.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/plot_extrapolation_nowcast.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code. .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_plot_extrapolation_nowcast.py: Extrapolation nowcast ===================== This tutorial shows how to compute and plot an extrapolation nowcast using Finnish radar data. .. GENERATED FROM PYTHON SOURCE LINES 10-19 .. code-block:: Python from datetime import datetime import matplotlib.pyplot as plt import numpy as np from pprint import pprint from pysteps import io, motion, nowcasts, rcparams, verification from pysteps.utils import conversion, transformation from pysteps.visualization import plot_precip_field, quiver .. GENERATED FROM PYTHON SOURCE LINES 20-26 Read the radar input images --------------------------- First, we will import the sequence of radar composites. You need the pysteps-data archive downloaded and the pystepsrc file configured with the data_source paths pointing to data folders. .. GENERATED FROM PYTHON SOURCE LINES 26-32 .. code-block:: Python # Selected case date = datetime.strptime("201609281600", "%Y%m%d%H%M") data_source = rcparams.data_sources["fmi"] n_leadtimes = 12 .. GENERATED FROM PYTHON SOURCE LINES 33-35 Load the data from the archive ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ .. GENERATED FROM PYTHON SOURCE LINES 35-70 .. code-block:: Python root_path = data_source["root_path"] path_fmt = data_source["path_fmt"] fn_pattern = data_source["fn_pattern"] fn_ext = data_source["fn_ext"] importer_name = data_source["importer"] importer_kwargs = data_source["importer_kwargs"] timestep = data_source["timestep"] # Find the input files from the archive fns = io.archive.find_by_date( date, root_path, path_fmt, fn_pattern, fn_ext, timestep, num_prev_files=2 ) # Read the radar composites importer = io.get_method(importer_name, "importer") Z, _, metadata = io.read_timeseries(fns, importer, **importer_kwargs) # Convert to rain rate R, metadata = conversion.to_rainrate(Z, metadata) # Plot the rainfall field plot_precip_field(R[-1, :, :], geodata=metadata) plt.show() # Store the last frame for plotting it later later R_ = R[-1, :, :].copy() # Log-transform the data to unit of dBR, set the threshold to 0.1 mm/h, # set the fill value to -15 dBR R, metadata = transformation.dB_transform(R, metadata, threshold=0.1, zerovalue=-15.0) # Nicely print the metadata pprint(metadata) .. image-sg:: /auto_examples/images/sphx_glr_plot_extrapolation_nowcast_001.png :alt: plot extrapolation nowcast :srcset: /auto_examples/images/sphx_glr_plot_extrapolation_nowcast_001.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none /home/docs/checkouts/readthedocs.org/user_builds/pysteps/envs/v1.16.0/lib/python3.10/site-packages/pysteps/visualization/utils.py:439: UserWarning: cartopy package is required for the get_geogrid function but it is not installed. Ignoring geographical information. warnings.warn( {'accutime': 5.0, 'cartesian_unit': 'm', 'institution': 'Finnish Meteorological Institute', 'projection': '+proj=stere +lon_0=25E +lat_0=90N +lat_ts=60 +a=6371288 ' '+x_0=380886.310 +y_0=3395677.920 +no_defs', 'threshold': np.float64(-10.0), 'timestamps': array([datetime.datetime(2016, 9, 28, 15, 50), datetime.datetime(2016, 9, 28, 15, 55), datetime.datetime(2016, 9, 28, 16, 0)], dtype=object), 'transform': 'dB', 'unit': 'mm/h', 'x1': 0.0049823258887045085, 'x2': 759752.2852757066, 'xpixelsize': 999.674053, 'y1': 0.009731985162943602, 'y2': 1225544.6588913496, 'yorigin': 'upper', 'ypixelsize': 999.62859, 'zerovalue': -15.0, 'zr_a': 223.0, 'zr_b': 1.53} .. GENERATED FROM PYTHON SOURCE LINES 71-78 Compute the nowcast ------------------- The extrapolation nowcast is based on the estimation of the motion field, which is here performed using a local tracking approach (Lucas-Kanade). The most recent radar rainfall field is then simply advected along this motion field in oder to produce an extrapolation forecast. .. GENERATED FROM PYTHON SOURCE LINES 78-96 .. code-block:: Python # Estimate the motion field with Lucas-Kanade oflow_method = motion.get_method("LK") V = oflow_method(R[-3:, :, :]) # Extrapolate the last radar observation extrapolate = nowcasts.get_method("extrapolation") R[~np.isfinite(R)] = metadata["zerovalue"] R_f = extrapolate(R[-1, :, :], V, n_leadtimes) # Back-transform to rain rate R_f = transformation.dB_transform(R_f, threshold=-10.0, inverse=True)[0] # Plot the motion field plot_precip_field(R_, geodata=metadata) quiver(V, geodata=metadata, step=50) plt.show() .. image-sg:: /auto_examples/images/sphx_glr_plot_extrapolation_nowcast_002.png :alt: plot extrapolation nowcast :srcset: /auto_examples/images/sphx_glr_plot_extrapolation_nowcast_002.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none /home/docs/checkouts/readthedocs.org/user_builds/pysteps/envs/v1.16.0/lib/python3.10/site-packages/pysteps/visualization/utils.py:439: UserWarning: cartopy package is required for the get_geogrid function but it is not installed. Ignoring geographical information. warnings.warn( /home/docs/checkouts/readthedocs.org/user_builds/pysteps/envs/v1.16.0/lib/python3.10/site-packages/pysteps/visualization/utils.py:439: UserWarning: cartopy package is required for the get_geogrid function but it is not installed. Ignoring geographical information. warnings.warn( .. GENERATED FROM PYTHON SOURCE LINES 97-103 Verify with FSS --------------- The fractions skill score (FSS) provides an intuitive assessment of the dependency of skill on spatial scale and intensity, which makes it an ideal skill score for high-resolution precipitation forecasts. .. GENERATED FROM PYTHON SOURCE LINES 103-140 .. code-block:: Python # Find observations in the data archive fns = io.archive.find_by_date( date, root_path, path_fmt, fn_pattern, fn_ext, timestep, num_prev_files=0, num_next_files=n_leadtimes, ) # Read the radar composites R_o, _, metadata_o = io.read_timeseries(fns, importer, **importer_kwargs) R_o, metadata_o = conversion.to_rainrate(R_o, metadata_o, 223.0, 1.53) # Compute fractions skill score (FSS) for all lead times, a set of scales and 1 mm/h fss = verification.get_method("FSS") scales = [2, 4, 8, 16, 32, 64, 128, 256, 512] thr = 1.0 score = [] for i in range(n_leadtimes): score_ = [] for scale in scales: score_.append(fss(R_f[i, :, :], R_o[i + 1, :, :], thr, scale)) score.append(score_) plt.figure() x = np.arange(1, n_leadtimes + 1) * timestep plt.plot(x, score) plt.legend(scales, title="Scale [km]") plt.xlabel("Lead time [min]") plt.ylabel("FSS ( > 1.0 mm/h ) ") plt.title("Fractions skill score") plt.show() # sphinx_gallery_thumbnail_number = 3 .. image-sg:: /auto_examples/images/sphx_glr_plot_extrapolation_nowcast_003.png :alt: Fractions skill score :srcset: /auto_examples/images/sphx_glr_plot_extrapolation_nowcast_003.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 9.202 seconds) .. _sphx_glr_download_auto_examples_plot_extrapolation_nowcast.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_extrapolation_nowcast.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_extrapolation_nowcast.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_extrapolation_nowcast.zip ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_