QPE (Manila)

This example demonstrates how to perform QPE, using raingauge data from Manila and radar data from Subic.

Definitions

Import all required modules and methods:

# 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 projection description
import pyproj
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 colorbars
from matplotlib.colors import BoundaryNorm, ListedColormap

# swirlspy raingauge data object
from swirlspy.obs import Rain
# swirlspy Philippine UF file parser function
from swirlspy.rad.uf_ph import read_uf_ph
# swirlspy raingauge data interpolate and blending
from swirlspy.qpe.rfmap import rg_interpolate, comp_qpe, show_raingauge
# swirlspy test data source locat utils function
from swirlspy.qpe.utils import timestamps_ending, locate_file
# swirlspy standardize data function
from swirlspy.utils import FrameType, standardize_attr, conversion
# directory constants
from swirlspy.tests.samples import DATA_DIR
from swirlspy.tests.outputs import OUTPUT_DIR

warnings.filterwarnings("ignore")
plt.switch_backend('agg')

Initialising

This section demonstrates extracting raingauge and radar data.

Step 1: Defining an end-time for accumulating rainfall.

acctime = pd.Timestamp('20180811112000').floor('min')
acctime_str = acctime.strftime('%Y%m%d%H%M')

Step 2: Setting up directories for raingauge and radar files.

rad_dir = os.path.join(DATA_DIR, 'uf_ph/sub')
rg_dir = os.path.join(DATA_DIR, 'rfmap')

Step 3: Generating timestamps and pattern for both radar and raingauge files.

# Timestamps of raingauges
rg_timestrings = timestamps_ending(
    acctime + pd.Timedelta(minutes=10),
    duration=pd.Timedelta(hours=1),
    interval=pd.Timedelta(minutes=10)
)

# Raingauge pattern
rg_pattern = ['rf60m_20'+ts for ts in rg_timestrings]

# Finding time nearest radar file
# to accumulation end time
minute = acctime.minute
nearest_6min = acctime.minute // 10 * 10

nearest_rad_timestamp = pd.Timestamp(
    acctime_str[:-2]+f'{nearest_6min:02}'
)

rad_timestrings = timestamps_ending(
    nearest_rad_timestamp,
    duration=pd.Timedelta(hours=1),
    interval=pd.Timedelta(minutes=10)
)

Step 4: Extracting raingauge and radar files from their respective directories.

located_rg_files = []
for pat in rg_pattern:
    located_rg_files.append(locate_file(rg_dir, pat))


located_radar_files = []
for ts in rad_timestrings:
    located_radar_files.append(locate_file(rad_dir, ts))

Step 5: Read data from raingauge files into a Rain object. Coordinates are geodetic, following that in the files.

rg_object_geodetic = Rain(
    located_rg_files,
    'WGS84',
    duration=pd.Timedelta(minutes=5),
    NAN=[3276.7, 32767]
)

Step 6: Define the target grid as a pyresample AreaDefinition.

# Defining target grid
area_id = "epsg3123_240km"
description = ("A 240 m resolution rectangular grid "
               "centred at Subic RADAR and extending to 240 km "
               "in each direction")
proj_id = 'epsg3123'
projection = ('+proj=tmerc +lat_0=0 '
              '+lon_0=121 +k=0.99995 +x_0=500000 '
              '+y_0=0 +ellps=clrk66 +towgs84=-127.62,-67.24,'
              '-47.04,-3.068,4.903,1.578,-1.06 +units=m '
              '+no_defs')
x_size = 1000
y_size = 1000
area_extent = (191376.04113, 1399386.68659, 671376.04113, 1879386.68659)
area_def = get_area_def(
    area_id, description, proj_id, projection, x_size, y_size, area_extent
)

Step 7: Convert coordinates of raingauge object to desired projection. In this example, the desired projection is PRS92. This can be achieved by the .reproject() method of the Rain object.

inProj = pyproj.Proj(init="epsg:4326")
outProj = pyproj.Proj(area_def.proj_str)

rg_object = rg_object_geodetic.reproject(inProj, outProj, "PRS92")

Step 8: Read radar files into xarray.DataArrays using read_uf_ph().

reflec_list = []
for file in located_radar_files:
    reflec = read_uf_ph(
        file, area_def=area_def,
        coord_label=['easting', 'northing'],
        indicator='deg_ppi', elevation=0.5
    )

    reflec_list.append(reflec)

reflectivity = xr.concat(reflec_list, dim='time')
standardize_attr(reflectivity, frame_type=FrameType.dBZ)

Accumulating and interpolating rainfall

Interpolate rainfall recorded by raingauges into the user-defined grid and accumulate radar rainfall over an hour after making the necessary adjustments.

Step 1: Interpolate Rain object to user-defined grid. In this example, a multiquadric Radial Basis Function is used.

interpolated_rg = rg_interpolate(
    rg_object, area_def, 'rbf',
    coord_label=['easting', 'northing']
)

Step 2: Convert to radar reflectivity to rainrates, convert rainrates to times of raingauges, and accumulate rainfalls every 10 minutes.

rainrates = conversion.to_rainfall_rate(reflectivity, False, a=300, b=1.4)

# Convert time coordinates of rainrates from start time
# to end time
rainrates_time_endtime = rainrates.copy()
rainrates_time_endtime.coords['time'] = \
    [
        pd.Timestamp(t) + pd.Timedelta(minutes=10)
        for t in rainrates.coords['time'].values
]

standardize_attr(rainrates_time_endtime, frame_type=FrameType.mmph)
rainfalls = conversion.to_rainfall_depth(rainrates_time_endtime)

Step 3: Accumulate rainfall over an hour.

acc_rf = conversion.acc_rainfall_depth(
    rainfalls,
    rg_object.end_time,
    rg_object.end_time,
    pd.Timedelta(minutes=60)
)

Compositing rainfall

Perform compositing on radar and raingauge derived rainfall to obtain a composite QPE.

comprf = comp_qpe(
    area_def,
    rg_object=rg_object,
    rg_interp=interpolated_rg,
    rad_rf=acc_rf
)

Plotting

Plot composited radar and raingauge rainfall.

