Hydrometeorclassification

Hydrometeorclassification#

[1]:

import wradlib as wrl
import xarray as xr
import os
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import warnings

warnings.filterwarnings("ignore")
try:
    get_ipython().run_line_magic("matplotlib inline")
except:
    plt.ion()
from scipy import interpolate
import datetime as dt
import glob

The hydrometeorclassification code is based on the paper by Zrnic et.al 2001 utilizing 2D trapezoidal membership functions based on the paper by Straka et. al 2000 adapted by Evaristo et. al 2013 for X-Band.

Precipitation Types#

[2]:

pr_types = wrl.classify.pr_types
for k, v in pr_types.items():
    print(str(k) + " - ".join(v))

0LR - Light Rain
1MR - Moderate Rain
2HR - Heavy Rain
3LD - Large Drops
4HL - Hail
5RH - Rain/Hail
6GH - Graupel/Hail
7DS - Dry Snow
8WS - Wet Snow
9HC - H Crystals
10VC - V Crystals
11NP - No Precip

Membership Functions#

Load 2D Membership Functions#

[3]:

filename = wrl.util.get_wradlib_data_file("misc/msf_xband_v1.nc")
msf = xr.open_dataset(filename)
display(msf)

Downloading file 'misc/msf_xband_v1.nc' from 'https://github.com/wradlib/wradlib-data/raw/pooch/data/misc/msf_xband_v1.nc' to '/home/runner/work/wradlib/wradlib/wradlib-data'.

<xarray.Dataset> Size: 97kB
Dimensions:  (idp: 55, hmc: 11, trapezoid: 4)
Coordinates:
  * idp      (idp) float64 440B -10.0 -8.0 -6.0 -4.0 ... 92.0 94.0 96.0 98.0
  * hmc      (hmc) <U2 88B 'LR' 'MR' 'HR' 'LD' 'HL' ... 'GH' 'DS' 'WS' 'HC' 'VC'
Dimensions without coordinates: trapezoid
Data variables:
    ZH       (hmc, idp, trapezoid) float64 19kB ...
    ZDR      (hmc, idp, trapezoid) float64 19kB ...
    RHO      (hmc, idp, trapezoid) float64 19kB ...
    KDP      (hmc, idp, trapezoid) float64 19kB ...
    TEMP     (hmc, idp, trapezoid) float64 19kB ...
Attributes:
    version:      1
    title:        2D Membershipfunctions Hydrometeorclassification for XBand
    institution:  Institute of Geosciences, Meteorology Section, University o...
    history:      created by Raquel Evaristo, adapted to netCDF by Kai Mühlbauer
    comment:      created with wradlib

Plot 2D Membership Functions#

[4]:

minmax = [(-10, 100), (-1, 6), (0.0, 1.0), (-5, 35), (-65, 45)]

for i, pr in enumerate(pr_types.values()):
    if pr[0] == "NP":
        continue
    fig = plt.figure(figsize=(10, 8))
    t = fig.suptitle(" - ".join(pr))
    t.set_y(1.02)
    hmc = msf.sel(hmc=pr[0])
    for k, p in enumerate(hmc.data_vars.values()):
        p = p.where(p != 0)
        ax = fig.add_subplot(3, 2, k + 1)
        p.sel(trapezoid=0).plot(x="idp", c="k", lw=1.0, ax=ax)
        p.sel(trapezoid=1).plot(x="idp", c="k", lw=2.0, ax=ax)
        p.sel(trapezoid=2).plot(x="idp", c="k", lw=2.0, ax=ax)
        p.sel(trapezoid=3).plot(x="idp", c="k", lw=1.0, ax=ax)
        ax.set_xlim((hmc.idp.min(), hmc.idp.max()))
        ax.margins(x=0.05, y=0.05)
        t = ax.set_title(f"{p.long_name}")
        ax.set_ylim(minmax[k])
    fig.tight_layout()
plt.show()
../../_images/notebooks_classify_2d_hmc_10_0.png
../../_images/notebooks_classify_2d_hmc_10_1.png
../../_images/notebooks_classify_2d_hmc_10_2.png
../../_images/notebooks_classify_2d_hmc_10_3.png
../../_images/notebooks_classify_2d_hmc_10_4.png
../../_images/notebooks_classify_2d_hmc_10_5.png
../../_images/notebooks_classify_2d_hmc_10_6.png
../../_images/notebooks_classify_2d_hmc_10_7.png
../../_images/notebooks_classify_2d_hmc_10_8.png
../../_images/notebooks_classify_2d_hmc_10_9.png
../../_images/notebooks_classify_2d_hmc_10_10.png

Use Sounding Data#

Retrieve Sounding Data#

To get the temperature as additional discriminator we use radiosonde data from the University of Wyoming.

The function get_radiosonde tries to find the next next available radiosonde measurement on the given date.

