Note
Click here to download the full example code
Simple Lightning Nowcast (Hong Kong)
This example demonstrates how to perform a simple lightning nowcast by the extrapolation of lightning strikes, using data from Hong Kong.
Definitions
import os
from copy import copy
import textwrap
import pyproj
import numpy as np
import pandas as pd
import xarray as xr
import matplotlib.pyplot as plt
from matplotlib.colors import BoundaryNorm, ListedColormap
from matplotlib.gridspec import GridSpec
import cartopy.crs as ccrs
import cartopy.feature as cfeature
from cartopy.io import shapereader
from matplotlib.cm import ScalarMappable
from pyresample import utils
from swirlspy.qpf import rover
from swirlspy.qpf import sla
from swirlspy.qpe.utils import timestamps_ending, locate_file
from swirlspy.rad.iris import read_iris_grid
from swirlspy.core.resample import grid_resample
from swirlspy.obs import Lightning
from swirlspy.ltg.map import gen_ltg_binary
from swirlspy.utils import standardize_attr, FrameType
plt.switch_backend('agg')
THIS_DIR = os.getcwd()
Initialising
Define the target grid:
# Defining target grid
area_id = "hk1980_250km"
description = ("A 1km 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 = utils.get_area_def(
area_id, description, proj_id, projection, x_size, y_size, area_extent
)
/tmp/build/docs/swirlspy/swirlspy/examples/ltg_bin_hk.py:67: UserWarning: 'get_area_def' has moved, import it with 'from pyresample import get_area_def'
area_id, description, proj_id, projection, x_size, y_size, area_extent
/opt/conda/envs/swirlspy/lib/python3.6/site-packages/pyproj/crs/crs.py:543: UserWarning: You will likely lose important projection information when converting to a PROJ string from another format. See: https://proj.org/faq.html#what-is-the-best-format-for-describing-coordinate-reference-systems
proj_string = self.to_proj4()
Define basemap:
# Load the shape of Hong Kong
map_shape_file = os.path.abspath(os.path.join(
THIS_DIR,
'../tests/samples/shape/hk_hk1980'
))
# coastline and province
map_with_province = cfeature.ShapelyFeature(
list(shapereader.Reader(map_shape_file).geometries()),
area_def.to_cartopy_crs()
)
# define the plot function
def plot_base(ax: plt.Axes, extents: list, crs: ccrs.Projection):
# fake the ocean color
ax.imshow(
np.tile(np.array([[[178, 208, 254]]], dtype=np.uint8), [2, 2, 1]),
origin='upper', transform=crs,
extent=extents, 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('')
/opt/conda/envs/swirlspy/lib/python3.6/site-packages/pyresample/geometry.py:1617: UserWarning: You will likely lose important projection information when converting to a PROJ string from another format. See: https://proj.org/faq.html#what-is-the-best-format-for-describing-coordinate-reference-systems
proj_params = self.crs.to_proj4()
/opt/conda/envs/swirlspy/lib/python3.6/site-packages/pyproj/crs/crs.py:543: UserWarning: You will likely lose important projection information when converting to a PROJ string from another format. See: https://proj.org/faq.html#what-is-the-best-format-for-describing-coordinate-reference-systems
proj_string = self.to_proj4()
Defining a basetime, nowcast interval and nowcast duration:
basetime = pd.Timestamp('201904201200').floor('min')
basetime_str = basetime.strftime('%Y%m%d%H%M')
nowcast_interval = pd.Timedelta(30, 'm')
nowcast_duration = pd.Timedelta(2, 'h')
Loading Lightning and Radar Data
Extract Lightning Location Information System (LLIS) files to read.
