SPROG (Hong Kong)

This example demonstrates how to use SPROG to forecast rainfall up to three hours, using rain guage and radar data from Hong Kong.

Setup

Import all required modules and methods:

import os
import numpy as np
import pandas as pd
import xarray as xr
import textwrap

from pyresample import utils
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import cartopy.feature as cfeature
import cartopy.io.shapereader as shpreader
from matplotlib.gridspec import GridSpec
from matplotlib.colors import BoundaryNorm, LinearSegmentedColormap
from matplotlib.colors import ListedColormap
from matplotlib.cm import ScalarMappable

from swirlspy.rad.iris import read_iris_grid
from swirlspy.qpe.utils import locate_file, timestamps_ending
from swirlspy.core.resample import grid_resample

from swirlspy.utils import FrameType, standardize_attr, FrameType, conversion
from swirlspy.qpf import sprog, dense_lucaskanade

Define working directory and nowcast parameters:

# working_dir = os.path.join(os.getcwd(), 'swirlspy/examples')
working_dir = os.getcwd()
radar_dir = os.path.abspath(
    os.path.join(working_dir, '../tests/samples/iris/ppi')
)

# Set nowcast parameters
n_timesteps = int(3 * 60 / 6)  # 3 hours, each timestamp is 6 minutes

Define the user grid:

area_id = "hk1980_250km"
description = (
    "A 250 m resolution rectangular grid centred at HKO and extending"
    "to 250 km in each direction in HK1980 easting/northing coordinates"
)
proj_id = 'hk1980'
projection = (
    '+proj=tmerc +lat_0=22.31213333333334 '
    '+lon_0=114.1785555555556 +k=1 +x_0=836694.05 '
    '+y_0=819069.8 +ellps=intl +towgs84=-162.619,-276.959,'
    '-161.764,0.067753,-2.24365,-1.15883,-1.09425 +units=m +no_defs'
)
x_size = 500
y_size = 500
area_extent = (587000, 569000, 1087000, 1069000)
area_def_tgt = utils.get_area_def(
    area_id, description, proj_id, projection, x_size, y_size, area_extent
)

Define the base map:

# Load the shape of Hong Kong
map_shape_file = os.path.abspath(os.path.join(
    working_dir,
    '../tests/samples/shape/hk'
))

# coastline and province
map_with_province = cfeature.ShapelyFeature(
    list(shpreader.Reader(map_shape_file).geometries()),
    ccrs.PlateCarree()
)


# define the plot function
def plot_base(ax: plt.Axes, extents: list, crs: ccrs.Projection):
    ax.set_extent(extents, crs=crs)
    # 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, province and state, color
    ax.add_feature(
        map_with_province, facecolor=cfeature.COLORS['land'],
        edgecolor='none', zorder=0
    )
    # overlay coastline, province and state without color
    ax.add_feature(
        map_with_province, facecolor='none', edgecolor='gray', linewidth=0.5
    )
    ax.set_title('')

Log the start time for reference:

start_time = pd.Timestamp.now()

Loading Radar Data

# Specify the basetime
basetime = pd.Timestamp('201902190800')

# Generate a list of timestamps of the radar data files
located_files = []
radar_ts = timestamps_ending(
    basetime,
    duration=pd.Timedelta(minutes=60),
    exclude_end=False
)
for timestamp in radar_ts:
    located_files.append(locate_file(radar_dir, timestamp))

# Read in the radar data
reflectivity_list = []  # stores reflec from read_iris_grid()
for filename in located_files:
    reflec = read_iris_grid(filename)
    reflectivity_list.append(reflec)

# Reproject the radar data to the user-defined grid
area_def_src = reflectivity_list[0].attrs['area_def']
reproj_reflectivity_list = []
for reflec in reflectivity_list:
    reproj_reflec = grid_resample(
        reflec, area_def_src, area_def_tgt,
        coord_label=['x', 'y']
    )
    reproj_reflectivity_list.append(reproj_reflec)

# Standardize reflectivity xarrays
raw_frames = xr.concat(reproj_reflectivity_list,
                       dim='time').sortby(['y'], ascending=False)
standardize_attr(raw_frames, frame_type=FrameType.dBZ)

# Transform from reflecitiy to rainfall rate
frames = conversion.to_rainfall_rate(raw_frames, True, a=58.53, b=1.56)

# Set the fill value
frames.attrs['zero_value'] = -15.0

# Apply threshold to -10dBR i.e. 0.1mm/h
threshold = -10.0
frames.values[frames.values < threshold] = frames.attrs['zero_value']

