.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/rainfarm_downscale.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_rainfarm_downscale.py: Precipitation downscaling with RainFARM ======================================= This example script shows how to use the stochastic downscaling method RainFARM available in pysteps. RainFARM is a downscaling algorithm for rainfall fields developed by Rebora et al. (2006). The method can represent the realistic small-scale variability of the downscaled precipitation field by means of Gaussian random fields. .. GENERATED FROM PYTHON SOURCE LINES 14-25 .. code-block:: default import matplotlib.pyplot as plt import numpy as np import os from pprint import pprint from pysteps import io, rcparams from pysteps.utils import aggregate_fields_space, square_domain, to_rainrate from pysteps.downscaling import rainfarm from pysteps.visualization import plot_precip_field .. GENERATED FROM PYTHON SOURCE LINES 26-31 Read the input data ------------------- As first step, we need to import the precipitation field that we are going to use in this example. .. GENERATED FROM PYTHON SOURCE LINES 31-55 .. code-block:: default # Import the example radar composite root_path = rcparams.data_sources["mch"]["root_path"] filename = os.path.join(root_path, "20160711", "AQC161932100V_00005.801.gif") precip, _, metadata = io.import_mch_gif( filename, product="AQC", unit="mm", accutime=5.0 ) # Convert to mm/h precip, metadata = to_rainrate(precip, metadata) # Reduce to a square domain precip, metadata = square_domain(precip, metadata, "crop") # Nicely print the metadata pprint(metadata) # Plot the original rainfall field plot_precip_field(precip, geodata=metadata) plt.show() # Assign the fill value to all the Nans precip[~np.isfinite(precip)] = metadata["zerovalue"] .. image-sg:: /auto_examples/images/sphx_glr_rainfarm_downscale_001.png :alt: rainfarm downscale :srcset: /auto_examples/images/sphx_glr_rainfarm_downscale_001.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none {'accutime': 5.0, 'cartesian_unit': 'm', 'institution': 'MeteoSwiss', 'orig_domain': (640, 710), 'product': 'AQC', 'projection': '+proj=somerc +lon_0=7.43958333333333 +lat_0=46.9524055555556 ' '+k_0=1 +x_0=600000 +y_0=200000 +ellps=bessel ' '+towgs84=674.374,15.056,405.346,0,0,0,0 +units=m +no_defs', 'square_method': 'crop', 'threshold': 0.01155375598376629, 'transform': None, 'unit': 'mm/h', 'x1': 290000.0, 'x2': 930000.0, 'xpixelsize': 1000.0, 'y1': -160000.0, 'y2': 480000.0, 'yorigin': 'upper', 'ypixelsize': 1000.0, 'zerovalue': 0.0, 'zr_a': 316.0, 'zr_b': 1.5} /home/docs/checkouts/readthedocs.org/user_builds/pysteps/envs/v1.7.4/lib/python3.8/site-packages/pysteps/visualization/utils.py:427: 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 56-63 Upscale the field ----------------- To test our downscaling method, we first need to upscale the original field to a lower resolution. This is only for demo purposes, as we need to artificially create a lower resolution field to apply our downscaling method. We are going to use a factor of 16 x. .. GENERATED FROM PYTHON SOURCE LINES 63-74 .. code-block:: default scale_factor = 16 upscaled_resolution = ( metadata["xpixelsize"] * scale_factor ) # upscaled resolution : 16 km precip_lr, metadata_lr = aggregate_fields_space(precip, metadata, upscaled_resolution) # Plot the upscaled rainfall field plt.figure() plot_precip_field(precip_lr, geodata=metadata_lr) .. image-sg:: /auto_examples/images/sphx_glr_rainfarm_downscale_002.png :alt: rainfarm downscale :srcset: /auto_examples/images/sphx_glr_rainfarm_downscale_002.