.. note:: :class: sphx-glr-download-link-note Click :ref:`here ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_sprog_hk.py: SPROG (Hong Kong) ======================================================== This example demonstrates how to use SPROG to forecast rainfall up to three hours, using rain guage and radar data from Hong Kong. Definitions -------------------------------------------------------- Import all required modules and methods: .. code-block:: python # Python package to allow system command line functions import os # Python package to manage warning message import warnings # Python package for time calculations import pandas as pd # Python package for numerical calculations import numpy as np # Python package for xarrays to read and handle netcdf data import xarray as xr # Python package for text formatting import textwrap # Python package for projection description from pyresample import get_area_def # Python package for projection import cartopy.crs as ccrs # Python package for land/sea features import cartopy.feature as cfeature # Python package for reading map shape file import cartopy.io.shapereader as shpreader # Python package for creating plots from matplotlib import pyplot as plt # Python package for output grid format from matplotlib.gridspec import GridSpec # Python package for colorbars from matplotlib.colors import BoundaryNorm, ListedColormap from matplotlib.cm import ScalarMappable # swirlspy data parser function from swirlspy.rad.iris import read_iris_grid # swirlspy test data source locat utils function from swirlspy.qpe.utils import timestamps_ending, locate_file # swirlspy regrid function from swirlspy.core.resample import grid_resample # swirlspy standardize data function from swirlspy.utils import FrameType, standardize_attr, conversion # swirlspy pysteps integrated package from swirlspy.qpf import sprog, dense_lucaskanade # directory constants from swirlspy.tests.samples import DATA_DIR from swirlspy.tests.outputs import OUTPUT_DIR warnings.filterwarnings("ignore") Define working directory and nowcast parameters: .. code-block:: python radar_dir = os.path.abspath( os.path.join(DATA_DIR, 'iris/ppi') ) # Set nowcast parameters n_timesteps = int(3 * 60 / 6) # 3 hours, each timestamp is 6 minutes Define the user grid: .. code-block:: python area_id = "hk1980_250km" description = ( "A 250 m resolution rectangular grid centred at HKO and extending" "to 250 km in each direction in HK1980 easting/northing coordinates" ) proj_id = 'hk1980' projection = ( '+proj=tmerc +lat_0=22.31213333333334 ' '+lon_0=114.1785555555556 +k=1 +x_0=836694.05 ' '+y_0=819069.8 +ellps=intl +towgs84=-162.619,-276.959,' '-161.764,0.067753,-2.24365,-1.15883,-1.09425 +units=m +no_defs' ) x_size = 500 y_size = 500 area_extent = (587000, 569000, 1087000, 1069000) area_def_tgt = get_area_def( area_id, description, proj_id, projection, x_size, y_size, area_extent ) Define the base map: .. code-block:: python # Load the shape of Hong Kong map_shape_file = os.path.abspath(os.path.join( DATA_DIR, 'shape/hk' )) # coastline and province map_with_province = cfeature.ShapelyFeature( list(shpreader.Reader(map_shape_file).geometries()), ccrs.PlateCarree() ) # define the plot function def plot_base(ax: plt.Axes, extents: list, crs: ccrs.Projection): ax.set_extent(extents, crs=crs) # fake the ocean color ax.imshow( np.tile(np.array([[[178, 208, 254]]], dtype=np.uint8), [2, 2, 1]), origin='upper', transform=ccrs.PlateCarree(), extent=[-180, 180, -180, 180], zorder=-1 ) # coastline, province and state, color ax.add_feature( map_with_province, facecolor=cfeature.COLORS['land'], edgecolor='none', zorder=0 ) # overlay coastline, province and state without color ax.add_feature( map_with_province, facecolor='none', edgecolor='gray', linewidth=0.5 ) ax.set_title('') Log the start time for reference: .. code-block:: python start_time = pd.Timestamp.now() Loading Radar Data --------------------------------------------------- .. code-block:: python # Specify the basetime basetime = pd.Timestamp('201902190800') # Generate a list of timestamps of the radar data files located_files = [] radar_ts = timestamps_ending( basetime, duration=pd.Timedelta(minutes=60), exclude_end=False ) for timestamp in radar_ts: located_files.append(locate_file(radar_dir, timestamp)) # Read in the radar data reflectivity_list = [] # stores reflec from read_iris_grid() for filename in located_files: reflec = read_iris_grid(filename) reflectivity_list.append(reflec) # Reproject the radar data to the user-defined grid area_def_src = reflectivity_list[0].attrs['area_def'] reproj_reflectivity_list = [] for reflec in reflectivity_list: reproj_reflec = grid_resample( reflec, area_def_src, area_def_tgt, coord_label=['x', 'y'] ) reproj_reflectivity_list.append(reproj_reflec) # Standardize reflectivity xarrays raw_frames = xr.concat(reproj_reflectivity_list, dim='time').sortby(['y'], ascending=False) standardize_attr(raw_frames, frame_type=FrameType.dBZ) # Transform from reflecitiy to rainfall rate frames = conversion.to_rainfall_rate(raw_frames, True, a=58.53, b=1.56) # Set the fill value frames.attrs['zero_value'] = -15.0 # Apply threshold to -10dBR i.e. 0.1mm/h threshold = -10.0 frames.values[frames.values < threshold] = frames.attrs['zero_value'] # Set missing values with the fill value frames.values[~np.isfinite(frames.values)] = frames.attrs['zero_value'] # Log the time for record initialising_time = pd.Timestamp.now() Running Lucas Kanade Optical flow and S-PROG -------------------------------------------- .. code-block:: python # Estimate the motion field motion = dense_lucaskanade(frames) motion_time = pd.Timestamp.now() # Generate forecast rainrate field forcast_frames = sprog( frames, motion, n_timesteps, n_cascade_levels=8, R_thr=threshold, decomp_method="fft", bandpass_filter_method="gaussian", probmatching_method="mean", ) sprog_time = pd.Timestamp.now() .. rst-class:: sphx-glr-script-out Out: .. code-block:: none Pysteps configuration file found at: /builds/com-swirls/swirlspy/.cache/conda/envs/swirlspy/lib/python3.6/site-packages/pysteps/pystepsrc Computing S-PROG nowcast: ------------------------- Inputs: ------- input dimensions: 500x500 Methods: -------- extrapolation: semilagrangian bandpass filter: gaussian decomposition: fft conditional statistics: no probability matching: mean FFT method: numpy domain: spatial Parameters: ----------- number of time steps: 30 parallel threads: 1 number of cascade levels: 8 order of the AR(p) model: 2 precip. intensity threshold: -10 ************************************************ * Correlation coefficients for cascade levels: * ************************************************ ----------------------------------------- | Level | Lag-1 | Lag-2 | ----------------------------------------- | 1 | 0.998920 | 0.996649 | ----------------------------------------- | 2 | 0.998260 | 0.995759 | ----------------------------------------- | 3 | 0.991719 | 0.979920 | ----------------------------------------- | 4 | 0.968905 | 0.917719 | ----------------------------------------- | 5 | 0.855422 | 0.667819 | ----------------------------------------- | 6 | 0.493524 | 0.215498 | ----------------------------------------- | 7 | 0.084985 | 0.006179 | ----------------------------------------- | 8 | -0.003395 | 0.002382 | ----------------------------------------- **************************************** * AR(p) parameters for cascade levels: * **************************************** ------------------------------------------------------ | Level | Phi-1 | Phi-2 | Phi-0 | ------------------------------------------------------ | 1 | 1.550586 | -0.552262 | 0.038733 | ------------------------------------------------------ | 2 | 1.217599 | -0.219721 | 0.057523 | ------------------------------------------------------ | 3 | 1.207339 | -0.217421 | 0.125356 | ------------------------------------------------------ | 4 | 1.302167 | -0.343957 | 0.232335 | ------------------------------------------------------ | 5 | 1.059279 | -0.238312 | 0.503009 | ------------------------------------------------------ | 6 | 0.511836 | -0.037105 | 0.869133 | ------------------------------------------------------ | 7 | 0.085074 | -0.001051 | 0.996382 | ------------------------------------------------------ | 8 | -0.003387 | 0.002370 | 0.999991 | ------------------------------------------------------ Starting nowcast computation. 