# Set missing values with the fill value
frames.values[~np.isfinite(frames.values)] = frames.attrs['zero_value']


# Log the time for record
initialising_time = pd.Timestamp.now()

Running Lucas Kanade Optical flow and S-PROG

# Estimate the motion field
motion = dense_lucaskanade(frames)

motion_time = pd.Timestamp.now()

# Generate forecast rainrate field
forcast_frames = sprog(
    frames,
    motion,
    n_timesteps,
    n_cascade_levels=8,
    R_thr=threshold,
    decomp_method="fft",
    bandpass_filter_method="gaussian",
    probmatching_method="mean",
)

sprog_time = pd.Timestamp.now()

Out:

Pysteps configuration file found at: /opt/conda/envs/swirlspy/lib/python3.6/site-packages/pysteps/pystepsrc

Computing the motion field with the Lucas-Kanade method.
--- 35 outliers removed ---
--- LK found 2839 sparse vectors ---
--- 165 sparse vectors left after declustering ---
--- 2.36 seconds ---
Computing S-PROG nowcast:
-------------------------

Inputs:
-------
input dimensions: 500x500

Methods:
--------
extrapolation:          semilagrangian
bandpass filter:        gaussian
decomposition:          fft
conditional statistics: no
probability matching:   mean
FFT method:             numpy

Parameters:
-----------
number of time steps:     30
parallel threads:         1
number of cascade levels: 8
order of the AR(p) model: 2
************************************************
* Correlation coefficients for cascade levels: *
************************************************
-----------------------------------------
| Level |     Lag-1     |     Lag-2     |
-----------------------------------------
| 1     | 0.998887      | 0.996312      |
-----------------------------------------
| 2     | 0.997713      | 0.994815      |
-----------------------------------------
| 3     | 0.989166      | 0.975985      |
-----------------------------------------
| 4     | 0.961085      | 0.903573      |
-----------------------------------------
| 5     | 0.830489      | 0.608149      |
-----------------------------------------
| 6     | 0.450699      | 0.157248      |
-----------------------------------------
| 7     | 0.046061      | -0.017010     |
-----------------------------------------
| 8     | -0.004227     | -0.005916     |
-----------------------------------------
****************************************
* AR(p) parameters for cascade levels: *
****************************************
------------------------------------------------------
| Level |    Phi-1     |    Phi-2     |    Phi-0     |
------------------------------------------------------
| 1     | 1.655639     | -0.657483    | 0.035539     |
------------------------------------------------------
| 2     | 1.132099     | -0.134695    | 0.066982     |
------------------------------------------------------
| 3     | 1.102335     | -0.114408    | 0.145835     |
------------------------------------------------------
| 4     | 1.214340     | -0.263510    | 0.266490     |
------------------------------------------------------
| 5     | 1.048796     | -0.262865    | 0.537445     |
------------------------------------------------------
| 6     | 0.476255     | -0.056705    | 0.891240     |
------------------------------------------------------
| 7     | 0.046085     | -0.000531    | 0.998939     |
------------------------------------------------------
| 8     | -0.004227    | -0.000004    | 0.999991     |
------------------------------------------------------
Starting nowcast computation.
Computing nowcast for time step 1... done.
Computing nowcast for time step 2... done.
Computing nowcast for time step 3... done.
Computing nowcast for time step 4... done.
Computing nowcast for time step 5... done.
Computing nowcast for time step 6... done.
Computing nowcast for time step 7... done.
Computing nowcast for time step 8... done.
Computing nowcast for time step 9... done.
Computing nowcast for time step 10... done.
Computing nowcast for time step 11... done.
Computing nowcast for time step 12... done.
Computing nowcast for time step 13... done.
Computing nowcast for time step 14... done.
Computing nowcast for time step 15... done.
Computing nowcast for time step 16... done.
Computing nowcast for time step 17... done.
Computing nowcast for time step 18... done.
Computing nowcast for time step 19... done.
Computing nowcast for time step 20... done.
Computing nowcast for time step 21... done.
Computing nowcast for time step 22... done.
Computing nowcast for time step 23... done.
Computing nowcast for time step 24... done.
Computing nowcast for time step 25... done.
Computing nowcast for time step 26... done.
Computing nowcast for time step 27... done.
Computing nowcast for time step 28... done.
Computing nowcast for time step 29... done.
Computing nowcast for time step 30... done.