[5]:

rs_time = dt.datetime(2014, 6, 10, 12, 0)
import urllib

try:
    rs_data, rs_meta = wrl.io.get_radiosonde(10410, rs_time)
except (urllib.error.HTTPError, urllib.error.URLError):
    dataf = wrl.util.get_wradlib_data_file("misc/radiosonde_10410_20140610_1200.h5")
    rs_data, _ = wrl.io.from_hdf5(dataf)
    metaf = wrl.util.get_wradlib_data_file("misc/radiosonde_10410_20140610_1200.json")
    with open(metaf, "r") as infile:
        import json

        rs_meta = json.load(infile)
rs_meta

[5]:

{'Station identifier': 'EDZE',
 'Station number': 10410,
 'Observation time': datetime.datetime(2014, 6, 10, 12, 0),
 'Station latitude': 51.4,
 'Station longitude': 6.97,
 'Station elevation': 147.0,
 'Showalter index': 1.65,
 'Lifted index': -5.85,
 'LIFT computed using virtual temperature': -6.24,
 'SWEAT index': 89.02,
 'K index': 23.7,
 'Cross totals index': 17.3,
 'Vertical totals index': 31.3,
 'Totals totals index': 48.6,
 'Convective Available Potential Energy': 1542.9,
 'CAPE using virtual temperature': 1644.44,
 'Convective Inhibition': -139.39,
 'CINS using virtual temperature': -79.9,
 'Equilibrum Level': 202.46,
 'Equilibrum Level using virtual temperature': 202.39,
 'Level of Free Convection': 736.36,
 'LFCT using virtual temperature': 773.09,
 'Bulk Richardson Number': 57.4,
 'Bulk Richardson Number using CAPV': 61.18,
 'Temp [K] of the Lifted Condensation Level': 288.22,
 'Pres [hPa] of the Lifted Condensation Level': 882.28,
 'Equivalent potential temp [K] of the LCL': 334.9,
 'Mean mixed layer potential temperature': 298.74,
 'Mean mixed layer mixing ratio': 12.42,
 '1000 hPa to 500 hPa thickness': 5663.0,
 'Precipitable water [mm] for entire sounding': 28.11,
 'quantity': {'PRES': 'hPa',
  'HGHT': 'm',
  'TEMP': 'C',
  'DWPT': 'C',
  'FRPT': 'C',
  'RELH': '%',
  'RELI': '%',
  'MIXR': 'g/kg',
  'DRCT': 'deg',
  'SKNT': 'knot',
  'THTA': 'K',
  'THTE': 'K',
  'THTV': 'K'}}

Extract Temperature and Height#

[6]:

stemp = rs_data["TEMP"]
sheight = rs_data["HGHT"]
# remove nans
idx = np.isfinite(stemp)
stemp = stemp[idx]
sheight = sheight[idx]

Create DataArray#

[7]:

stemp_da = xr.DataArray(
    data=stemp,
    dims=["height"],
    coords=dict(
        height=(["height"], sheight),
    ),
    attrs=dict(
        description="Temperature.",
        units="degC",
    ),
)
display(stemp_da)

<xarray.DataArray (height: 97)> Size: 776B
array([ 25.6,  19.8,  21.6,  21.6,  21.6,  19.7,  16.7,  16.4,  15.6,
        13.4,   5. ,   3.2,   1.8,  -5.3,  -6.7,  -9.3, -12.5, -14.9,
       -18.5, -18.9, -23.5, -25.5, -31.9, -32.7, -37. , -40.7, -41.9,
       -46.5, -50.5, -51.1, -60.7, -61.5, -65.7, -66.1, -64.1, -60.5,
       -59.1, -59.1, -59.4, -59.9, -60.4, -60.7, -58.5, -57.4, -55.5,
       -54.3, -54.9, -55.1, -55.3, -55.5, -55.9, -56.1, -55.4, -54.4,
       -53.4, -52.5, -52.7, -53.2, -55.6, -55.9, -55.5, -55.1, -54.9,
       -54.8, -54.6, -54.5, -54.3, -54.2, -54.2, -54.1, -53.9, -53.8,
       -52.5, -52.2, -51.8, -51.5, -50.7, -49.5, -48.9, -48.5, -48.6,
       -49.5, -49.7, -49.9, -49.1, -48.5, -47.6, -46.7, -45.8, -44.8,
       -43.9, -39.5, -37.3, -35.9, -36.1, -36.7, -35.5])
Coordinates:
  * height   (height) float64 776B 147.0 744.0 828.0 ... 3.155e+04 3.228e+04
Attributes:
    description:  Temperature.
    units:        degC

Interpolate to higher resolution#

[8]:

hmax = 30000.0
ht = np.arange(0.0, hmax)
itemp_da = stemp_da.interp({"height": ht})
display(itemp_da)

<xarray.DataArray (height: 30000)> Size: 240kB
array([         nan,          nan,          nan, ..., -38.47842779,
       -38.47440585, -38.47038391])
Coordinates:
  * height   (height) float64 240kB 0.0 1.0 2.0 3.0 ... 3e+04 3e+04 3e+04 3e+04
Attributes:
    description:  Temperature.
    units:        degC

Fix Temperature below first measurement#

[9]:

itemp_da = itemp_da.bfill(dim="height")

