QPF (Manila)

This example demonstrates how to perform operational deterministic QPF up to three hours using raingauge data from Manila and radar data from Subic.

Definitions

import os
import numpy as np
import pandas as pd
import xarray as xr
import cartopy.feature as cfeature
import cartopy.crs as ccrs
import cartopy.io.shapereader as shpreader
import matplotlib.pyplot as plt
from matplotlib.colors import BoundaryNorm, ListedColormap
from pyresample import utils

from swirlspy.rad.uf_ph import read_uf_ph
from swirlspy.qpe.utils import locate_file, timestamps_ending
from swirlspy.qpf import rover
from swirlspy.qpf import sla
from swirlspy.utils import standardize_attr, FrameType
from swirlspy.utils.conversion import to_rainfall_depth, acc_rainfall_depth
from swirlspy.core.resample import grid_resample

plt.switch_backend('agg')

THIS_DIR = os.getcwd()
os.chdir(THIS_DIR)

start_time = pd.Timestamp.now()

Initialising

This section demonstrates extracting radar reflectivity data.

Step 1: Define a basetime.

# Supply basetime
basetime = pd.Timestamp('20180811112000').floor('min')

Step 2: Using basetime, generate timestamps of desired radar files timestamps_ending() and locate files using locate_file().

# Obtain radar files
dir = THIS_DIR + '/../tests/samples/uf_ph/sub/'
located_files = []
radar_ts = timestamps_ending(
    basetime,
    duration=pd.Timedelta(60, 'm'),
    interval=pd.Timedelta(10, 'm')
)

for timestamp in radar_ts:
    located_files.append(locate_file(dir, timestamp))

Step 3: Define the target grid as a pyresample AreaDefinition.

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 = 500
y_size = 500
area_extent = (191376.04113, 1399386.68659, 671376.04113, 1879386.68659)
area_def = utils.get_area_def(
    area_id, description, proj_id, projection, x_size, y_size, area_extent
)

Step 4: Read data from radar files into xarray.DataArray using read_uf_ph().

reflectivity_list = []  # stores reflec from read_iris()
for filename in located_files:
    reflec = read_uf_ph(
        filename, area_def=area_def,
        coord_label=['easting', 'northing'],
        indicator='cappi', elevation=2
    )
    reflectivity_list.append(reflec)

Step 5: Assigning reflectivity xarrays at the last two timestamps to variables for use during ROVER QPF.

initialising_time = pd.Timestamp.now()

Running ROVER and Semi-Lagrangian Advection

  1. Concatenate two reflectivity xarrays along time dimension.
  2. Run ROVER, with the concatenated xarray as the input.
  3. Perform Semi-Lagrangian Advection using the motion fields from rover.
# Combining the two reflectivity DataArrays
# the order of the coordinate keys is now ['y', 'x', 'time']
# as opposed to ['time', 'x', 'y']
reflec_concat = xr.concat(reflectivity_list, dim='time')
standardize_attr(reflec_concat, frame_type=FrameType.dBZ, zero_value=9999.)

# Rover
motion = rover(reflec_concat)

rover_time = pd.Timestamp.now()

# Semi Lagrangian Advection
reflectivity = sla(reflec_concat, motion, nowcast_steps=30)

sla_time = pd.Timestamp.now()

Out:

RUNNING 'rover' FOR EXTRAPOLATION.....

Concatenating observed and forecasted reflectivities

  1. Add forecasted reflectivity to reproj_reflectivity_list.
  2. Concatenate observed and forecasted reflectivity xarray.DataArrays along the time dimension.
reflectivity = xr.concat([reflec_concat[:-1, ...], reflectivity], dim='time')
reflectivity.attrs['long_name'] = 'Reflectivity 2km CAPPI'
standardize_attr(reflectivity)

concat_time = pd.Timestamp.now()

Generating radar reflectivity maps

Define the color scale and format of the plots and plot using xarray.plot().

In this example, only hourly images will be plotted.

# 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([
    '#FFFFFF', '#08C5F5', '#0091F3', '#3898FF', '#008243', '#00A433',
    '#00D100', '#01F508', '#77FF00', '#E0D100', '#FFDC01', '#EEB200',
    '#F08100', '#F00101', '#E20200', '#B40466', '#ED02F0'
])

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

# Defining the crs
crs = area_def.to_cartopy_crs()

# Generating a timelist for every hour
timelist = [
    (basetime + pd.Timedelta(minutes=60*i-10)) for i in range(4)
]

# Obtaining the slice of the xarray to be plotted
da_plot = reflectivity.sel(time=timelist)

# Defining motion quivers
qx = motion.coords['easting'].values[::5]
qy = motion.coords['northing'].values[::5]
qu = motion.values[0, ::5, ::5]
qv = motion.values[1, ::5, ::5]