# The interval between LLIS files
llis_file_interval = pd.Timedelta(1, 'm')
# Finding the string representation
# of timestamps marking the relevant LLIS files
timestamps = timestamps_ending(
basetime,
duration=nowcast_interval,
interval=llis_file_interval,
format='%Y%m%d%H%M'
)
# Locating files
llis_dir = os.path.join(THIS_DIR, '../tests/samples/llis')
located_files = []
for timestamp in timestamps:
located_file = locate_file(llis_dir, timestamp)
located_files.append(located_file)
if located_file is not None:
print(f"LLIS file for {timestamp} has been located")
LLIS file for 201904201130 has been located
LLIS file for 201904201131 has been located
LLIS file for 201904201132 has been located
LLIS file for 201904201133 has been located
LLIS file for 201904201134 has been located
LLIS file for 201904201135 has been located
LLIS file for 201904201136 has been located
LLIS file for 201904201137 has been located
LLIS file for 201904201138 has been located
LLIS file for 201904201139 has been located
LLIS file for 201904201140 has been located
LLIS file for 201904201141 has been located
LLIS file for 201904201142 has been located
LLIS file for 201904201143 has been located
LLIS file for 201904201144 has been located
LLIS file for 201904201145 has been located
LLIS file for 201904201146 has been located
LLIS file for 201904201147 has been located
LLIS file for 201904201148 has been located
LLIS file for 201904201149 has been located
LLIS file for 201904201150 has been located
LLIS file for 201904201151 has been located
LLIS file for 201904201152 has been located
LLIS file for 201904201153 has been located
LLIS file for 201904201154 has been located
LLIS file for 201904201155 has been located
LLIS file for 201904201156 has been located
LLIS file for 201904201157 has been located
LLIS file for 201904201158 has been located
LLIS file for 201904201159 has been located
Read data from files into Lightning objects.
# Initialising Lightning objects
# Coordinates are geodetic
ltg_object_geodetic = Lightning(
located_files,
'geodetic',
basetime - nowcast_interval,
basetime
)
Locating IRIS REF radar files needed to generate motion field. The IRIS REF files are marked by start time.
# The interval between the radar files
rad_interval = pd.Timedelta(6, 'm')
# Finding the string representation
# of the timestamps
# marking the required files
# In this case, the most recent radar files
# separated by `nowcast_interval` is required
rad_timestamps = timestamps_ending(
basetime,
duration=nowcast_interval + rad_interval,
interval=rad_interval
)
rad_timestamps = [rad_timestamps[0], rad_timestamps[-1]]
rad_dir = os.path.join(THIS_DIR, '../tests/samples/iris/')
located_rad_files = []
for timestamp in rad_timestamps:
located_file = locate_file(rad_dir, timestamp)
located_rad_files.append(located_file)
if located_file is not None:
print(f"IRIS REF file for {timestamp} has been located")
IRIS REF file for 1904201124 has been located
IRIS REF file for 1904201154 has been located
Read from iris radar files using read_iris_grid(). The data is in the Azimuthal Equidistant Projection.
reflec_unproj_list = []
for file in located_rad_files:
reflec = read_iris_grid(file)
reflec_unproj_list.append(reflec)
Traceback (most recent call last):
File "/tmp/build/docs/swirlspy/swirlspy/examples/ltg_bin_hk.py", line 195, in <module>
reflec = read_iris_grid(file)
File "/tmp/build/docs/swirlspy/swirlspy/rad/_iris.py", line 407, in read_iris_grid
raise ValueError("Invalid file") from e
ValueError: Invalid file
Data Reprojection
Since the coordinates of the Lightning objects are geodetic, a conversion from geodetic coordinates to that of the user-defined projection system is required. This can be achieved by swirlspy.obs.Lightning.reproject()
# pyproj representation of the map projection of the input data
inProj = pyproj.Proj(init='epsg:4326')
# pyproj representation of the intended map projection
outProj = pyproj.Proj(area_def.proj_str)
# reproject
ltg_object = ltg_object_geodetic.reproject(inProj, outProj, 'HK1980')
Radar data also needs to be reprojected from Azimuthal Equidistant to HK1980. This is achieved by the swirlspy.core.resample function. Following reprojection, the individual xarray.DataArrays can be concatenated to a single xarray.DataArrays.