Generating radar reflectivity maps

Define the color scale and format of the plot.

# 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([
    '#FFFFFF00', '#08C5F5', '#0091F3', '#3898FF', '#008243', '#00A433',
    '#00D100', '#01F508', '#77FF00', '#E0D100', '#FFDC01', '#EEB200',
    '#F08100', '#F00101', '#E20200', '#B40466', '#ED02F0'
])

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

mappable = ScalarMappable(cmap=cmap, norm=norm)
mappable.set_array([])

# Defining the crs
crs = area_def_tgt.to_cartopy_crs()

# Defining area
x = frames.coords['x'].values
y = frames.coords['y'].values
x_d = x[1] - x[0]
y_d = y[1] - y[0]
extents = [x[0], y[0], x[-1], y[-1]]

# Generating a time steps for every hour
time_steps = [
    basetime + pd.Timedelta(minutes=6*i)
    for i in range(n_timesteps + 1) if i % 10 == 0
]

ref_frames = conversion.to_reflectivity(forcast_frames, True)
ref_frames.data[ref_frames.data < 0.1] = np.nan
ref_frames = xr.concat([raw_frames[:-1, ...], ref_frames], dim='time')
ref_frames.attrs['values_name'] = 'Reflectivity 2km CAPPI'
standardize_attr(ref_frames)

qx = motion.coords['x'].values[::5]
qy = motion.coords['y'].values[::5]
qu = motion.values[0, ::5, ::5]
qv = motion.values[1, ::5, ::5]

fig: plt.Figure = plt.figure(figsize=(8, 8), frameon=False)
gs = GridSpec(
    2, 2, figure=fig, wspace=0.03, hspace=-0.25, top=0.95,
    bottom=0.05, left=0.17, right=0.845
)

for i, t in enumerate(time_steps):
    row = i // 2
    col = i % 2
    ax = fig.add_subplot(gs[row, col], projection=crs)

    # plot base map
    plot_base(ax, extents, crs)

    # plot reflectivity
    frame = ref_frames.sel(time=t)
    im = ax.imshow(frame.values, cmap=cmap, norm=norm, interpolation='nearest',
                   extent=extents)

    # plot motion vector
    ax.quiver(qx, qy, qu, qv, pivot='mid', regrid_shape=20)

    ax.text(
        extents[0],
        extents[1],
        textwrap.dedent(
            """
            Reflectivity
            Based @ {baseTime}
            """
        ).format(
            baseTime=basetime.strftime('%H:%MH')
        ).strip(),
        fontsize=10,
        va='bottom',
        ha='left',
        linespacing=1
    )
    ax.text(
        extents[2] - (extents[2] - extents[0]) * 0.03,
        extents[1],
        textwrap.dedent(
            """
            {validDate}
            Valid @ {validTime}
            """
        ).format(
            validDate=basetime.strftime('%Y-%m-%d'),
            validTime=t.strftime('%H:%MH')
        ).strip(),
        fontsize=10,
        va='bottom',
        ha='right',
        linespacing=1
    )

cbar_ax = fig.add_axes([0.875, 0.125, 0.03, 0.75])
cbar = fig.colorbar(
    mappable, cax=cbar_ax, ticks=levels[1:], extend='max', format='%.3g')
cbar.ax.set_ylabel(ref_frames.attrs['values_name'], rotation=90)

fig.savefig(
    os.path.join(
        working_dir,
        "../tests/outputs/sprog-reflectivity.png"
    ),
    bbox_inches='tight'
)

radar_image_time = pd.Timestamp.now()
../_images/sphx_glr_sprog_hk_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+30min.

# Optional, convert to rainfall depth
rf_frames = conversion.to_rainfall_depth(ref_frames, a=58.53, b=1.56)

# Compute hourly accumulated rainfall every 60 minutes.
acc_rf_frames = conversion.acc_rainfall_depth(
    rf_frames,
    basetime,
    basetime + pd.Timedelta(hours=3),
    pd.Timedelta(minutes=60)
)


# Replace zero value with NaN
acc_rf_frames.data[acc_rf_frames.data <=
                   acc_rf_frames.attrs['zero_value']] = np.nan

acc_time = pd.Timestamp.now()

