如何使用WRFOUT绘制探空图进阶版
前言
本项目将带领您使用WRFOUT数据绘制探空图，探索大气垂直结构。我们将使用Python中的MetPy库和Matplotlib库来处理和可视化WRF模型输出数据。
在本项目中，我们将学习如何：
从WRFOUT文件中提取探空所需的变量，如压力、温度、露点温度、风向和风速。
使用MetPy库将变量单位转换为适当的物理单位，并计算其他有用的气象参数，如相对湿度。
使用Matplotlib库创建探空图，展示大气垂直结构，并标注重要的气象参数。
添加自定义标记和注释，以使探空图更具可读性和专业性。
通过完成本项目，您将掌握使用Python处理WRF模型输出数据并绘制探空图的基本技能，有助于您更好地理解和分析大气中的垂直变化。
镜像：气象分析3.7
数据：自制wrfout数据
提示：代码参考https://unidata.github.io/MetPy/latest/examples/Advanced_Sounding_With_Complex_Layout.html
导入库
In :
#
First let's start with some simple imports
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import metpy.calc as mpcalc
from metpy.cbook import get_test_data
from metpy.plots import add_metpy_logo, Hodograph, SkewT
from metpy.units import units
from wrf import uvmet, to_np, getvar, interplevel, smooth2d, get_cartopy, cartopy_xlim, cartopy_ylim, latlon_coords,ll_to_xy
import numpy as np
from netCDF4 import Dataset
import matplotlib.pyplot as plt
import matplotlib as m
import metpy.calc as mpcalc
from metpy.plots import Hodograph, SkewT
from metpy.units import units
import metpy.calc as mpcalc
from metpy.cbook import get_test_data
from metpy.plots import add_metpy_logo, SkewT
from metpy.units import units
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
数据读取与转化为pandas
In :
#读取WRF输出文件
wrfin = Dataset('/home/mw/input/wrfout3385/wrfout_d02_2022-07-14_0700.nc')
#指定要提取的经纬度坐标点
lat_lon =
#将经纬度坐标转换为模型坐标系（x, y）
x_y = ll_to_xy(wrfin, lat_lon, lat_lon)
#提取所需变量数据
p = getvar(wrfin, "pressure", timeidx=0)[:, x_y, x_y] * units.hPa
T = getvar(wrfin, "tc", timeidx=0)[:, x_y, x_y] * units.degC
Td = getvar(wrfin, "td", timeidx=0)[:, x_y, x_y] * units.degC
u = getvar(wrfin, "ua", timeidx=0)[:, x_y, x_y] * units('m/s')
v = getvar(wrfin, "va", timeidx=0)[:, x_y, x_y] * units('m/s')
h = getvar(wrfin, "height", timeidx=0)[:, x_y, x_y]
# 计算风向和风速
wind_dir = mpcalc.wind_direction(u, v)
wind_speed = mpcalc.wind_speed(u, v)因为是模拟数据，没有nan值就不作处理
In :
# 将变量转换为pandas数据框
data = {'pressure': p, 'height': h, 'temperature': T,
'dewpoint': Td, 'direction': wind_dir, 'speed': wind_speed}
df = pd.DataFrame(data)
df
Out:
给变量乘单位
In :
p = df['pressure'].values * units.hPa
z = df['height'].values * units.m
T = df['temperature'].values * units.degC
Td = df['dewpoint'].values * units.degC
wind_speed = df['speed'].values * units.knots
wind_dir = df['direction'].values * units.degrees
u, v = mpcalc.wind_components(wind_speed, wind_dir)
简易版
In :fig = plt.figure(figsize=(9, 9))
skew = SkewT(fig, rotation=45, rect=(0.1, 0.1, 0.55, 0.85))
# Plot the data using normal plotting functions, in this case using
# log scaling in Y, as dictated by the typical meteorological plot
skew.plot(p, T, 'r')
skew.plot(p, Td, 'g')
skew.plot_barbs(p, u, v)
# Change to adjust data limits and give it a semblance of what we want
skew.ax.set_adjustable('datalim')
skew.ax.set_ylim(1000, 100)
skew.ax.set_xlim(-20, 30)
# Add the relevant special lines
skew.plot_dry_adiabats()
skew.plot_moist_adiabats()
skew.plot_mixing_lines()
# Create a hodograph
ax = plt.axes((0.7, 0.75, 0.2, 0.2))
h = Hodograph(ax, component_range=60.)
