Radar Advection with NWP Blend (Vietnam)

This example shows the technique of blending Numerical Weather Forecast (NWP) with COM-SWIRLS output to generate operational deterministic QPF up to 3 and 6 hours ahead.

Definitions

# 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
# Python com-swirls package to blend nwp and nowcast (RaINS)
from swirlspy.blending import rains, nwp_bias_correction
# Python package to allow system command line functions
import os

working_dir = os.getcwd()
os.chdir(working_dir)

start_time = pd.Timestamp.now()

Initializing

This section demonstrates the extraction of radar & nwp data from netcdf into python

Step 1: Define your input data directory and output directory

# Supply the directory of radar and nwp data
data_dir = os.path.abspath(
    os.path.join(working_dir, '../tests/samples/vnmha')
)

# output directory
output_dir = os.path.abspath(
    os.path.join(working_dir, '../tests/outputs')
)

Step 2: Define a basetime

# Supply basetime
basetime = pd.Timestamp('202001240300')

Step 3: Read data files from the radar data using xarray()

# 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(19):
    t = basetime - pd.Timedelta(minutes=i * interval)
    # Radar data nomenclature
    filename = os.path.join(
        data_dir,
        'uf_vietnam',
        t.strftime("composite_uf_vn_%Y%m%d%H%M.nc")
    )
    reflec = xr.open_dataset(filename).assign_coords(time=[t])
    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 '__xarray_dataarray_variable__', therefore, we extract '__xarray_dataarray_variable__'
radar = reflec_concat['__xarray_dataarray_variable__']

# Reversing such that time goes from earliest to latest; 3 hours before basetime[0] --> basetime[17]
radar = radar.sortby('time', ascending=True)

Step 4: Reading nwp netcdf data into xarray

# NWP data listed from the basetime[0] --> 3 hours AFTER basetime[18] (nowcast)
nwp_datas = []
for i in range(1, 19):
    t = basetime + pd.Timedelta(minutes=i * interval)
    # Radar nwp nomenclature
    filename = os.path.join(
        data_dir,
        'wrf_dbz_vietnam',
        t.strftime("wrfout_d01_%Y-%m-%d_%H_%M_00.nc")
    )
    reflec = xr.open_dataset(filename).assign_coords(time=[t])
    nwp_datas.append(reflec)

# Concatenating the nwp reflectivity list of data by time
reflec_concat = xr.concat(nwp_datas, dim='time')

# Extracting the nwp data: The nwp dBZ variable is called 'zwrf', extracting 'zwrf'
nwp = reflec_concat['zwrf'].rename({'lon': 'x', 'lat': 'y'})
nwp.sortby('y', ascending=False).sortby('time', ascending=True)
# Remove "invalid" data
nwp.values[nwp.values < 0] = np.nan

initialising_time = pd.Timestamp.now()

Step 5: Bias correction of nwp data The objective of bias correction is to match the nwp percentile to the radar percentile This is also known as frequency matching.

nwp_corrected = nwp_bias_correction(
    radar, nwp, proj4_str='+proj=longlat +datum=WGS84 +no_defs'
)

bias_correction_time = pd.Timestamp.now()

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

# Adding in some attributes that is step_size <10 mins in pandas.Timedelta>, zero_value <9999.> frame_type <FrameType.dBZ>
radar.attrs['step_size'] = pd.Timedelta(minutes=10)
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()

Out:

RUNNING 'rover' FOR EXTRAPOLATION.....

Blending (RaINS)

Blending Numerical Weather Forecast (NWP) with COM-SWIRLS output to generate operational deterministic QPF up to 3 hours ahead.

blended = rains(nwp_corrected, swirls[1:])  # skip swirls base frame

rains_time = pd.Timestamp.now()

Plotting result

Step 1: Defining the dBZ levels, colorbar parameters and projection

# 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(
    working_dir,
    '../tests/samples/shape/se_asia_s'
))
# coastline and province
se_asia = cfeature.ShapelyFeature(
    list(shpreader.Reader(map_shape_file).geometries()),
    ccrs.PlateCarree()
)
# output area
extents = [
    blended.x.values[0], blended.x.values[-1],
    blended.y.values[0], blended.y.values[-1]
]

