Hydrometeorclassification - PPI

Contents

Hydrometeorclassification - PPI#

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.

PPI Stratiform (2014-10-07 02:45)
PPI Convective (2013-07-01 15:40)

import datetime as dt

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

Utility Functions#

import radarpolarimetry as rp

Precipitation Types#

pr_types = dict(wrl.classify.pr_types.values())
for k, v in enumerate(pr_types.items()):
    print(f"{k:02} {' - '.join(v)}")

LR - Light Rain
MR - Moderate Rain
HR - Heavy Rain
LD - Large Drops
HL - Hail
RH - Rain/Hail
GH - Graupel/Hail
DS - Dry Snow
WS - Wet Snow
HC - H Crystals
VC - V Crystals
NP - No Precip

Load Sounding Data#

# http://weather.uwyo.edu/upperair/sounding.html
rs_time_s = dt.datetime(2014, 10, 7, 2, 0)
rs_time_c = dt.datetime(2013, 7, 1, 12, 0)
import urllib

try:
    rs_data_s, rs_meta_s = wrl.io.get_radiosonde(10410, rs_time_s, cols=(0, 1, 2))
    rs_data_c, rs_meta_c = wrl.io.get_radiosonde(10410, rs_time_c, cols=(0, 1, 2))
    print("downloaded from UWYO")
except urllib.error.HTTPError:
    raise
print(rs_meta_s, rs_meta_c)

downloaded from UWYO
{'Station identifier': 'EDZE', 'Station number': 10410, 'Observation time': datetime.datetime(2014, 10, 7, 0, 0), 'Station latitude': 51.4, 'Station longitude': 6.97, 'Station elevation': 147.0, 'Showalter index': 7.79, 'Lifted index': 4.55, 'LIFT computed using virtual temperature': 4.63, 'SWEAT index': 204.28, 'K index': 22.9, 'Cross totals index': 14.7, 'Vertical totals index': 24.7, 'Totals totals index': 39.4, 'Convective Available Potential Energy': 28.92, 'CAPE using virtual temperature': 36.38, 'Convective Inhibition': -70.37, 'CINS using virtual temperature': -11.48, 'Equilibrum Level': 689.06, 'Equilibrum Level using virtual temperature': 688.7, 'Level of Free Convection': 766.58, 'LFCT using virtual temperature': 881.01, 'Bulk Richardson Number': 1.33, 'Bulk Richardson Number using CAPV': 1.68, 'Temp [K] of the Lifted Condensation Level': 282.6, 'Pres [hPa] of the Lifted Condensation Level': 913.15, 'Equivalent potential temp [K] of the LCL': 313.42, 'Mean mixed layer potential temperature': 290.05, 'Mean mixed layer mixing ratio': 8.2, '1000 hPa to 500 hPa thickness': 5547.0, 'Precipitable water [mm] for entire sounding': 24.77, 'quantity': {'PRES': 'hPa', 'HGHT': 'm', 'TEMP': 'C'}} {'Station identifier': 'EDZE', 'Station number': 10410, 'Observation time': datetime.datetime(2013, 7, 1, 12, 0), 'Station latitude': 51.4, 'Station longitude': 6.97, 'Station elevation': 147.0, 'Showalter index': 6.1, 'Lifted index': 0.57, 'LIFT computed using virtual temperature': 0.51, 'SWEAT index': 77.7, 'K index': 11.7, 'Cross totals index': 13.7, 'Vertical totals index': 28.7, 'Totals totals index': 42.4, 'Convective Available Potential Energy': 7.03, 'CAPE using virtual temperature': 18.0, 'Convective Inhibition': 0.0, 'CINS using virtual temperature': 0.0, 'Equilibrum Level': 597.47, 'Equilibrum Level using virtual temperature': 589.23, 'Level of Free Convection': 931.56, 'LFCT using virtual temperature': 934.17, 'Bulk Richardson Number': 0.24, 'Bulk Richardson Number using CAPV': 0.62, 'Temp [K] of the Lifted Condensation Level': 284.17, 'Pres [hPa] of the Lifted Condensation Level': 934.17, 'Equivalent potential temp [K] of the LCL': 315.07, 'Mean mixed layer potential temperature': 289.76, 'Mean mixed layer mixing ratio': 8.92, '1000 hPa to 500 hPa thickness': 5543.0, 'Precipitable water [mm] for entire sounding': 19.02, 'quantity': {'PRES': 'hPa', 'HGHT': 'm', 'TEMP': 'C'}}

Extract Temperature Profile#

itemp_s = rp.get_temperature_profile(rs_data_s)
itemp_c = rp.get_temperature_profile(rs_data_c)

Plot Temperature Profile#

fig = plt.figure(figsize=(2.5, 5))
ax = fig.add_subplot(111)
itemp_s.plot(y="height", ax=ax, zorder=0, label="stratiform")
itemp_c.plot(y="height", ax=ax, zorder=0, label="convective")
plt.legend()
ax.grid(True)

HMC Workflow#

switch between stratiform and convective processing here

# hmc_ds = hmc_s.copy()
hmc_ds = hmc_c.copy()
# unify masks
hmc_ds = hmc_ds.where(
    ~np.isnan(hmc_ds.DBZH_CORR1)
    & ~np.isnan(hmc_ds.ZDR_CORR1)
    & ~np.isnan(hmc_ds.RHOHV_NC)
    & ~np.isnan(hmc_ds.KDP_VULP)
    & ~np.isnan(hmc_ds.TEMP)
)

Fuzzyfication#

fu = msf_val.wrl.classify.fuzzyfi(
    hmc_ds,
    dict(ZH="DBZH_CORR1", ZDR="ZDR_CORR1", RHO="RHOHV_NC", KDP="KDP_VULP", TEMP="TEMP"),
)
fu = fu.chunk({"hmc": 1})

display(fu)

<xarray.DataArray 'fuzzyfi' (hmc: 11, obs: 5, azimuth: 360, range: 1000)> Size: 158MB
dask.array<xarray-<this-array>, shape=(11, 5, 360, 1000), dtype=float64, chunksize=(1, 5, 360, 1000), chunktype=numpy.ndarray>
Coordinates: (12/25)
  * hmc          (hmc) <U2 88B 'LR' 'MR' 'HR' 'LD' 'HL' ... 'DS' 'WS' 'HC' 'VC'
  * obs          (obs) object 40B 'ZH' 'ZDR' 'RHO' 'KDP' 'TEMP'
  * azimuth      (azimuth) float64 3kB 0.5 1.5 2.5 3.5 ... 357.5 358.5 359.5
    elevation    (azimuth) float64 3kB dask.array<chunksize=(360,), meta=np.ndarray>
    time         (azimuth) datetime64[ns] 3kB dask.array<chunksize=(360,), meta=np.ndarray>
  * range        (range) float32 4kB 50.0 150.0 250.0 ... 9.985e+04 9.995e+04
    ...           ...
    crs_wkt      int64 8B 0
    band         int64 8B 1
    spatial_ref  int64 8B 0
    stdev        float64 8B 0.00471
    NOISE_LEVEL  float64 8B -27.0
    alpha        float64 8B 0.28
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