Plot Temperature Profile#

[10]:

fig = plt.figure(figsize=(5, 10))
ax = fig.add_subplot(111)
itemp_da.plot(y="height", ax=ax, marker="o", zorder=0, c="r")
stemp_da.to_dataset(name="stemp").plot.scatter(
    x="stemp", y="height", ax=ax, marker="o", c="b", zorder=1
)
ax.grid(True)
../../_images/notebooks_classify_2d_hmc_23_0.png

Prepare Radar Data#

Load Radar Data#

[11]:

# read the radar volume scan
filename = "hdf5/2014-06-09--185000.rhi.mvol"
filename = wrl.util.get_wradlib_data_file(filename)

Downloading file 'hdf5/2014-06-09--185000.rhi.mvol' from 'https://github.com/wradlib/wradlib-data/raw/pooch/data/hdf5/2014-06-09--185000.rhi.mvol' to '/home/runner/work/wradlib/wradlib/wradlib-data'.

Extract data for georeferencing#

[12]:

import xradar as xd

swp = xr.open_dataset(
    filename, engine=xd.io.backends.GamicBackendEntrypoint, group="sweep_0", chunks={}
)
swp = xd.util.remove_duplicate_rays(swp)
swp = xd.util.reindex_angle(
    swp, start_angle=0, stop_angle=90, angle_res=0.2, direction=1
)
swp

[12]:

<xarray.Dataset> Size: 14MB
Dimensions:            (range: 667, elevation: 450)
Coordinates:
  * range              (range) float32 3kB 37.5 112.5 ... 4.991e+04 4.999e+04
  * elevation          (elevation) float64 4kB 0.1 0.3 0.5 ... 89.5 89.7 89.9
    azimuth            (elevation) float64 4kB dask.array<chunksize=(450,), meta=np.ndarray>
    time               (elevation) datetime64[ns] 4kB dask.array<chunksize=(450,), meta=np.ndarray>
    longitude          float64 8B ...
    latitude           float64 8B ...
    altitude           float64 8B ...
Data variables: (12/17)
    KDP                (elevation, range) float32 1MB dask.array<chunksize=(450, 667), meta=np.ndarray>
    PHIDP              (elevation, range) float32 1MB dask.array<chunksize=(450, 667), meta=np.ndarray>
    DBZH               (elevation, range) float32 1MB dask.array<chunksize=(450, 667), meta=np.ndarray>
    DBZV               (elevation, range) float32 1MB dask.array<chunksize=(450, 667), meta=np.ndarray>
    RHOHV              (elevation, range) float32 1MB dask.array<chunksize=(450, 667), meta=np.ndarray>
    DBTH               (elevation, range) float32 1MB dask.array<chunksize=(450, 667), meta=np.ndarray>
    ...                 ...
    ZDR                (elevation, range) float32 1MB dask.array<chunksize=(450, 667), meta=np.ndarray>
    sweep_mode         <U3 12B ...
    sweep_number       int64 8B ...
    prt_mode           <U7 28B ...
    follow_mode        <U7 28B ...
    sweep_fixed_angle  float64 8B ...
Attributes:
    source:       gamic
    pulse_width:  0
[13]:

swp.azimuth.load(), swp.time.load()

[13]:

(<xarray.DataArray 'azimuth' (elevation: 450)> Size: 4kB
 array([225.        , 225.        , 225.        , 225.00549316,
        405.00549316, 225.00549316, 405.00549316, 225.        ,
        225.00549316, 225.01098633, 405.00549316, 225.00549316,
        405.00549316, 225.00549316, 225.01098633, 225.01098633,
        405.00549316, 225.00549316, 405.00549316, 225.        ,
        225.00549316, 225.01098633, 405.00549316, 225.00549316,
        225.01098633, 405.00549316, 225.        , 225.01098633,
        225.        , 225.        , 225.        , 225.        ,
        225.        , 225.        , 225.00549316, 225.01098633,
        405.00549316, 225.00549316, 225.01098633, 405.00549316,
        225.00549316, 405.00549316, 225.00549316, 225.00549316,
        405.00549316, 225.00549316, 405.00549316, 225.        ,
        225.00549316, 405.00549316, 225.        , 225.        ,
        225.00549316, 405.00549316, 225.00549316, 405.00549316,
        225.        , 225.        , 225.        , 225.        ,
        225.00549316, 225.        , 225.        , 225.        ,
        225.        , 225.00549316, 405.00549316, 225.00549316,
        225.01098633, 405.00549316, 225.        , 225.        ,
        225.        , 225.        , 225.        , 225.        ,
        225.        , 225.        , 225.        , 225.        ,
 ...
        224.98901367, 224.98901367, 224.98901367, 224.98901367,
        224.98901367, 224.98901367, 224.98901367, 224.98901367,
        224.98901367, 224.98901367, 224.98901367, 224.98901367,
        224.98901367, 224.98901367, 224.98901367, 224.98901367,
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        224.98901367, 224.98901367, 224.98901367, 224.98901367,
        224.98901367, 224.98901367, 224.98901367, 224.98901367,
        224.98901367, 224.98901367, 224.98901367, 224.98901367,
        224.98901367, 224.98901367, 224.98901367, 224.98901367,
        224.98901367, 224.98901367, 224.98901367, 224.98901367,
        224.98901367, 224.98901367, 224.98901367, 224.98901367,
        224.98901367, 224.98901367, 224.98901367, 224.98901367,
        224.98901367, 224.98901367, 224.98901367, 224.98901367,
        224.98901367, 224.98901367, 224.98901367, 224.98901367,
        224.98901367, 224.98901367, 224.98901367, 224.98901367,
        224.98901367, 224.98901367, 224.98901367, 224.98901367,
        224.98901367, 224.98901367, 224.98901367, 224.98901367,
        224.98901367, 224.98901367, 224.98901367, 224.98901367,
        224.98901367, 224.98901367, 224.98901367, 224.98901367,
        224.98901367, 224.98901367])
 Coordinates:
   * elevation  (elevation) float64 4kB 0.1 0.3 0.5 0.7 ... 89.3 89.5 89.7 89.9
     azimuth    (elevation) float64 4kB 225.0 225.0 225.0 ... 225.0 225.0 225.0
     time       (elevation) datetime64[ns] 4kB 2014-06-09T18:50:01 ... 2014-06...
     longitude  float64 8B 7.072
     latitude   float64 8B 50.73
     altitude   float64 8B 99.5
 Attributes:
     standard_name:  ray_azimuth_angle
     long_name:      azimuth_angle_from_true_north
     units:          degrees
     axis:           radial_azimuth_coordinate,
 <xarray.DataArray 'time' (elevation: 450)> Size: 4kB
 array(['2014-06-09T18:50:01.000000000', '2014-06-09T18:50:04.000000000',
        '2014-06-09T18:50:04.000000000', '2014-06-09T18:50:04.000000000',
        '2014-06-09T18:50:04.000000000', '2014-06-09T18:50:04.000000000',
        '2014-06-09T18:50:04.000000000', '2014-06-09T18:50:04.000000000',
        '2014-06-09T18:50:04.000000000', '2014-06-09T18:50:04.000000000',
        '2014-06-09T18:50:04.000000000', '2014-06-09T18:50:04.000000000',
        '2014-06-09T18:50:04.000000000', '2014-06-09T18:50:04.000000000',
        '2014-06-09T18:50:04.000000000', '2014-06-09T18:50:04.000000000',
        '2014-06-09T18:50:04.000000000', '2014-06-09T18:50:04.000000000',
        '2014-06-09T18:50:05.000000000', '2014-06-09T18:50:05.000000000',
        '2014-06-09T18:50:05.000000000', '2014-06-09T18:50:05.000000000',
        '2014-06-09T18:50:05.000000000', '2014-06-09T18:50:05.000000000',
        '2014-06-09T18:50:05.000000000', '2014-06-09T18:50:05.000000000',
        '2014-06-09T18:50:05.000000000', '2014-06-09T18:50:05.000000000',
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        '2014-06-09T18:50:05.000000000', '2014-06-09T18:50:05.000000000',
        '2014-06-09T18:50:05.000000000', '2014-06-09T18:50:05.000000000',
        '2014-06-09T18:50:05.000000000', '2014-06-09T18:50:05.000000000',
        '2014-06-09T18:50:05.000000000', '2014-06-09T18:50:05.000000000',
        '2014-06-09T18:50:06.000000000', '2014-06-09T18:50:06.000000000',
 ...
        '2014-06-09T18:50:24.000000000', '2014-06-09T18:50:24.000000000',
        '2014-06-09T18:50:24.000000000', '2014-06-09T18:50:24.000000000',
        '2014-06-09T18:50:25.000000000', '2014-06-09T18:50:25.000000000',
        '2014-06-09T18:50:25.000000000', '2014-06-09T18:50:25.000000000',
        '2014-06-09T18:50:25.000000000', '2014-06-09T18:50:25.000000000',
        '2014-06-09T18:50:25.000000000', '2014-06-09T18:50:25.000000000',
        '2014-06-09T18:50:25.000000000', '2014-06-09T18:50:25.000000000',
        '2014-06-09T18:50:25.000000000', '2014-06-09T18:50:25.000000000',
        '2014-06-09T18:50:25.000000000', '2014-06-09T18:50:25.000000000',
        '2014-06-09T18:50:25.000000000', '2014-06-09T18:50:25.000000000',
        '2014-06-09T18:50:25.000000000', '2014-06-09T18:50:25.000000000',
        '2014-06-09T18:50:25.000000000', '2014-06-09T18:50:25.000000000',
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        '2014-06-09T18:50:26.000000000', '2014-06-09T18:50:26.000000000',
        '2014-06-09T18:50:26.000000000', '2014-06-09T18:50:26.000000000',
        '2014-06-09T18:50:26.000000000', '2014-06-09T18:50:26.000000000',
        '2014-06-09T18:50:26.000000000', '2014-06-09T18:50:26.000000000',
        '2014-06-09T18:50:26.000000000', '2014-06-09T18:50:26.000000000',
        '2014-06-09T18:50:26.000000000', '2014-06-09T18:50:27.000000000'],
       dtype='datetime64[ns]')
 Coordinates:
   * elevation  (elevation) float64 4kB 0.1 0.3 0.5 0.7 ... 89.3 89.5 89.7 89.9
     azimuth    (elevation) float64 4kB 225.0 225.0 225.0 ... 225.0 225.0 225.0
     time       (elevation) datetime64[ns] 4kB 2014-06-09T18:50:01 ... 2014-06...
     longitude  float64 8B 7.072
     latitude   float64 8B 50.73
     altitude   float64 8B 99.5
 Attributes:
     standard_name:  time)