Generating radar reflectivity maps

# Defining colour scale and  format.
levels = [
    0, 0.5, 2, 5, 10, 20,
    30, 40, 50, 70, 100, 150,
    200, 300, 400, 500, 600, 700
]
cmap = ListedColormap([
    '#ffffff00', '#9bf7f7', '#00ffff', '#00d5cc', '#00bd3d', '#2fd646',
    '#9de843', '#ffdd41', '#ffac33', '#ff621e', '#d23211', '#9d0063',
    '#e300ae', '#ff00ce', '#ff57da', '#ff8de6', '#ffe4fd'
])

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

mappable = ScalarMappable(cmap=cmap, norm=norm)
mappable.set_array([])

fig: plt.Figure = plt.figure(figsize=(8, 8), frameon=False)
gs = GridSpec(
    2, 2, figure=fig, wspace=0.03, hspace=-0.25, top=0.95,
    bottom=0.05, left=0.17, right=0.845
)

for i, t in enumerate(acc_rf_frames.coords['time'].values):
    row = i // 2
    col = i % 2
    ax = fig.add_subplot(gs[row, col], projection=crs)

    # plot base map
    plot_base(ax, extents, crs)

    # plot accumulated rainfall depth
    t = pd.Timestamp(t)
    frame = acc_rf_frames.sel(time=t)
    im = ax.imshow(frame.values, cmap=cmap, norm=norm, interpolation='nearest',
                   extent=extents)

    ax.text(
        extents[0],
        extents[1],
        textwrap.dedent(
            """
            Hourly Rainfall
            Based @ {baseTime}
            """
        ).format(
            baseTime=basetime.strftime('%H:%MH')
        ).strip(),
        fontsize=10,
        va='bottom',
        ha='left',
        linespacing=1
    )
    ax.text(
        extents[2] - (extents[2] - extents[0]) * 0.03,
        extents[1],
        textwrap.dedent(
            """
            {validDate}
            Valid @ {validTime}
            """
        ).format(
            validDate=basetime.strftime('%Y-%m-%d'),
            validTime=t.strftime('%H:%MH')
        ).strip(),
        fontsize=10,
        va='bottom',
        ha='right',
        linespacing=1
    )

cbar_ax = fig.add_axes([0.875, 0.125, 0.03, 0.75])
cbar = fig.colorbar(
    mappable, cax=cbar_ax, ticks=levels[1:], extend='max', format='%.3g')
cbar.ax.set_ylabel(acc_rf_frames.attrs['values_name'], rotation=90)

fig.savefig(
    os.path.join(
        working_dir,
        "../tests/outputs/sprog-rainfall.png"
    ),
    bbox_inches='tight'
)

ptime = pd.Timestamp.now()
../_images/sphx_glr_sprog_hk_002.png

Checking run time of each component

print(f"Start time: {start_time}")
print(f"Initialising time: {initialising_time}")
print(f"Motion field time: {motion_time}")
print(f"S-PROG time: {sprog_time}")
print(f"Plotting radar image time: {radar_image_time}")
print(f"Accumulating rainfall time: {acc_time}")
print(f"Plotting rainfall maps: {ptime}")

print(f"Time to initialise: {initialising_time - start_time}")
print(f"Time to run motion field: {motion_time - initialising_time}")
print(f"Time to perform S-PROG: {sprog_time - motion_time}")
print(f"Time to plot radar image: {radar_image_time - sprog_time}")
print(f"Time to accumulate rainfall: {acc_time - radar_image_time}")
print(f"Time to plot rainfall maps: {ptime - acc_time}")

print(f"Total: {ptime - start_time}")

Out:

Start time: 2021-09-29 10:04:39.845486
Initialising time: 2021-09-29 10:04:45.073966
Motion field time: 2021-09-29 10:04:47.531036
S-PROG time: 2021-09-29 10:04:52.488712
Plotting radar image time: 2021-09-29 10:04:59.757912
Accumulating rainfall time: 2021-09-29 10:05:00.673645
Plotting rainfall maps: 2021-09-29 10:05:06.008522
Time to initialise: 0 days 00:00:05.228480
Time to run motion field: 0 days 00:00:02.457070
Time to perform S-PROG: 0 days 00:00:04.957676
Time to plot radar image: 0 days 00:00:07.269200
Time to accumulate rainfall: 0 days 00:00:00.915733
Time to plot rainfall maps: 0 days 00:00:05.334877
Total: 0 days 00:00:26.163036

Total running time of the script: ( 0 minutes 25.985 seconds)

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