# Defining coastlines
map_shape_file = os.path.join(DATA_DIR, "shape/rsmc")
ocean_color = np.array([[[178, 208, 254]]], dtype=np.uint8)
land_color = cfeature.COLORS['land']
coastline = cfeature.ShapelyFeature(
    list(shpreader.Reader(map_shape_file).geometries()),
    ccrs.PlateCarree()
)

# Plotting function for neatness.


def plot_map(
    da, rg_object, acctime, area_def,
    based='raingauge and radar',
    savepath='', area_extent=None
):
    """
    A custom function for plotting a map.

    Parameters
    --------------
    da: xarray.DataArray
        Contains data to be plotted.
    rg_object: Rain
        Contains raingauge data.
    acctime: pd.Timestamp
        Contains the endtime of the accumulation
        period.
    area_def: pyresample.geometry.AreaDefinition
        AreaDefinition of the grid.
    based: str
        Type of data plotted in the map.
    savepath: str
        Path to save the image to.
    area_extent: tuple
        Area extent (x0, x1, y0, y1) to be plotted.
        Defaults to None.

    """
    # Defining the colour scheme
    levels = [
        0, 0.5, 2, 5, 10, 20,
        30, 40, 50, 70, 100, 150,
        200, 300, 400, 500, 600, 700
    ]

    cmap = ListedColormap([
        '#ffffff', '#9bf7f7', '#00ffff', '#00d5cc', '#00bd3d', '#2fd646',
        '#9de843', '#ffdd41', '#ffac33', '#ff621e', '#d23211', '#9d0063',
        '#e300ae', '#ff00ce', '#ff57da', '#ff8de6', '#ffe4fd'
    ])

    norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True)

    # Plotting axes
    plt.figure(figsize=(24, 21))
    crs = area_def.to_cartopy_crs()
    ax = plt.axes(projection=crs)

    if area_extent is not None:
        ax.set_extent(area_extent, crs=crs)

    # Plot data
    quadmesh = da.plot(
        cmap=cmap,
        norm=norm,
        extend='max',
        cbar_kwargs={'ticks': levels, 'format': '%.3g'}
    )

    # Adjusting size of colorbar
    cb = quadmesh.colorbar
    cb.ax.set_ylabel(
        da.attrs['long_name']+'['+da.attrs['units']+']',
        fontsize=28
    )

    cb.ax.tick_params(labelsize=24)

    # Setting labels
    ax.xaxis.set_visible(True)
    ax.yaxis.set_visible(True)

    for tick in ax.xaxis.get_major_ticks():
        tick.label.set_fontsize(24)
    for tick in ax.yaxis.get_major_ticks():
        tick.label.set_fontsize(24)

    ax.xaxis.label.set_size(28)
    ax.yaxis.label.set_size(28)

    # ocean
    ax.imshow(np.tile(ocean_color, [2, 2, 1]),
              origin='upper',
              transform=ccrs.PlateCarree(),
              extent=[-180, 180, -180, 180],
              zorder=-1)
    # coastline, color
    ax.add_feature(coastline,
                   facecolor=land_color, edgecolor='none', zorder=0)
    # overlay coastline without color
    ax.add_feature(coastline, facecolor='none',
                   edgecolor='gray', linewidth=0.5, zorder=3)

    # Show raingauge
    show_raingauge(
        rg_object, ax, show_value=True, color='red', markersize=20,
        fontsize=20
    )
    # Show title
    plt.title(
        (f"Last Hour Rainfall at {acctime.strftime('%H:%MH %d-%b-%Y')}\n"
         f"(based on {based} data)"),
        fontsize=32
    )

    plt.savefig(savepath)
# Plotting maps


r = 64000
proj_site = reflectivity.proj_site
area_extent = (
    proj_site[0]-r,
    proj_site[0]+r,
    proj_site[1]-r,
    proj_site[1]+r
)

# Raingauge only
plot_map(
    interpolated_rg, rg_object,
    acctime, area_def,
    based='raingauge',
    savepath=os.path.join(OUTPUT_DIR, f'raingauge_{acctime_str}.png'),
    area_extent=area_extent
)

# Radar only
plot_map(
    acc_rf, rg_object,
    acctime, area_def,
    based='radar',
    savepath=os.path.join(OUTPUT_DIR, f'radar_{acctime_str}.png'),
    area_extent=area_extent
)

# Composite raingauge and radar
plot_map(
    comprf, rg_object,
    acctime, area_def,
    savepath=os.path.join(OUTPUT_DIR, f'comp_{acctime_str}.png'),
    area_extent=area_extent
)
  • ../_images/sphx_glr_qpe_manila_001.png
  • ../_images/sphx_glr_qpe_manila_002.png
  • ../_images/sphx_glr_qpe_manila_003.png

Total running time of the script: ( 0 minutes 31.258 seconds)

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