.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/qpf_laos.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_laos.py: QPF (Laos) ======================================================== This example demonstrates how to perform operational deterministic QPF up to three hours using reflectivity 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 16-43 .. 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 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") # data files data_paths = [ os.path.abspath(os.path.join(DATA_DIR, 'laos_h8/laos_20190731070000.nc')), os.path.abspath(os.path.join(DATA_DIR, 'laos_h8/laos_20190731071000.nc')), ] .. GENERATED FROM PYTHON SOURCE LINES 44-50 Initialising --------------------------------------------------- This section demonstrates preprocessing reflectivity data. .. GENERATED FROM PYTHON SOURCE LINES 50-63 .. code-block:: default # Read data from saved files reflec_datas = [] for file_path in data_paths: d = xr.open_dataarray(file_path) reflec_datas.append(d) # Concatenate list on time axis data_frames = xr.concat(reflec_datas, dim='time') # Make sure data is ordered by time data_frames = data_frames.sortby('time', ascending=True) .. GENERATED FROM PYTHON SOURCE LINES 64-70 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 70-82 .. code-block:: default # Adding in some attributes that is step_size <10 mins in pandas.Timedelta>, zero_value and frame_type standardize_attr(data_frames) # Rover motion field computation motion = rover(data_frames) rover_time = pd.Timestamp.now() # Semi-Lagrangian Advection swirls = sla(data_frames, motion, 17) # Radar time goes from earliest to latest .. rst-class:: sphx-glr-script-out .. code-block:: none RUNNING 'rover' FOR EXTRAPOLATION..... .. GENERATED FROM PYTHON SOURCE LINES 83-86 Plotting result --------------------------------------------------- Step 1: Import plotting library and necessary library .. GENERATED FROM PYTHON SOURCE LINES 86-104 .. code-block:: default # 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 plt.switch_backend('agg') .. GENERATED FROM PYTHON SOURCE LINES 105-107 Step 2: Defining the dBZ levels, colorbar parameters and projection .. GENERATED FROM PYTHON SOURCE LINES 107-155 .. 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([ '#FFFFFF00', '#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 = [99.5, 108., 13.75, 22.75] # 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 156-158 Step 3: Filtering values <= 15dbZ are not plotted .. GENERATED FROM PYTHON SOURCE LINES 158-166 .. code-block:: default swirls = swirls.where(swirls > 15, np.nan) # Defining motion quivers qx = motion.coords['x'].values[::5] qy = motion.coords['y'].values[::5] qu = motion.values[0, ::5, ::5] qv = motion.values[1, ::5, ::5] .. GENERATED FROM PYTHON SOURCE LINES 167-169 Step 4: Plotting the swirls-radar-advection, nwp-bias-corrected, blended 3 hours ahead .. GENERATED FROM PYTHON SOURCE LINES 169-235 .. code-block:: default fig: plt.Figure = plt.figure( figsize=(3 * 3.5 + 1, 3), frameon=False ) gs = GridSpec( 1, 3, figure=fig, wspace=0, hspace=0, top=0.95, bottom=0.05, left=0.17, right=0.845 ) basetime = pd.Timestamp(swirls.time.values[0]) interval = pd.Timedelta(swirls.attrs['step_size']) for col in range(3): time_index = (col + 1) * 6 - 1 timelabel = basetime + pd.Timedelta(interval * (time_index + 1), 'm') ax: plt.Axes = fig.add_subplot( gs[0, col], projection=ccrs.PlateCarree() ) z = swirls[time_index].values y = swirls[time_index].y x = swirls[time_index].x # plot base map plot_base(ax) # plot reflectivity ax.contourf( x, y, z, 60, transform=ccrs.PlateCarree(), cmap=cmap, norm=norm, levels=levels ) # plot motion field ax.quiver(qx, qy, qu, qv, pivot='mid', regrid_shape=20) ax.set_title( f"Nowcast\n" + f"Initial @ {basetime.strftime('%H:%MZ')}", loc='left', fontsize=8.75 ) ax.set_title('') ax.set_title( f"Initial {basetime.strftime('%Y-%m-%d')} \n" + f"Valid @ {timelabel.strftime('%H:%MZ')} ", loc='right', fontsize=8.75 ) cbar_ax = fig.add_axes([0.85, 0.105, 0.01, 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, "qpf_laos.png" ), dpi=450, bbox_inches="tight", pad_inches=0.25 ) radar_image_time = pd.Timestamp.now() .. image-sg:: /auto_examples/images/sphx_glr_qpf_laos_001.png :alt: Nowcast Initial @ 07:10Z, Initial 2019-07-31 Valid @ 08:10Z , Nowcast Initial @ 07:10Z, Initial 2019-07-31 Valid @ 09:10Z , Nowcast Initial @ 07:10Z, Initial 2019-07-31 Valid @ 10:10Z :srcset: /auto_examples/images/sphx_glr_qpf_laos_001.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 19.753 seconds) .. _sphx_glr_download_auto_examples_qpf_laos.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_laos.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: qpf_laos.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_