[14]:

swp.azimuth.values

[14]:

array([225.        , 225.        , 225.        , 225.00549316,
       405.00549316, 225.00549316, 405.00549316, 225.        ,
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       405.00549316, 225.00549316, 405.00549316, 225.        ,
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       405.00549316, 225.        , 225.00549316, 225.01098633,
       405.00549316, 405.00549316, 225.        , 225.00549316,
       405.00549316, 225.        , 225.        , 225.        ,
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       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.99450684, 404.99450684, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367])

Get Heights of Radar Bins#

[15]:

swp = swp.wrl.georef.georeference()
swp.azimuth.values

[15]:

array([225.        , 225.        , 225.        , 225.00549316,
       405.00549316, 225.00549316, 405.00549316, 225.        ,
       225.00549316, 225.01098633, 405.00549316, 225.00549316,
       405.00549316, 225.00549316, 225.01098633, 225.01098633,
       405.00549316, 225.00549316, 405.00549316, 225.        ,
       225.00549316, 225.01098633, 405.00549316, 225.00549316,
       225.01098633, 405.00549316, 225.        , 225.01098633,
       225.        , 225.        , 225.        , 225.        ,
       225.        , 225.        , 225.00549316, 225.01098633,
       405.00549316, 225.00549316, 225.01098633, 405.00549316,
       225.00549316, 405.00549316, 225.00549316, 225.00549316,
       405.00549316, 225.00549316, 405.00549316, 225.        ,
       225.00549316, 405.00549316, 225.        , 225.        ,
       225.00549316, 405.00549316, 225.00549316, 405.00549316,
       225.        , 225.        , 225.        , 225.        ,
       225.00549316, 225.        , 225.        , 225.        ,
       225.        , 225.00549316, 405.00549316, 225.00549316,
       225.01098633, 405.00549316, 225.        , 225.        ,
       225.        , 225.        , 225.        , 225.        ,
       225.        , 225.        , 225.        , 225.        ,
       225.        , 225.00549316, 405.00549316, 225.        ,
       225.        , 225.00549316, 225.01098633, 405.00549316,
       225.00549316, 225.01098633, 405.00549316, 225.        ,
       225.        , 225.        , 225.00549316, 405.00549316,
       225.00549316, 405.00549316, 225.        , 225.        ,
       225.        , 225.        , 225.        , 225.        ,
       225.        , 225.        , 225.        , 225.        ,
       225.        , 225.        , 225.00549316, 225.01098633,
       405.00549316, 225.        , 225.00549316, 225.01098633,
       405.00549316, 405.00549316, 225.        , 225.00549316,
       405.00549316, 225.        , 225.        , 225.        ,
       225.        , 225.        , 225.        , 225.        ,
       404.99450684, 404.98352051, 224.98352051, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.99450684, 404.99450684, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367, 224.98901367, 224.98901367,
       224.98901367, 224.98901367])

Plot RHI of Heights#

[16]:

fig = plt.figure(figsize=(8, 7))
ax = fig.add_subplot(111)
cmap = mpl.cm.viridis
swp.z.plot(x="gr", y="z", ax=ax, cbar_kwargs=dict(label="Height [m]"))
ax.set_xlabel("Range [m]")
ax.set_ylabel("Height [m]")
ax.grid(True)
plt.show()
../../_images/notebooks_classify_2d_hmc_34_0.png

Get Index into High Res Height Array#

[17]:

def merge_radar_profile(rds, cds):
    cds = cds.interp({"height": rds.z}, method="linear")
    rds = rds.assign({"TEMP": cds})
    return rds

[18]:

hmc_ds = swp.pipe(merge_radar_profile, itemp_da)
display(hmc_ds)