# Defining coastlines
map_shape_file = os.path.join(THIS_DIR, "./../tests/samples/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
p = da_plot.plot(
    col='time', col_wrap=2,
    subplot_kws={'projection': crs},
    cbar_kwargs={
        'extend': 'max',
        'ticks': levels[1:],
        'format': '%.3g'
    },
    cmap=cmap,
    norm=norm
)
for idx, ax in enumerate(p.axes.flat):
    # 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)

    ax.quiver(qx, qy, qu, qv, pivot='mid', regrid_shape=20)
    ax.gridlines()
    ax.set_title(
        "Reflectivity\n"
        f"Based @ {basetime.strftime('%H:%MH')}",
        loc='left',
        fontsize=9
    )
    ax.set_title(
        ''
    )
    ax.set_title(
        f"{basetime.strftime('%Y-%m-%d')} \n"
        f"Valid @ {timelist[idx].strftime('%H:%MH')} ",
        loc='right',
        fontsize=9
    )
plt.savefig(
    THIS_DIR +
    f"/../tests/outputs/rover-output-map-mn.png",
    dpi=300
)

radar_image_time = pd.Timestamp.now()
../_images/sphx_glr_qpf_manila_001.png

Accumulating hourly rainfall for 3-hour forecast

Hourly accumulated rainfall is calculated every 30 minutes, the first endtime is the basetime i.e. T+0min.

  1. Convert rainrates to rainfalls in 10 mins with to_rainfall_depth().
  2. Accumulate hourly rainfall every 30 minutes using acc_rainfall_depth().
# Convert reflectivity to rainrates
rainfalls = to_rainfall_depth(reflectivity, a=300, b=1.4)

# Converting the coordinates of xarray from start to endtime
rainfalls.coords['time'] = [
    pd.Timestamp(t) + pd.Timedelta(10, 'm')
    for t in rainfalls.coords['time'].values
]
rainfalls.attrs['step_size'] = pd.Timedelta(10, 'm')

# Accumulate hourly rainfall every 30 minutes
acc_rf = acc_rainfall_depth(
    rainfalls,
    basetime,
    basetime+pd.Timedelta(hours=3)
)
acc_rf.attrs['long_name'] = 'Rainfall accumulated over the past 60 minutes'

acc_time = pd.Timestamp.now()

Plotting rainfall maps

Define the colour scheme and format and plot using xarray.plot().

In this example, only hourly images will be plotted.

# 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)

# Defining projection
crs = area_def.to_cartopy_crs()
# Defining zoom extent
r = 64000
proj_site = acc_rf.proj_site
zoom = (
    proj_site[0]-r, proj_site[0]+r, proj_site[1]-r, proj_site[1]+r
)  # (x0, x1, y0, y1)

# Defining times for plotting
timelist = [basetime + pd.Timedelta(i, 'h') for i in range(4)]

# Obtaining xarray slice to be plotted
da_plot = acc_rf.sel(
    easting=slice(zoom[0], zoom[1]),
    northing=slice(zoom[3], zoom[2]),
    time=timelist
)

# Plotting
p = da_plot.plot(
    col='time', col_wrap=2,
    subplot_kws={'projection': crs},
    cbar_kwargs={
        'extend': 'max',
        'ticks': levels,
        'format': '%.3g'
    },
    cmap=cmap,
    norm=norm
)
for idx, ax in enumerate(p.axes.flat):
    # 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)

    ax.gridlines()
    ax.set_xlim(zoom[0], zoom[1])
    ax.set_ylim(zoom[2], zoom[3])
    ax.set_title(
        "Past Hour Rainfall\n"
        f"Based @ {basetime.strftime('%H:%MH')}",
        loc='left',
        fontsize=8
    )
    ax.set_title(
        ''
    )
    ax.set_title(
        f"{basetime.strftime('%Y-%m-%d')} \n"
        f"Valid @ {timelist[idx].strftime('%H:%MH')} ",
        loc='right',
        fontsize=8
    )
plt.savefig(
    THIS_DIR +
    f"/../tests/outputs/rainfall_mn.png",
    dpi=300
)

rf_image_time = pd.Timestamp.now()
../_images/sphx_glr_qpf_manila_002.png

Extract the rainfall values at a specified location

In this example, the rainfall values at the location is assumed to be the same as the nearest gridpoint.

