the computation time.

Returns:

out – A three-dimensional array of shape (num_timesteps,m,n) containing a time series of forecast precipitation fields. The time series starts from t0+timestep, where timestep is taken from the input precipitation fields precip. If measure_time is True, the return value is a three-element tuple containing the nowcast array, the initialization time of the nowcast generator and the time used in the main loop (seconds).

Return type:

ndarray

See also

pysteps.extrapolation.interface, pysteps.cascade.interface

References

[See03], [PCH18a]

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