h.add_grid(increment=20)
h.plot(u, v)
Out:
[<matplotlib.lines.Line2D at 0x7f45e9a01650>]
进阶版
In :
# STEP 1: CREATE THE SKEW-T OBJECT AND MODIFY IT TO CREATE A
# NICE, CLEAN PLOT
# Create a new figure. The dimensions here give a good aspect ratio
fig = plt.figure(figsize=(18, 12))
skew = SkewT(fig, rotation=45, rect=(0.05, 0.05, 0.50, 0.90))
# Change to adjust data limits and give it a semblance of what we want
skew.ax.set_adjustable('datalim')
skew.ax.set_ylim(1000, 100)
skew.ax.set_xlim(-20, 30)
# Set some better labels than the default to increase readability
skew.ax.set_xlabel(str.upper(f'Temperature ({T.units:~P})'), weight='bold')
skew.ax.set_ylabel(str.upper(f'Pressure ({p.units:~P})'), weight='bold')
# Set the facecolor of the skew-t object and the figure to white
fig.set_facecolor('#ffffff')
skew.ax.set_facecolor('#ffffff')
# Here we can use some basic math and Python functionality to make a cool
# shaded isotherm pattern.
x1 = np.linspace(-100, 40, 8)
x2 = np.linspace(-90, 50, 8)
y =
for i in range(0, 8):
skew.shade_area(y=y, x1=x1<i>, x2=x2<i>, color='gray', alpha=0.02, zorder=1)
# STEP 2: PLOT DATA ON THE SKEW-T. TAKE A COUPLE EXTRA STEPS TO
# INCREASE READABILITY
# Plot the data using normal plotting functions, in this case using
# log scaling in Y, as dictated by the typical meteorological plot
# Set the linewidth to 4 for increased readability.
# We will also add the 'label' keyword argument for our legend.
skew.plot(p, T, 'r', lw=4, label='TEMPERATURE')
skew.plot(p, Td, 'g', lw=4, label='DEWPOINT')
# Again we can use some simple Python math functionality to 'resample'
# the wind barbs for a cleaner output with increased readability.
# Something like this would work.
interval = np.logspace(2, 3, 40) * units.hPa
idx = mpcalc.resample_nn_1d(p, interval)
skew.plot_barbs(pressure=p, u=u, v=v)
# Add the relevant special lines native to the Skew-T Log-P diagram &
# provide basic adjustments to linewidth and alpha to increase readability
# first, we add a matplotlib axvline to highlight the 0-degree isotherm
skew.ax.axvline(0 * units.degC, linestyle='--', color='blue', alpha=0.3)
skew.plot_dry_adiabats(lw=1, alpha=0.3)
skew.plot_moist_adiabats(lw=1, alpha=0.3)
skew.plot_mixing_lines(lw=1, alpha=0.3)
# Calculate LCL height and plot as a black dot. Because `p`'s first value is
# ~1000 mb and its last value is ~250 mb, the `0` index is selected for
# `p`, `T`, and `Td` to lift the parcel from the surface. If `p` was inverted,
# i.e. start from a low value, 250 mb, to a high value, 1000 mb, the `-1` index
# should be selected.
lcl_pressure, lcl_temperature = mpcalc.lcl(p, T, Td)
skew.plot(lcl_pressure, lcl_temperature, 'ko', markerfacecolor='black')
# Calculate full parcel profile and add to plot as black line
prof = mpcalc.parcel_profile(p, T, Td).to('degC')
skew.plot(p, prof, 'k', linewidth=2, label='SB PARCEL PATH')
# Shade areas of CAPE and CIN
skew.shade_cin(p, T, prof, Td, alpha=0.2, label='SBCIN')
skew.shade_cape(p, T, prof, alpha=0.2, label='SBCAPE')
# STEP 3: CREATE THE HODOGRAPH INSET. TAKE A FEW EXTRA STEPS TO
# INCREASE READABILITY
# Create a hodograph object: first we need to add an axis
# then we can create the Metpy Hodograph
hodo_ax = plt.axes((0.48, 0.45, 0.5, 0.5))
h = Hodograph(hodo_ax, component_range=80.)
# Add two separate grid increments for a cooler look. This also
# helps to increase readability
h.add_grid(increment=20, ls='-', lw=1.5, alpha=0.5)
h.add_grid(increment=10, ls='--', lw=1, alpha=0.2)
# The next few steps makes for a clean hodograph inset, removing the
# tick marks, tick labels, and axis labels
h.ax.set_box_aspect(1)
h.ax.set_yticklabels([])
h.ax.set_xticklabels([])
h.ax.set_xticks([])
h.ax.set_yticks([])
h.ax.set_xlabel(' ')
h.ax.set_ylabel(' ')
# Here we can add a simple Python for loop that adds tick marks
# to the inside of the hodograph plot to increase readability!
plt.xticks(np.arange(0, 0, 1))
plt.yticks(np.arange(0, 0, 1))
for i in range(10, 120, 10):
h.ax.annotate(str(i), (i, 0), xytext=(0, 2), textcoords='offset pixels',
clip_on=True, fontsize=10, weight='bold', alpha=0.3, zorder=0)
for i in range(10, 120, 10):
h.ax.annotate(str(i), (0, i), xytext=(0, 2), textcoords='offset pixels',
clip_on=True, fontsize=10, weight='bold', alpha=0.3, zorder=0)
# plot the hodograph itself, using plot_colormapped, colored
# by height
h.plot_colormapped(u, v, c=z, linewidth=6, label='0-12km WIND')
