Lesson 8 - Quantitative Precipitation Estimation (QPE)

Contents

Lesson 8 - Quantitative Precipitation Estimation (QPE)#

import datetime as dt

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

import radarpolarimetry as rp

Rain Rate Retrieval#

Exercise 1: Calculate Rain Rate Retrievals and Plot#

Marshall-Palmer
- \(z = 200 R^{1.6}\)
- \(R = \frac{z}{200}^{\frac{1}{1.6}}\)
Convective Relation
- \(z = 300 R^{1.4}\)
- \(R = \frac{z}{300}^{\frac{1}{1.4}}\)
\(R(K_{DP})\)
- \(R = 16.9 K_{DP}^{0.801} sign(K_{DP})\)
\(R(A)\)
- \(R = 43.5 A^{0.79}\)

Use R(Z) in light rain and R(KDP)/R(A) in strong rain.

Solution 1: Calculate Rain Rate Retrievals and Plot#

print(radar_ds.DBTH_CORR1.shape)
dbth_corr = wrl.trafo.idecibel(radar_ds.DBTH_CORR1)

rr1 = (dbth_corr / 200.0) ** (1.0 / 1.6)
rr2 = (dbth_corr / 300.0) ** (1.0 / 1.4)

(24, 360, 750)

Plot#

cbar_kwargs = dict(
    extend="both",
    extendrect=False,
    extendfrac="auto",
    pad=0.05,
    fraction=0.1,
)
norm = mpl.colors.LogNorm(0.1, 200.0)
cbar_kwargs.update(dict(label="Rainrate"))
cbar_extend = cbar_kwargs.get("extend", None)
cmap = "turbo"

fig = plt.figure(figsize=(10, 8))
ax = fig.add_subplot(111)
rr1[0].plot(x="x", y="y", ax=ax, norm=norm, cbar_kwargs=cbar_kwargs)

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../../_images/3e7839d0bfe03b5ec521f8e8b97958c4b52aa1605539ce46401d4dc92d67cc6a.png

Apply \(R(A)\) and \(R(K_{DP})\)#

kdp = radar_ds.KDP_AH
rr_kdp = 16.9 * kdp**0.801 * np.sign(kdp)

# needs ZPHI
rr_a = 43.5 * radar_ds.AH**0.79

Plot Rainrates#

cbar_kwargs = dict(
    extend="both",
    extendrect=False,
    extendfrac="auto",
    pad=0.05,
    fraction=0.1,
)
norm = mpl.colors.LogNorm(0.1, 200.0)
cbar_kwargs.update(dict(label="Rainrate"))
cbar_extend = cbar_kwargs.get("extend", None)
cmap = "turbo"

fig = plt.figure(figsize=(10, 8))
ax = fig.add_subplot(111)
rr_kdp[0].plot(x="x", y="y", ax=ax, norm=norm, cbar_kwargs=cbar_kwargs)

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../../_images/9cbab5b21c6a0a79c3f11806d056344938b39255bbe5ec1509daf162299e2e27.png

fig = plt.figure(figsize=(10, 8))
ax = fig.add_subplot(111)
rr_a[0].plot(x="x", y="y", ax=ax, norm=norm, cbar_kwargs=cbar_kwargs)

<matplotlib.collections.QuadMesh at 0x7f6a64bed450>

../../_images/edb9478c03d9a2cf8c2d51722e7a1805c5d616be4506dcc8cb31d284af40c36b.png

Use R(Z) in light rain and R(KDP) in strong rain#

lightrain = rr1.where((rr1 < 20) & (rr1 >= 0))
strongrain = rr_kdp.where(rr1 >= 20)

fig = plt.figure(figsize=(10, 8))
ax = fig.add_subplot(111)
lightrain[0].plot(x="x", y="y", ax=ax, norm=norm, cbar_kwargs=cbar_kwargs)
strongrain[0].plot(x="x", y="y", ax=ax, norm=norm, add_colorbar=False)

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../../_images/95bdb1daba35e88f2cc43eb1c4263f6277ddaa56622651f2e11fc4c29980fe3b.png