<xarray.Dataset> Size: 32MB
Dimensions:            (range: 667, elevation: 450)
Coordinates: (12/15)
  * range              (range) float32 3kB 37.5 112.5 ... 4.991e+04 4.999e+04
  * elevation          (elevation) float64 4kB 0.1 0.3 0.5 ... 89.5 89.7 89.9
    azimuth            (elevation) float64 4kB 225.0 225.0 225.0 ... 225.0 225.0
    time               (elevation) datetime64[ns] 4kB 2014-06-09T18:50:01 ......
    longitude          float64 8B 7.072
    latitude           float64 8B 50.73
    ...                 ...
    z                  (elevation, range) float64 2MB 99.57 99.7 ... 5.009e+04
    gr                 (elevation, range) float64 2MB 18.39 93.39 ... 67.62
    rays               (elevation, range) float64 2MB 0.1 0.1 0.1 ... 89.9 89.9
    bins               (elevation, range) float32 1MB 37.5 112.5 ... 4.999e+04
    crs_wkt            int64 8B 0
    height             (elevation, range) float64 2MB 99.57 99.7 ... 5.009e+04
Data variables: (12/18)
    KDP                (elevation, range) float32 1MB dask.array<chunksize=(450, 667), meta=np.ndarray>
    PHIDP              (elevation, range) float32 1MB dask.array<chunksize=(450, 667), meta=np.ndarray>
    DBZH               (elevation, range) float32 1MB dask.array<chunksize=(450, 667), meta=np.ndarray>
    DBZV               (elevation, range) float32 1MB dask.array<chunksize=(450, 667), meta=np.ndarray>
    RHOHV              (elevation, range) float32 1MB dask.array<chunksize=(450, 667), meta=np.ndarray>
    DBTH               (elevation, range) float32 1MB dask.array<chunksize=(450, 667), meta=np.ndarray>
    ...                 ...
    sweep_mode         <U3 12B ...
    sweep_number       int64 8B ...
    prt_mode           <U7 28B ...
    follow_mode        <U7 28B ...
    sweep_fixed_angle  float64 8B ...
    TEMP               (elevation, range) float64 2MB 25.6 25.6 25.6 ... nan nan
Attributes:
    source:       gamic
    pulse_width:  0
[19]:

fig = plt.figure(figsize=(10, 5))
ax = fig.add_subplot(111)
hmc_ds.TEMP.plot(
    x="gr",
    y="z",
    cmap=cmap,
    ax=ax,
    add_colorbar=True,
    cbar_kwargs=dict(label="Temperature [°C]"),
)
ax.set_xlabel("Range [m]")
ax.set_ylabel("Range [m]")
ax.set_aspect("equal")
ax.set_ylim(0, 30000)
plt.show()
../../_images/notebooks_classify_2d_hmc_38_0.png

HMC Workflow#

Setup Independent Observable \(Z_H\)#

Retrieve membership function values based on independent observable

[20]:

%%time
msf_val = msf.wrl.classify.msf_index_indep(swp.DBZH)
display(msf_val)

<xarray.DataArray 'msf_index_indep' (hmc: 11, obs: 5, elevation: 450,
                                     range: 667, trapezoid: 4)> Size: 264MB
dask.array<transpose, shape=(11, 5, 450, 667, 4), dtype=int32, chunksize=(11, 5, 450, 667, 4), chunktype=numpy.ndarray>
Coordinates: (12/16)
  * hmc        (hmc) <U2 88B 'LR' 'MR' 'HR' 'LD' 'HL' ... 'DS' 'WS' 'HC' 'VC'
  * obs        (obs) object 40B 'ZH' 'ZDR' 'RHO' 'KDP' 'TEMP'
  * range      (range) float32 3kB 37.5 112.5 187.5 ... 4.991e+04 4.999e+04
  * elevation  (elevation) float64 4kB 0.1 0.3 0.5 0.7 ... 89.3 89.5 89.7 89.9
    azimuth    (elevation) float64 4kB 225.0 225.0 225.0 ... 225.0 225.0 225.0
    time       (elevation) datetime64[ns] 4kB 2014-06-09T18:50:01 ... 2014-06...
    ...         ...
    y          (elevation, range) float64 2MB -26.52 -79.55 ... -61.25 -61.34
    z          (elevation, range) float64 2MB 99.57 99.7 ... 5.001e+04 5.009e+04
    gr         (elevation, range) float64 2MB 18.39 93.39 168.4 ... 67.49 67.62
    rays       (elevation, range) float64 2MB 0.1 0.1 0.1 0.1 ... 89.9 89.9 89.9
    bins       (elevation, range) float32 1MB 37.5 112.5 ... 4.991e+04 4.999e+04
    crs_wkt    int64 8B 0
Dimensions without coordinates: trapezoid
CPU times: user 52.2 ms, sys: 6 μs, total: 52.2 ms
Wall time: 51.8 ms

Fuzzyfication#

[21]:

%%time
fu = msf_val.wrl.classify.fuzzyfi(
    hmc_ds, dict(ZH="DBZH", ZDR="ZDR", RHO="RHOHV", KDP="KDP", TEMP="TEMP")
)

CPU times: user 14.7 ms, sys: 0 ns, total: 14.7 ms
Wall time: 14.7 ms

Probability#

[22]:

# weights dataset
w = xr.Dataset(dict(ZH=2.0, ZDR=1.0, RHO=1.0, KDP=1.0, TEMP=1.0))
display(w)

<xarray.Dataset> Size: 40B
Dimensions:  ()
Data variables:
    ZH       float64 8B 2.0
    ZDR      float64 8B 1.0
    RHO      float64 8B 1.0
    KDP      float64 8B 1.0
    TEMP     float64 8B 1.0
[23]:

%%time
prob = fu.wrl.classify.probability(w).compute()
display(prob)

<xarray.DataArray (hmc: 11, elevation: 450, range: 667)> Size: 26MB
array([[[0.16666667, 0.5       , 0.5       , ..., 0.        ,
         0.        , 0.        ],
        [0.        , 0.5       , 0.66666667, ..., 0.        ,
         0.        , 0.        ],
        [0.        , 0.5       , 0.5       , ..., 0.        ,
         0.        , 0.        ],
        ...,
        [0.16666667, 0.66666667, 0.5       , ..., 0.        ,
         0.        , 0.        ],
        [0.16666667, 0.66666667, 0.5       , ..., 0.        ,
         0.        , 0.        ],
        [0.16666667, 0.66666667, 0.5       , ..., 0.        ,
         0.        , 0.        ]],

       [[0.        , 0.        , 0.        , ..., 0.        ,
         0.        , 0.        ],
        [0.        , 0.        , 0.        , ..., 0.        ,
         0.        , 0.        ],
        [0.        , 0.        , 0.        , ..., 0.        ,
         0.        , 0.        ],
...
        [0.29812113, 0.33333333, 0.33333333, ..., 0.        ,
         0.        , 0.        ],
        [0.31296539, 0.33333333, 0.33333333, ..., 0.        ,
         0.        , 0.        ],
        [0.32917684, 0.33333333, 0.33333333, ..., 0.        ,
         0.        , 0.        ]],

       [[0.29480082, 0.33333333, 0.33333333, ..., 0.        ,
         0.        , 0.        ],
        [0.25280711, 0.33333333, 0.33333333, ..., 0.        ,
         0.        , 0.        ],
        [0.25808055, 0.33333333, 0.33333333, ..., 0.        ,
         0.        , 0.        ],
        ...,
        [0.29812113, 0.33333333, 0.33333333, ..., 0.        ,
         0.        , 0.        ],
        [0.31296539, 0.33333333, 0.33333333, ..., 0.        ,
         0.        , 0.        ],
        [0.32917684, 0.33333333, 0.33333333, ..., 0.        ,
         0.        , 0.        ]]])
Coordinates: (12/16)
  * hmc        (hmc) <U2 88B 'LR' 'MR' 'HR' 'LD' 'HL' ... 'DS' 'WS' 'HC' 'VC'
  * range      (range) float32 3kB 37.5 112.5 187.5 ... 4.991e+04 4.999e+04
  * elevation  (elevation) float64 4kB 0.1 0.3 0.5 0.7 ... 89.3 89.5 89.7 89.9
    azimuth    (elevation) float64 4kB 225.0 225.0 225.0 ... 225.0 225.0 225.0
    time       (elevation) datetime64[ns] 4kB 2014-06-09T18:50:01 ... 2014-06...
    longitude  float64 8B 7.072
    ...         ...
    z          (elevation, range) float64 2MB 99.57 99.7 ... 5.001e+04 5.009e+04
    gr         (elevation, range) float64 2MB 18.39 93.39 168.4 ... 67.49 67.62
    rays       (elevation, range) float64 2MB 0.1 0.1 0.1 0.1 ... 89.9 89.9 89.9
    bins       (elevation, range) float32 1MB 37.5 112.5 ... 4.991e+04 4.999e+04
    crs_wkt    int64 8B 0
    height     (elevation, range) float64 2MB 99.57 99.7 ... 5.001e+04 5.009e+04
CPU times: user 619 ms, sys: 261 ms, total: 880 ms
Wall time: 788 ms

[24]:

# prob = prob.compute()

Classification#

[25]:

cl_res = prob.wrl.classify.classify(threshold=0.0)
display(cl_res)

<xarray.DataArray (hmc: 12, elevation: 450, range: 667)> Size: 29MB
array([[[0.16666667, 0.5       , 0.5       , ..., 0.        ,
         0.        , 0.        ],
        [0.        , 0.5       , 0.66666667, ..., 0.        ,
         0.        , 0.        ],
        [0.        , 0.5       , 0.5       , ..., 0.        ,
         0.        , 0.        ],
        ...,
        [0.16666667, 0.66666667, 0.5       , ..., 0.        ,
         0.        , 0.        ],
        [0.16666667, 0.66666667, 0.5       , ..., 0.        ,
         0.        , 0.        ],
        [0.16666667, 0.66666667, 0.5       , ..., 0.        ,
         0.        , 0.        ]],

       [[0.        , 0.        , 0.        , ..., 0.        ,
         0.        , 0.        ],
        [0.        , 0.        , 0.        , ..., 0.        ,
         0.        , 0.        ],
        [0.        , 0.        , 0.        , ..., 0.        ,
         0.        , 0.        ],
...
        [0.29812113, 0.33333333, 0.33333333, ..., 0.        ,
         0.        , 0.        ],
        [0.31296539, 0.33333333, 0.33333333, ..., 0.        ,
         0.        , 0.        ],
        [0.32917684, 0.33333333, 0.33333333, ..., 0.        ,
         0.        , 0.        ]],