# 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('')

Step 2: Filtering values <= 5dbZ are not plotted

nwp_corrected = nwp_corrected.where(nwp_corrected > 5, np.nan)
blended = blended.where(blended > 5, np.nan)
swirls = swirls[1:]  # skip base time frame

Step 3: Plotting the swirls-radar-advection, nwp-bias-corrected, blended 3 hours ahead

plot_height = 3
plot_width = 2
n_rows = 2
n_column = 3
fig: plt.Figure = plt.figure(
    figsize=(plot_width * n_column + 1, plot_height * n_rows),
    frameon=False
)
gs = GridSpec(
    n_rows, n_column, figure=fig,
    wspace=0.05, hspace=0, top=0.95, bottom=0.05, left=0.17, right=0.845
)

for row in range(n_rows):
    time_index = (row + 1) * 6 - 1
    timelabel = basetime + pd.Timedelta(interval * (time_index + 1), 'm')

    for col in range(n_column):
        ax: plt.Axes = fig.add_subplot(
            gs[row, col],
            projection=ccrs.PlateCarree()
        )
        if col % 3 == 0:
            z = swirls[time_index].values
            y = swirls[time_index].y
            x = swirls[time_index].x
            if row == 0:
                ax.text(
                    0, 1,
                    'SWIRLS Reflectivity',
                    fontsize=8,
                    ha='left',
                    va='bottom',
                    transform=ax.transAxes
                )
        elif col % 3 == 1:
            z = nwp_corrected[time_index].values
            y = nwp_corrected[time_index].y
            x = nwp_corrected[time_index].x
            if row == 0:
                ax.text(
                    0, 1,
                    'NWP Reflectivity',
                    fontsize=8,
                    ha='left',
                    va='bottom',
                    transform=ax.transAxes
                )
        elif col % 3 == 2:
            z = blended[time_index].values
            y = blended[time_index].y
            x = blended[time_index].x
            if row == 0:
                ax.text(
                    0, 1,
                    'Blended Reflectivity',
                    fontsize=8,
                    ha='left',
                    va='bottom',
                    transform=ax.transAxes
                )

        if col == 0:
            ax.text(
                -0.25, 0,
                f"Initial @ {basetime.strftime('%H:%MZ')} \n" +
                f"Forecast Valid @ {timelabel.strftime('%H:%MZ')} ",
                fontsize=8,
                ha='left',
                va='bottom',
                transform=ax.transAxes,
                rotation=90
            )

        # plot base map
        plot_base(ax)

        # plot reflectivity
        ax.contourf(
            x, y, z, 60,
            transform=ccrs.PlateCarree(),
            cmap=cmap, norm=norm, levels=levels
        )

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,
        "swirls_nwp_blend_3hr.png"
    ),
    dpi=450,
    bbox_inches="tight",
    pad_inches=0.1
)

radar_image_time = pd.Timestamp.now()
../_images/sphx_glr_nwp-radar_qpf_vn_001.png

Checking run time of each component

print(f"Start time: {start_time}")
print(f"Initialising time: {initialising_time}")
print(f"NWP bias correction time: {bias_correction_time}")
print(f"SLA time: {sla_time}")
print(f"RaINS time: {rains_time}")
print(f"Plotting radar image time: {radar_image_time}")

print(f"Time to initialise: {initialising_time - start_time}")
print(f"Time to run NWP bias correction: {bias_correction_time - initialising_time}")
print(f"Time to run rover: {rover_time - bias_correction_time}")
print(f"Time to perform SLA: {sla_time - rover_time}")
print(f"Time to perform RaINS: {rains_time - sla_time}")
print(f"Time to plot radar image: {radar_image_time - rains_time}")

Out:

Start time: 2021-09-29 09:49:47.609648
Initialising time: 2021-09-29 09:49:48.641554
NWP bias correction time: 2021-09-29 09:49:50.117512
SLA time: 2021-09-29 09:50:06.588968
RaINS time: 2021-09-29 09:50:06.770239
Plotting radar image time: 2021-09-29 09:50:11.913032
Time to initialise: 0 days 00:00:01.031906
Time to run NWP bias correction: 0 days 00:00:01.475958
Time to run rover: 0 days 00:00:00.123519
Time to perform SLA: 0 days 00:00:16.347937
Time to perform RaINS: 0 days 00:00:00.181271
Time to plot radar image: 0 days 00:00:05.142793

Total running time of the script: ( 0 minutes 24.297 seconds)

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