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Generating radar reflectivity maps ---------------------------------- Define the color scale and format of the plot. .. code-block:: python # Defining colour scale and format. levels = [ -32768, 10, 15, 20, 24, 28, 32, 34, 38, 41, 44, 47, 50, 53, 56, 58, 60, 62 ] cmap = ListedColormap([ '#FFFFFF00', '#08C5F5', '#0091F3', '#3898FF', '#008243', '#00A433', '#00D100', '#01F508', '#77FF00', '#E0D100', '#FFDC01', '#EEB200', '#F08100', '#F00101', '#E20200', '#B40466', '#ED02F0' ]) norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True) mappable = ScalarMappable(cmap=cmap, norm=norm) mappable.set_array([]) # Defining the crs crs = area_def_tgt.to_cartopy_crs() # Defining area x = frames.coords['x'].values y = frames.coords['y'].values x_d = x[1] - x[0] y_d = y[1] - y[0] extents = [x[0], y[0], x[-1], y[-1]] # Generating a time steps for every hour time_steps = [ basetime + pd.Timedelta(minutes=6*i) for i in range(n_timesteps + 1) if i % 10 == 0 ] ref_frames = conversion.to_reflectivity(forcast_frames, True) ref_frames.data[ref_frames.data < 0.1] = np.nan ref_frames = xr.concat([raw_frames[:-1, ...], ref_frames], dim='time') ref_frames.attrs['values_name'] = 'Reflectivity 2km CAPPI' standardize_attr(ref_frames) qx = motion.coords['x'].values[::5] qy = motion.coords['y'].values[::5] qu = motion.values[0, ::5, ::5] qv = motion.values[1, ::5, ::5] fig: plt.Figure = plt.figure(figsize=(8, 8), frameon=False) gs = GridSpec( 2, 2, figure=fig, wspace=0.03, hspace=-0.25, top=0.95, bottom=0.05, left=0.17, right=0.845 ) for i, t in enumerate(time_steps): row = i // 2 col = i % 2 ax = fig.add_subplot(gs[row, col], projection=crs) # plot base map plot_base(ax, extents, crs) # plot reflectivity frame = ref_frames.sel(time=t) im = ax.imshow(frame.values, cmap=cmap, norm=norm, interpolation='nearest', extent=extents) # plot motion vector ax.quiver(qx, qy, qu, qv, pivot='mid', regrid_shape=20) ax.text( extents[0], extents[1], textwrap.dedent( """ Reflectivity Based @ {baseTime} """ ).format( baseTime=basetime.strftime('%H:%MH') ).strip(), fontsize=10, va='bottom', ha='left', linespacing=1 ) ax.text( extents[2] - (extents[2] - extents[0]) * 0.03, extents[1], textwrap.dedent( """ {validDate} Valid @ {validTime} """ ).format( validDate=basetime.strftime('%Y-%m-%d'), validTime=t.strftime('%H:%MH') ).strip(), fontsize=10, va='bottom', ha='right', linespacing=1 ) cbar_ax = fig.add_axes([0.875, 0.125, 0.03, 0.75]) cbar = fig.colorbar( mappable, cax=cbar_ax, ticks=levels[1:], extend='max', format='%.3g') cbar.ax.set_ylabel(ref_frames.attrs['values_name'], rotation=90) fig.savefig( os.path.join( OUTPUT_DIR, "sprog-reflectivity.png" ), bbox_inches='tight' ) radar_image_time = pd.Timestamp.now() .. image:: /auto_examples/images/sphx_glr_sprog_hk_001.png :class: sphx-glr-single-img Accumulating hourly rainfall for 3-hour forecast ------------------------------------------------ Hourly accumulated rainfall is calculated every 30 minutes, the first endtime is the basetime i.e. T+30min. .. code-block:: python # Optional, convert to rainfall depth rf_frames = conversion.to_rainfall_depth(ref_frames, a=58.53, b=1.56) # Compute hourly accumulated rainfall every 60 minutes. acc_rf_frames = conversion.acc_rainfall_depth( rf_frames, basetime, basetime + pd.Timedelta(hours=3), pd.Timedelta(minutes=60) ) # Replace zero value with NaN acc_rf_frames.data[acc_rf_frames.data <= acc_rf_frames.attrs['zero_value']] = np.nan acc_time = pd.Timestamp.now() Generating radar reflectivity maps ----------------------------------- .. code-block:: python # Defining colour scale and format. levels = [ 0, 0.5, 2, 5, 10, 20, 30, 40, 50, 70, 100, 150, 200, 300, 400, 500, 600, 700 ] cmap = ListedColormap([ '#ffffff00', '#9bf7f7', '#00ffff', '#00d5cc', '#00bd3d', '#2fd646', '#9de843', '#ffdd41', '#ffac33', '#ff621e', '#d23211', '#9d0063', '#e300ae', '#ff00ce', '#ff57da', '#ff8de6', '#ffe4fd' ]) norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True) mappable = ScalarMappable(cmap=cmap, norm=norm) mappable.set_array([]) fig: plt.Figure = plt.figure(figsize=(8, 8), frameon=False) gs = GridSpec( 2, 2, figure=fig, wspace=0.03, hspace=-0.25, top=0.95, bottom=0.05, left=0.17, right=0.845 ) for i, t in enumerate(acc_rf_frames.coords['time'].values): row = i // 2 col = i % 2 ax = fig.add_subplot(gs[row, col], projection=crs) # plot base map plot_base(ax, extents, crs) # plot accumulated rainfall depth t = pd.Timestamp(t) frame = acc_rf_frames.sel(time=t) im = ax.imshow(frame.values, cmap=cmap, norm=norm, interpolation='nearest', extent=extents) ax.text( extents[0], extents[1], textwrap.dedent( """ Hourly Rainfall Based @ {baseTime} """ ).format( baseTime=basetime.strftime('%H:%MH') ).strip(), fontsize=10, va='bottom', ha='left', linespacing=1 ) ax.text( extents[2] - (extents[2] - extents[0]) * 0.03, extents[1], textwrap.dedent( """ {validDate} Valid @ {validTime} """ ).format( validDate=basetime.strftime('%Y-%m-%d'), validTime=t.strftime('%H:%MH') ).strip(), fontsize=10, va='bottom', ha='right', linespacing=1 ) cbar_ax = fig.add_axes([0.875, 0.125, 0.03, 0.75]) cbar = fig.colorbar( mappable, cax=cbar_ax, ticks=levels[1:], extend='max', format='%.3g') cbar.ax.set_ylabel(acc_rf_frames.attrs['values_name'], rotation=90) fig.savefig( os.path.join( OUTPUT_DIR, "sprog-rainfall.png" ), bbox_inches='tight' ) ptime = pd.Timestamp.now() .. image:: /auto_examples/images/sphx_glr_sprog_hk_002.png :class: sphx-glr-single-img Checking run time of each component -------------------------------------------------------------------- .. code-block:: python print(f"Start time: {start_time}") print(f"Initialising time: {initialising_time}") print(f"Motion field time: {motion_time}") print(f"S-PROG time: {sprog_time}") print(f"Plotting radar image time: {radar_image_time}") print(f"Accumulating rainfall time: {acc_time}") print(f"Plotting rainfall maps: {ptime}") print(f"Time to initialise: {initialising_time - start_time}") print(f"Time to run motion field: {motion_time - initialising_time}") print(f"Time to perform S-PROG: {sprog_time - motion_time}") print(f"Time to plot radar image: {radar_image_time - sprog_time}") print(f"Time to accumulate rainfall: {acc_time - radar_image_time}") print(f"Time to plot rainfall maps: {ptime - acc_time}") print(f"Total: {ptime - start_time}") .. rst-class:: sphx-glr-script-out Out: .. code-block:: none Start time: 2025-10-11 04:44:28.464614 Initialising time: 2025-10-11 04:44:34.359015 Motion field time: 2025-10-11 04:44:36.473976 S-PROG time: 2025-10-11 04:44:41.382591 Plotting radar image time: 2025-10-11 04:44:52.709358 Accumulating rainfall time: 2025-10-11 04:44:53.500320 Plotting rainfall maps: 2025-10-11 04:45:02.894817 Time to initialise: 0 days 00:00:05.894401 Time to run motion field: 0 days 00:00:02.114961 Time to perform S-PROG: 0 days 00:00:04.908615 Time to plot radar image: 0 days 00:00:11.326767 Time to accumulate rainfall: 0 days 00:00:00.790962 Time to plot rainfall maps: 0 days 00:00:09.394497 Total: 0 days 00:00:34.430203 **Total running time of the script:** ( 0 minutes 34.030 seconds) .. _sphx_glr_download_auto_examples_sprog_hk.py: .. only :: html .. container:: sphx-glr-footer :class: sphx-glr-footer-example .. container:: sphx-glr-download :download:`Download Python source code: sprog_hk.py ` .. container:: sphx-glr-download :download:`Download Jupyter notebook: sprog_hk.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_