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‎doc/source/references.bib

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@@ -96,6 +96,28 @@ @ARTICLE{RL2008
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DOI = "10.1175/2007MWR2123.1"
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}
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@ARTICLE{Seed2003,
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AUTHOR = "A. W. Seed",
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TITLE = "A Dynamic and Spatial Scaling Approach to Advection Forecasting",
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JOURNAL = "Journal of Applied Meteorology",
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VOLUME = 42,
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NUMBER = 3,
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PAGES = "381-388",
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YEAR = 2003,
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DOI = "10.1175/1520-0450(2003)042<0381:ADASSA>2.0.CO;2"
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}
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@ARTICLE{SPN2013,
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AUTHOR = "A. W. Seed and C. E. Pierce and K. Norman",
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TITLE = "Formulation and evaluation of a scale decomposition-based stochastic precipitation nowcast scheme",
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JOURNAL = "Water Resources Research",
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VOLUME = 49,
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NUMBER = 10,
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PAGES = "6624--6641",
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YEAR = 2013,
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DOI = "10.1002/wrcr.20536"
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}
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@ARTICLE{ZR2009,
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AUTHOR = "P. Zacharov and D. Rezacova",
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TITLE = "Using the fractions skill score to assess the relationship between an ensemble {QPF} spread and skill",

‎pysteps/noise/interface.py

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@@ -25,13 +25,13 @@ def get_method(name):
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Methods for velocity fields:
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+-------------------+-------------------------------------------------------+
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| Name | Description |
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+===================+=======================================================+
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| bps | The method of Bowler et al. (2006), where |
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| | time-dependent velocity perturbations are sampled |
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| | from the exponential distribution |
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+-------------------+-------------------------------------------------------+
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+-------------------+-----------------------------------------------------+
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| Name | Description |
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+===================+=====================================================+
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| bps | The method described in :cite:`BPS2006`, where |
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| | time-dependent velocity perturbations are sampled |
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| | from the exponential distribution |
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+-------------------+-----------------------------------------------------+
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"""
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if name.lower() == "parametric":

‎pysteps/noise/motion.py

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@@ -105,8 +105,7 @@ def initialize_bps(V, pixelsperkm, timestep, p_pert_par=(10.88,0.23,-7.68),
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def generate_bps(perturbator, t):
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"""Generate a motion perturbation field by using the method described in
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Bowler et al. 2006: STEPS: A probabilistic precipitation forecasting scheme
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which merges an extrapolation nowcast with downscaled NWP.
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:cite:`BPS2006`.
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Parameters
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----------

‎pysteps/noise/utils.py

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def compute_noise_stddev_adjs(R, R_thr_1, R_thr_2, F, decomp_method, num_iter,
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conditional=True, num_workers=None):
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"""Simulate the effect of applying a precipitation mask to a Gaussian noise
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"""Apply a scale-dependent adjustment factor to the noise fields used in STEPS.
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Simulates the effect of applying a precipitation mask to a Gaussian noise
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field obtained by the nonparametric filter method. The idea is to decompose
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the masked noise field into a cascade and compare the standard deviations of
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each level into those of the observed precipitation intensity field. This
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gives correction factors for the standard deviations (Bowler et al. 2006).
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the masked noise field into a cascade and compare the standard deviations
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of each level into those of the observed precipitation intensity field.
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This gives correction factors for the standard deviations :cite:`BPS2006`.
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The calculations are done for n realizations of the noise field, and the
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correction factors are calculated from the average values of the standard
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deviations.

‎pysteps/nowcasts/interface.py

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where R (m,n) is the input precipitation field to be extrapolated and V (2,m,n) is
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an array containing the x- and y-components of the m*n advection field. num_timesteps
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is an integer specifying the number of time steps to forecast.
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Non-keyworded arguments specific to each method can be included
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The interface accepts additional ptional keyword arguments that are specific to a given
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forecast method.
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is an integer specifying the number of time steps to forecast. Non-keyword
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arguments specific to each method can be included. The interface accepts optional
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keyword arguments that are specific to a given forecast method.
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The output of each method is a three-dimensional array of shape (num_timesteps,m,n)
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containing a time series of nowcast precipitation fields.
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Methods for precipitation nowcasting:
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+-------------------+-------------------------------------------------------+
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| Name | Description |
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+===================+=======================================================+
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| eulerian | this approach simply keeps the last observation |
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| | frozen (Eulerian persistence) |
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+-------------------+-------------------------------------------------------+
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| lagrangian or | this approach extrapolate the last observation |
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| extrapolation | following the motion field (Lagrangian persistence) |
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+-------------------+-------------------------------------------------------+
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| | implementation of the STEPS stochastic nowcasting |
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| steps | method as described in Seed (2003), Bowler et al |
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| | (2006) and Seed et al (2013) |
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+-------------------+-------------------------------------------------------+
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+-------------------+-----------------------------------------------------+
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| Name | Description |
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+===================+=====================================================+
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| eulerian | this approach simply keeps the last observation |
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| | frozen (Eulerian persistence) |
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+-------------------+-----------------------------------------------------+
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| lagrangian or | this approach extrapolates the last observation |
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| extrapolation | following the motion field (Lagrangian persistence) |
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+-------------------+-----------------------------------------------------+
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| steps | implementation of the STEPS stochastic nowcasting |
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| | method as described in :cite:`Seed2003`, |
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| | :cite:`BPS2006` and :cite:`SPN2013` |
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+-------------------+-----------------------------------------------------+
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"""
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if name.lower() in ["eulerian"]:
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def eulerian(R, V, num_timesteps, *args, **kwargs):

‎pysteps/nowcasts/steps.py

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@@ -128,7 +128,7 @@ def forecast(R, V, n_timesteps, n_ens_members, n_cascade_levels,
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References
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----------
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:cite:`BPS2006`
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:cite:`Seed2003`, :cite:`BPS2006`, :cite:`SPN2013`
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"""
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_check_inputs(R, V, ar_order)

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