.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/qpf_ms.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. 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_qpf_ms.py: QPF (Malaysia) ======================================================== This example demonstrates how to perform operational deterministic QPF up to three hours using national radar data. .. GENERATED FROM PYTHON SOURCE LINES 10-13 Definitions -------------------------------------------------------- .. GENERATED FROM PYTHON SOURCE LINES 15-16 Import all required modules and methods: .. GENERATED FROM PYTHON SOURCE LINES 16-54 .. code-block:: default # Python package to allow system command line functions import os # Python package to manage warning message import warnings # Python package for timestamp import pandas as pd # Python package for xarrays to read and handle netcdf data import xarray as xr # Python package for numerical calculations import numpy as np # Python package for reading map shape file import cartopy.io.shapereader as shpreader # Python package for land/sea features import cartopy.feature as cfeature # Python package for projection import cartopy.crs as ccrs # Python package for creating plots from matplotlib import pyplot as plt # Python package for output import grid from matplotlib.gridspec import GridSpec # Python package for colorbars from matplotlib.colors import BoundaryNorm, ListedColormap # Python package for scalar data to RGBA mapping from matplotlib.cm import ScalarMappable # Python com-swirls package to standardize attributes from swirlspy.utils import standardize_attr, FrameType # Python com-swirls package to calculate motion field (rover) and semi-lagrangian advection from swirlspy.qpf import rover, sla # directory constants from swirlspy.tests.samples import DATA_DIR from swirlspy.tests.outputs import OUTPUT_DIR warnings.filterwarnings("ignore") start_time = pd.Timestamp.now() .. GENERATED FROM PYTHON SOURCE LINES 55-63 Initialising --------------------------------------------------- This section demonstrates extracting radar reflectivity data. Step 1: Define your input data directory and output directory .. GENERATED FROM PYTHON SOURCE LINES 63-70 .. code-block:: default # Supply the directory of radar and nwp data data_dir = os.path.abspath( os.path.join(DATA_DIR, 'netcdf_ms') ) .. GENERATED FROM PYTHON SOURCE LINES 71-73 Step 2: Define a basetime .. GENERATED FROM PYTHON SOURCE LINES 73-77 .. code-block:: default # Supply basetime basetime = pd.Timestamp('201908090900') .. GENERATED FROM PYTHON SOURCE LINES 78-80 Step 3: Read data files from the radar data using xarray() .. GENERATED FROM PYTHON SOURCE LINES 80-108 .. code-block:: default # Radar data listed from the basetime[0] --> 3 hours before the basetime[17] (descending time) interval = 10 # Interval of radar data radar_datas = [] for i in range(0, 2): t = basetime - pd.Timedelta(minutes=i * interval) # Radar data nomenclature filename = os.path.join( data_dir, t.strftime("radar_d03_%Y-%m-%d_%H_%M_00.rapids.nc") ) reflec = xr.open_dataset(filename) radar_datas.append(reflec) # Concatenate list by time reflec_concat = xr.concat(radar_datas, dim='time') # Extracting the radar data: The radar dBZ variable is named 'Zradar', therefore, we extract 'Zradar' radar = reflec_concat['Zradar'] # Reversing such that time goes from earliest to latest; 3 hours before basetime[0] --> basetime[17] radar = radar.sortby('time', ascending=True) # Filtering radar = radar.where(radar > 15, np.nan) initialising_time = pd.Timestamp.now() .. GENERATED FROM PYTHON SOURCE LINES 109-115 Nowcast (SWIRLS-Radar-Advection) --------------------------------------------------- The swirls radar advection was performed using the observed radar data Firstly, some attributes necessary for com-swirls input variable is added Secondly, rover function is invoked to compute the motion field Thirdly, semi-lagrangian advection is performed to advect the radar data using the rover motion field .. GENERATED FROM PYTHON SOURCE LINES 115-127 .. code-block:: default # Adding in some attributes that is step_size <10 mins in pandas.Timedelta>, zero_value <9999.