reflec_list = []
for z in reflec_unproj_list:
reflec = grid_resample(
z,
z.attrs['area_def'],
area_def,
coord_label=['easting', 'northing']
)
reflec_list.append(reflec)
reflectivity = xr.concat(reflec_list, dim='time')
standardize_attr(reflectivity, frame_type=FrameType.dBZ)
print(reflectivity)
Grid lightning observations
Generate a binary grid of lighting observations, where 0 indicates no lightning and 1 indicates that there is lightning.
ltg_bin = gen_ltg_binary(ltg_object,
area_def,
coord_label=['easting', 'northing'])
print(ltg_bin)
# Duplicating ltg_density along the time dimension
# so that it can be advected.
ltg_bin1 = ltg_bin.copy()
ltg_bin1.coords['time'] = [basetime - nowcast_interval]
ltg_bin_concat = xr.concat(
[ltg_bin1, ltg_bin],
dim='time'
)
# Identifying zero value
ltg_bin_concat.attrs['zero_value'] = np.nan
Generating motion fields and extrapolating
Generate motion field using ROVER.
Extrapolate gridded lightning strikes using the motion field by Semi-Lagrangian Advection.
motion = rover(reflectivity) # rover
steps = int(nowcast_duration.total_seconds()
// nowcast_interval.total_seconds())
ltg_frames = sla(ltg_bin_concat, motion, nowcast_steps=steps-1) # sla
print(ltg_frames)
Visualization
Now, let us visualize the lightning nowcast up to a duration of 2 hours.
# Colormap and colorbar parameters
n_col = 2
cmap = ListedColormap(['#FFFFFF', '#000000'])
levels = [0., 0.5, 1.]
ticks = np.array([0, 1])
norm = BoundaryNorm(levels, cmap.N, clip=True)
mappable = ScalarMappable(cmap=cmap, norm=norm)
mappable.set_array([])
# Defining the crs
crs = area_def.to_cartopy_crs()
# Defining area
x = ltg_frames.coords['easting'].values
y = ltg_frames.coords['northing'].values
extents = [
np.min(x), np.max(x),
np.min(y), np.max(y)
]
qx = motion.coords['easting'].values[::20]
qy = motion.coords['northing'].values[::20]
qu = motion.values[0, ::20, ::20]
qv = motion.values[1, ::20, ::20]
fig = 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(ltg_frames.time.values):
time = pd.Timestamp(t)
row = i // 2
col = i % 2
ax = fig.add_subplot(gs[row, col], projection=crs)
# plot base map
plot_base(ax, extents, crs)
# plot lightning
frame = ltg_frames.sel(time=time)
frame.where(frame > levels[1]).plot(
cmap=cmap,
norm=norm,
add_colorbar=False
)
# plot motion vector
ax.quiver(
qx, qy, qu, qv, pivot='mid'
)
ax.set_title('')
ax.text(
extents[0],
extents[3],
textwrap.dedent(
"""
Lightning Density
Based @ {baseTime}
"""
).format(
baseTime=basetime.strftime('%H:%MH')
).strip(),
fontsize=10,
va='bottom',
ha='left',
linespacing=1
)
ax.text(
extents[1],
extents[3],
textwrap.dedent(
"""
{validDate}
Valid @ {validTime}
"""
).format(
validDate=basetime.strftime('%Y-%m-%d'),
validTime=time.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)
tick_locs = (np.arange(n_col) + 0.5)*(n_col-1)/n_col
cbar.set_ticks(tick_locs)
cbar.set_ticklabels(np.arange(n_col))
cbar.ax.set_ylabel(
f"{ltg_bin.attrs['long_name']}[{ltg_bin.attrs['units']}]"
)
fig.savefig(
os.path.join(
THIS_DIR,
"../tests/outputs/ltg-bin-hk.png"
),
bbox_inches='tight'
)
Total running time of the script: ( 0 minutes 0.784 seconds)