       [[0.        , 0.        , 0.        , ..., 1.        ,
         1.        , 1.        ],
        [0.        , 0.        , 0.        , ..., 1.        ,
         1.        , 1.        ],
        [0.        , 0.        , 0.        , ..., 1.        ,
         1.        , 1.        ],
        ...,
        [0.        , 0.        , 0.        , ..., 1.        ,
         1.        , 1.        ],
        [0.        , 0.        , 0.        , ..., 1.        ,
         1.        , 1.        ],
        [0.        , 0.        , 0.        , ..., 1.        ,
         1.        , 1.        ]]])
Coordinates: (12/16)
  * hmc        (hmc) <U2 96B 'LR' 'MR' 'HR' 'LD' 'HL' ... 'WS' 'HC' 'VC' 'NP'
  * range      (range) float32 3kB 37.5 112.5 187.5 ... 4.991e+04 4.999e+04
  * elevation  (elevation) float64 4kB 0.1 0.3 0.5 0.7 ... 89.3 89.5 89.7 89.9
    azimuth    (elevation) float64 4kB 225.0 225.0 225.0 ... 225.0 225.0 225.0
    time       (elevation) datetime64[ns] 4kB 2014-06-09T18:50:01 ... 2014-06...
    longitude  float64 8B 7.072
    ...         ...
    z          (elevation, range) float64 2MB 99.57 99.7 ... 5.001e+04 5.009e+04
    gr         (elevation, range) float64 2MB 18.39 93.39 168.4 ... 67.49 67.62
    rays       (elevation, range) float64 2MB 0.1 0.1 0.1 0.1 ... 89.9 89.9 89.9
    bins       (elevation, range) float32 1MB 37.5 112.5 ... 4.991e+04 4.999e+04
    crs_wkt    int64 8B 0
    height     (elevation, range) float64 2MB 99.57 99.7 ... 5.001e+04 5.009e+04

Compute#

[26]:

%%time
cl_res = cl_res.compute()
cl_res = cl_res.assign_coords(sweep_mode="rhi")

CPU times: user 2.47 ms, sys: 0 ns, total: 2.47 ms
Wall time: 2.48 ms

HMC Results#

Plot Probability of HMC Types#

[27]:

prob = prob.assign_coords(hmc=np.array(list(pr_types.values())).T[1][:11])
prob = prob.where(prob > 0)
prob.plot(x="x", y="y", col="hmc", col_wrap=4, cbar_kwargs=dict(label="Probability"))

[27]:

<xarray.plot.facetgrid.FacetGrid at 0x7f82819f6ff0>
../../_images/notebooks_classify_2d_hmc_54_1.png

Plot maximum probability#

[28]:

fig = plt.figure(figsize=(10, 6))
cmap = "cubehelix"
im = cl_res.max("hmc").wrl.vis.plot(
    ax=111,
    crs={"angular_spacing": 20.0, "radial_spacing": 12.0, "latmin": 2.5},
    cmap=cmap,
    fig=fig,
)
cgax = plt.gca()
cbar = plt.colorbar(im, ax=cgax, fraction=0.046, pad=0.05)
cbar.set_label("Probability")
cgax.set_xlim(0, 40000)
cgax.set_ylim(0, 14000)
t = cgax.set_title("Hydrometeorclassification", y=1.05)

caax = cgax.parasites[0]
caax.set_xlabel("Range [m]")
caax.set_ylabel("Range [m]")
plt.show()
../../_images/notebooks_classify_2d_hmc_56_0.png

Plot classification result#

[29]:

bounds = np.arange(-0.5, prob.shape[0] + 0.6, 1)
ticks = np.arange(0, prob.shape[0] + 1)
cmap = mpl.cm.get_cmap("cubehelix", len(ticks))
norm = mpl.colors.BoundaryNorm(bounds, cmap.N)

[30]:

hydro = cl_res.argmax("hmc")
hydro.attrs = dict(long_name="Hydrometeorclassification")
hydro = hydro.assign_coords(sweep_mode="rhi")

[31]:

fig = plt.figure(figsize=(10, 8))
im = hydro.wrl.vis.plot(
    ax=111,
    crs={"angular_spacing": 20.0, "radial_spacing": 12.0, "latmin": 2.5},
    norm=norm,
    cmap=cmap,
    fig=fig,
)
cgax = plt.gca()
caax = cgax.parasites[0]
paax = cgax.parasites[1]

cbar = plt.colorbar(im, ticks=ticks, ax=cgax, fraction=0.046, norm=norm, pad=0.05)
cbar.set_label("Hydrometeorclass")
caax.set_xlabel("Range [km]")
caax.set_ylabel("Range [km]")
labels = [pr_types[i][1] for i, _ in enumerate(pr_types)]
labels = cbar.ax.set_yticklabels(labels)
t = cgax.set_title("Hydrometeorclassification", y=1.05)
cgax.set_xlim(0, 40000)
cgax.set_ylim(0, 14000)
plt.tight_layout()
../../_images/notebooks_classify_2d_hmc_60_0.png