pysteps.nowcasts

Implementation of deterministic and ensemble nowcasting methods.

pysteps.nowcasts.interface

Interface for the nowcasts module. It returns a callable function for computing nowcasts.

The methods in the nowcasts module implement the following interface:

forecast(precip, velocity, num_timesteps, **keywords)

where precip is a (m,n) array with input precipitation field to be advected and velocity is a (2,m,n) array containing the x- and y-components of the m x n advection field. num_timesteps is an integer specifying the number of time steps to forecast. The interface accepts optional keyword arguments specific to the given method.

The output depends on the type of the method. For deterministic methods, the output is a three-dimensional array of shape (num_timesteps,m,n) containing a time series of nowcast precipitation fields. For stochastic methods that produce an ensemble, the output is a four-dimensional array of shape (num_ensemble_members,num_timesteps,m,n). The time step of the output is taken from the inputs.

get_method(name)

Return a callable function for computing nowcasts.

pysteps.nowcasts.extrapolation

Implementation of extrapolation-based nowcasting methods.

forecast(precip, velocity, num_timesteps[, …])

Generate a nowcast by applying a simple advection-based extrapolation to the given precipitation field.

pysteps.nowcasts.sprog

Implementation of the S-PROG method described in [See03]

forecast(R, V, n_timesteps[, …])

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

pysteps.nowcasts.sseps

Implementation of the Short-space ensemble prediction system (SSEPS) method. Essentially, SSEPS is a localized version of STEPS.

For localization we intend the use of a subset of the observations in order to estimate model parameters that are distributed in space. The short-space approach used in [NBS+17] is generalized to the whole nowcasting system. This essenially boils down to a moving window localization of the nowcasting procedure, whereby all parameters are estimated over a subdomain of prescribed size.

forecast(R, metadata, V, n_timesteps[, …])

Generate a nowcast ensemble by using the Short-space ensemble prediction system (SSEPS) method.

pysteps.nowcasts.steps

Implementation of the STEPS stochastic nowcasting method as described in [See03], [BPS06] and [SPN13].

forecast(R, V, n_timesteps[, n_ens_members, …])

Generate a nowcast ensemble by using the Short-Term Ensemble Prediction System (STEPS) method.

pysteps.nowcasts.utils

Module with common utilities used by nowcasts methods.

print_ar_params(PHI)	Print the parameters of an AR(p) model.
print_corrcoefs(GAMMA)	Print the parameters of an AR(p) model.
stack_cascades(R_d, n_levels[, donorm])	Stack the given cascades into a larger array.
recompose_cascade(R, mu, sigma)	Recompose a cascade by inverting the normalization and summing the cascade levels.