  1. Read information regarding the rain gauge stations into a pandas.DataFrame.
  2. Extract the rainfall values at the nearest gridpoint to location for given timesteps (in this example, 30 minute intervals).
  3. Store rainfall values over time in a pandas.DataFrame.
  4. Plot the time series of rainfall at different stations.
# Getting rain gauge station coordinates
df = pd.read_csv(
    os.path.join(THIS_DIR, "../tests/samples/manila_rg_list.csv"),
    delim_whitespace=True,
    usecols=[0, 3, 4]
)

# Extract rainfall values at gridpoint closest to the
# location specified for given timesteps and storing it
# in pandas.DataFrame.

rf_time = []
for time in acc_rf.coords['time'].values:
    rf = []
    for index, row in df.iterrows():
        rf.append(acc_rf.sel(
            time=time, northing=row[2],
            easting=row[1],
            method='nearest'
        ).values)
    rf_time.append(rf)

rf_time = np.array(rf_time)

station_rf = pd.DataFrame(
    data=rf_time,
    columns=df.iloc[:, 0],
    index=pd.Index(
        acc_rf.coords['time'].values,
        name='time'
    )
)

print(station_rf)

loc_stn = \
    ['BAA', 'BUM', 'PAF', 'QUL', 'ZAP', 'ZAA']
loc_stn_drop = [
    stn for stn in station_rf.columns.to_list()
    if stn not in loc_stn
]
df_loc = station_rf.drop(loc_stn_drop, axis=1)

print(df_loc)

# Plotting time series graph for selected stations
ax = df_loc.plot(title="Time Series of Hourly Accumulated Rainfall",
                 grid=True)
ax.set_ylabel("Hourly Accumulated Rainfall (mm)")
plt.savefig(THIS_DIR+"/../tests/outputs/qpf_time_series.png")

extract_time = pd.Timestamp.now()
../_images/sphx_glr_qpf_manila_003.png

Out:

ID                        BAA       BAL       BAT  ...       ZAB       ZAM       ZAP
time                                               ...
2018-08-11 11:20:00  0.017007  0.223224  0.017007  ...  2.888977  3.786356  9.242831
2018-08-11 11:50:00  0.017007  0.086242  0.017007  ...  0.764354  3.247153  4.608823
2018-08-11 12:20:00  0.017007  0.020247  0.017007  ...  0.270428  0.168942  0.057691
2018-08-11 12:50:00  0.017007  0.017007  0.017007  ...  0.267216  8.327499  0.020052
2018-08-11 13:20:00  0.017007  0.017007  0.017007  ...  0.248028  8.176266  0.106470
2018-08-11 13:50:00  0.017007  0.017007  0.017007  ...  0.205860  0.017709  0.441282
2018-08-11 14:20:00  0.017007  0.017007  0.017007  ...  0.118519  0.020832  0.379346

[7 rows x 33 columns]
ID                        BAA       BUM       PAF       QUL       ZAA       ZAP
time
2018-08-11 11:20:00  0.017007  2.371299  2.161147  0.029432  4.374117  9.242831
2018-08-11 11:50:00  0.017007  0.365518  1.003360  0.175768  4.596762  4.608823
2018-08-11 12:20:00  0.017007  0.750153  0.077334  0.500229  0.514771  0.057691
2018-08-11 12:50:00  0.017007  0.827638  0.045105  0.490023  0.027347  0.020052
2018-08-11 13:20:00  0.017007  0.146383  0.017007  0.159096  0.017007  0.106470
2018-08-11 13:50:00  0.017007  0.049226  0.017007  0.020801  0.108929  0.441282
2018-08-11 14:20:00  0.017007  5.954420  0.410553  0.017007  0.108929  0.379346

Checking run time of each component

print(f"Start time: {start_time}")
print(f"Initialising time: {initialising_time}")
print(f"Rover time: {rover_time}")
print(f"SLA time: {sla_time}")
print(f"Plotting radar image time: {radar_image_time}")
print(f"Accumulating rainfall time: {acc_time}")
print(f"Concatenating time: {concat_time}")
print(f"Plotting rainfall map time: {rf_image_time}")
print(f"Extracting and plotting time series time: {extract_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 concatenate xarrays: {concat_time - sla_time}")
print(f"Time to plot radar image: {radar_image_time - concat_time}")
print(f"Time to accumulate rainfall: {acc_time - radar_image_time}")
print(f"Time to plot rainfall maps: {rf_image_time-acc_time}")
print(f"Time to extract and plot time series: {extract_time-rf_image_time}")

Out:

Start time: 2021-09-29 09:54:36.924741
Initialising time: 2021-09-29 09:55:20.066340
Rover time: 2021-09-29 09:55:20.169945
SLA time: 2021-09-29 09:55:38.520485
Plotting radar image time: 2021-09-29 09:55:50.197439
Accumulating rainfall time: 2021-09-29 09:55:52.201155
Concatenating time: 2021-09-29 09:55:38.538308
Plotting rainfall map time: 2021-09-29 09:55:57.230944
Extracting and plotting time series time: 2021-09-29 09:55:58.831307
Time to initialise: 0 days 00:00:43.141599
Time to run rover: 0 days 00:00:00.103605
Time to perform SLA: 0 days 00:00:18.350540
Time to concatenate xarrays: 0 days 00:00:00.017823
Time to plot radar image: 0 days 00:00:11.659131
Time to accumulate rainfall: 0 days 00:00:02.003716
Time to plot rainfall maps: 0 days 00:00:05.029789
Time to extract and plot time series: 0 days 00:00:01.600363

Total running time of the script: ( 1 minutes 20.296 seconds)

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