> frame_type standardize_attr(radar, frame_type=FrameType.dBZ, zero_value=np.nan) # Rover motion field computation motion = rover(radar) rover_time = pd.Timestamp.now() # Semi-Lagrangian Advection swirls = sla(radar, motion, 18) # Radar time goes from earliest to latest sla_time = pd.Timestamp.now() .. rst-class:: sphx-glr-script-out .. code-block:: none RUNNING 'rover' FOR EXTRAPOLATION..... .. GENERATED FROM PYTHON SOURCE LINES 128-131 Plotting result --------------------------------------------------- Step 1: Defining the dBZ levels, colorbar parameters and projection .. GENERATED FROM PYTHON SOURCE LINES 131-179 .. code-block:: default # levels of colorbar (dBZ) levels = [-32768, 10, 15, 20, 24, 28, 32, 34, 38, 41, 44, 47, 50, 53, 56, 58, 60, 62] # hko colormap for dBZ at each levels cmap = ListedColormap([ '#FFFFFF', '#08C5F5', '#0091F3', '#3898FF', '#008243', '#00A433', '#00D100', '#01F508', '#77FF00', '#E0D100', '#FFDC01', '#EEB200', '#F08100', '#F00101', '#E20200', '#B40466', '#ED02F0' ]) # boundary norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True) # scalar data to RGBA mapping scalar_map = ScalarMappable(cmap=cmap, norm=norm) scalar_map.set_array([]) # Defining plot parameters map_shape_file = os.path.abspath(os.path.join( DATA_DIR, 'shape/se_asia' )) # coastline and province se_asia = cfeature.ShapelyFeature( list(shpreader.Reader(map_shape_file).geometries()), ccrs.PlateCarree() ) # output area extents = [99, 120, 0.5, 7.25] # base_map plotting function def plot_base(ax: plt.Axes): ax.set_extent(extents, crs=ccrs.PlateCarree()) # 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, state, color ax.add_feature(se_asia, facecolor=cfeature.COLORS['land'], edgecolor='none', zorder=0) # overlay coastline, state without color ax.add_feature(se_asia, facecolor='none', edgecolor='gray', linewidth=0.5) ax.set_title('') .. GENERATED FROM PYTHON SOURCE LINES 180-182 Step 2: Plotting the swirls-radar-advection, nwp-bias-corrected, blended 3 hours ahead .. GENERATED FROM PYTHON SOURCE LINES 182-244 .. code-block:: default fig: plt.Figure = plt.figure( figsize=(5 + 1, 3 * 2), frameon=False ) gs = GridSpec( 3, 1, figure=fig, wspace=0.03, hspace=0, top=0.95, bottom=0.05, left=0.17, right=0.845 ) for row in range(3): time_index = (row + 1) * 6 timelabel = basetime + pd.Timedelta(interval * (time_index), 'm') ax: plt.Axes = fig.add_subplot( gs[row, 0], projection=ccrs.PlateCarree() ) z = swirls[time_index].values lats = swirls[time_index].latitude lons = swirls[time_index].longitude title = 'SWIRLS Reflectivity' # plot base map plot_base(ax) # plot reflectivity ax.contourf( lons, lats, z, 60, transform=ccrs.PlateCarree(), cmap=cmap, norm=norm, levels=levels ) ax.set_title( f"{title}\n" + f"Initial @ {basetime.strftime('%H:%MZ')}", loc='left', fontsize=9 ) ax.set_title('') ax.set_title( f"Initial {basetime.strftime('%Y-%m-%d')} \n" + f"Forecast Valid @ {timelabel.strftime('%H:%MZ')} ", loc='right', fontsize=9 ) cbar_ax = fig.add_axes([0.9, 0.105, 0.04, 0.845]) cbar = fig.colorbar( scalar_map, cax=cbar_ax, ticks=levels[1:], extend='max', format='%.3g' ) cbar.ax.set_ylabel('Reflectivity (dBZ)', rotation=90) fig.savefig( os.path.join( OUTPUT_DIR, "swirls_ms_fcs.png" ), dpi=450, bbox_inches="tight", pad_inches=0.1 ) radar_image_time = pd.Timestamp.now() .. image-sg:: /auto_examples/images/sphx_glr_qpf_ms_001.png :alt: SWIRLS Reflectivity Initial @ 09:00Z, Initial 2019-08-09 Forecast Valid @ 10:00Z , SWIRLS Reflectivity Initial @ 09:00Z, Initial 2019-08-09 Forecast Valid @ 11:00Z , SWIRLS Reflectivity Initial @ 09:00Z, Initial 2019-08-09 Forecast Valid @ 12:00Z :srcset: /auto_examples/images/sphx_glr_qpf_ms_001.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 245-248 Checking run time of each component -------------------------------------------------------------------- .. GENERATED FROM PYTHON SOURCE LINES 248-258 .. code-block:: default print(f"Start time: {start_time}") print(f"Initialising time: {initialising_time}") print(f"SLA time: {sla_time}") print(f"Plotting radar image time: {radar_image_time}") print(f"Time to initialise: {initialising_time - start_time}") print(f"Time to run rover: {rover_time - initialising_time}") print(f"Time to perform SLA: {sla_time - rover_time}") print(f"Time to plot radar image: {radar_image_time - sla_time}") .. rst-class:: sphx-glr-script-out .. code-block:: none Start time: 2024-04-22 03:55:11.352939 Initialising time: 2024-04-22 03:55:11.512528 SLA time: 2024-04-22 03:58:11.804893 Plotting radar image time: 2024-04-22 03:58:22.139918 Time to initialise: 0 days 00:00:00.159589 Time to run rover: 0 days 00:00:01.169084 Time to perform SLA: 0 days 00:02:59.123281 Time to plot radar image: 0 days 00:00:10.335025 .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 3 minutes 11.298 seconds) .. _sphx_glr_download_auto_examples_qpf_ms.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: qpf_ms.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: qpf_ms.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_