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none .. GENERATED FROM PYTHON SOURCE LINES 75-80 Downscale the field ------------------- We can now use RainFARM to generate stochastic realizations of the downscaled precipitation field. .. GENERATED FROM PYTHON SOURCE LINES 80-120 .. code-block:: default fig = plt.figure(figsize=(5, 8)) # Set the number of stochastic realizations num_realizations = 5 # Per realization, generate a stochastically downscaled precipitation field # and plot it. # The first time, the spectral slope alpha needs to be estimated. To illustrate # the sensitivity of this parameter, we are going to plot some realizations with # half or double the estimated slope. alpha = None for n in range(num_realizations): # Spectral slope estimated from the upscaled field precip_hr, alpha = rainfarm.downscale( precip_lr, ds_factor=scale_factor, alpha=alpha, return_alpha=True ) plt.subplot(num_realizations, 3, n * 3 + 2) plot_precip_field(precip_hr, geodata=metadata, axis="off", colorbar=False) if n == 0: plt.title(f"alpha={alpha:.1f}") # Half the estimated slope precip_hr = rainfarm.downscale(precip_lr, ds_factor=scale_factor, alpha=alpha * 0.5) plt.subplot(num_realizations, 3, n * 3 + 1) plot_precip_field(precip_hr, geodata=metadata, axis="off", colorbar=False) if n == 0: plt.title(f"alpha={alpha * 0.5:.1f}") # Double the estimated slope precip_hr = rainfarm.downscale(precip_lr, ds_factor=scale_factor, alpha=alpha * 2) plt.subplot(num_realizations, 3, n * 3 + 3) plot_precip_field(precip_hr, geodata=metadata, axis="off", colorbar=False) if n == 0: plt.title(f"alpha={alpha * 2:.1f}") plt.subplots_adjust(wspace=0, hspace=0) plt.tight_layout() plt.show() .. image-sg:: /auto_examples/images/sphx_glr_rainfarm_downscale_003.png :alt: alpha=1.5, alpha=0.8, alpha=3.1 :srcset: /auto_examples/images/sphx_glr_rainfarm_downscale_003.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none /home/docs/checkouts/readthedocs.org/user_builds/pysteps/envs/v1.7.4/lib/python3.8/site-packages/pysteps/visualization/utils.py:427: 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/v1.7.4/lib/python3.8/site-packages/pysteps/visualization/utils.py:427: 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/v1.7.4/lib/python3.8/site-packages/pysteps/visualization/utils.py:427: 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/v1.7.4/lib/python3.8/site-packages/pysteps/visualization/utils.py:427: 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/v1.7.4/lib/python3.8/site-packages/pysteps/visualization/utils.py:427: 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/v1.7.4/lib/python3.8/site-packages/pysteps/visualization/utils.py:427: 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/v1.7.4/lib/python3.8/site-packages/pysteps/visualization/utils.py:427: 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/v1.7.4/lib/python3.8/site-packages/pysteps/visualization/utils.py:427: 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/v1.7.4/lib/python3.8/site-packages/pysteps/visualization/utils.py:427: 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/v1.7.4/lib/python3.8/site-packages/pysteps/visualization/utils.py:427: 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/v1.7.4/lib/python3.8/site-packages/pysteps/visualization/utils.py:427: 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/v1.7.4/lib/python3.8/site-packages/pysteps/visualization/utils.py:427: 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/v1.7.4/lib/python3.8/site-packages/pysteps/visualization/utils.py:427: 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/v1.7.4/lib/python3.8/site-packages/pysteps/visualization/utils.py:427: 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/v1.7.4/lib/python3.8/site-packages/pysteps/visualization/utils.py:427: 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 121-128 Remarks ------- Currently, the pysteps implementation of RainFARM only covers spatial downscaling. That is, it can improve the spatial resolution of a rainfall field. However, unlike the original algorithm from Rebora et al. (2006), it cannot downscale the temporal dimension. .. GENERATED FROM PYTHON SOURCE LINES 131-137 References ---------- Rebora, N., L. Ferraris, J. von Hardenberg, and A. Provenzale, 2006: RainFARM: Rainfall downscaling by a filtered autoregressive model. J. Hydrometeor., 7, 724–738. .. GENERATED FROM PYTHON SOURCE LINES 137-139 .. code-block:: default # sphinx_gallery_thumbnail_number = 2 .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 10.025 seconds) .. _sphx_glr_download_auto_examples_rainfarm_downscale.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: rainfarm_downscale.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: rainfarm_downscale.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_