# compute Bunkers storm motion so we can plot it on the hodograph!
RM, LM, MW = mpcalc.bunkers_storm_motion(p, u, v, z)
h.ax.text((RM.m + 0.5), (RM.m - 0.5), 'RM', weight='bold', ha='left',
fontsize=13, alpha=0.6)
h.ax.text((LM.m + 0.5), (LM.m - 0.5), 'LM', weight='bold', ha='left',
fontsize=13, alpha=0.6)
h.ax.text((MW.m + 0.5), (MW.m - 0.5), 'MW', weight='bold', ha='left',
fontsize=13, alpha=0.6)
h.ax.arrow(0, 0, RM.m - 0.3, RM.m - 0.3, linewidth=2, color='black',
alpha=0.2, label='Bunkers RM Vector',
length_includes_head=True, head_width=2)
# STEP 4: ADD A FEW EXTRA ELEMENTS TO REALLY MAKE A NEAT PLOT
# First we want to actually add values of data to the plot for easy viewing
# To do this, let's first add a simple rectangle using Matplotlib's 'patches'
# functionality to add some simple layout for plotting calculated parameters
# xloc yloc xsizeysize
fig.patches.extend([plt.Rectangle((0.563, 0.05), 0.334, 0.37,
edgecolor='black', facecolor='white',
linewidth=1, alpha=1, transform=fig.transFigure,
figure=fig)])
# Now let's take a moment to calculate some simple severe-weather parameters using
# metpy's calculations
# Here are some classic severe parameters!
kindex = mpcalc.k_index(p, T, Td)
total_totals = mpcalc.total_totals_index(p, T, Td)
# mixed layer parcel properties!
ml_t, ml_td = mpcalc.mixed_layer(p, T, Td, depth=50 * units.hPa)
ml_p, _, _ = mpcalc.mixed_parcel(p, T, Td, depth=50 * units.hPa)
mlcape, mlcin = mpcalc.mixed_layer_cape_cin(p, T, prof, depth=50 * units.hPa)
# most unstable parcel properties!
mu_p, mu_t, mu_td, _ = mpcalc.most_unstable_parcel(p, T, Td, depth=50 * units.hPa)
mucape, mucin = mpcalc.most_unstable_cape_cin(p, T, Td, depth=50 * units.hPa)
# Estimate height of LCL in meters from hydrostatic thickness (for sig_tor)
new_p = np.append(p, lcl_pressure)
new_t = np.append(T, lcl_temperature)
lcl_height = mpcalc.thickness_hydrostatic(new_p, new_t)
# Compute Surface-based CAPE
sbcape, sbcin = mpcalc.surface_based_cape_cin(p, T, Td)
# Compute SRH
(u_storm, v_storm), *_ = mpcalc.bunkers_storm_motion(p, u, v, z)
*_, total_helicity1 = mpcalc.storm_relative_helicity(z, u, v, depth=1 * units.km,
storm_u=u_storm, storm_v=v_storm)
*_, total_helicity3 = mpcalc.storm_relative_helicity(z, u, v, depth=3 * units.km,
storm_u=u_storm, storm_v=v_storm)
*_, total_helicity6 = mpcalc.storm_relative_helicity(z, u, v, depth=6 * units.km,
storm_u=u_storm, storm_v=v_storm)
# Copmute Bulk Shear components and then magnitude
ubshr1, vbshr1 = mpcalc.bulk_shear(p, u, v, height=z, depth=1 * units.km)
bshear1 = mpcalc.wind_speed(ubshr1, vbshr1)
ubshr3, vbshr3 = mpcalc.bulk_shear(p, u, v, height=z, depth=3 * units.km)
bshear3 = mpcalc.wind_speed(ubshr3, vbshr3)
ubshr6, vbshr6 = mpcalc.bulk_shear(p, u, v, height=z, depth=6 * units.km)
bshear6 = mpcalc.wind_speed(ubshr6, vbshr6)
# Use all computed pieces to calculate the Significant Tornado parameter
sig_tor = mpcalc.significant_tornado(sbcape, lcl_height,
total_helicity3, bshear3).to_base_units()
# Perform the calculation of supercell composite if an effective layer exists
super_comp = mpcalc.supercell_composite(mucape, total_helicity3, bshear3)
# There is a lot we can do with this data operationally, so let's plot some of
# these values right on the plot, in the box we made
# First lets plot some thermodynamic parameters
plt.figtext(0.58, 0.37, 'SBCAPE: ', weight='bold', fontsize=15,
color='black', ha='left')
plt.figtext(0.71, 0.37, f'{sbcape:.0f~P}', weight='bold',
fontsize=15, color='orangered', ha='right')
plt.figtext(0.58, 0.34, 'SBCIN: ', weight='bold',
fontsize=15, color='black', ha='left')
plt.figtext(0.71, 0.34, f'{sbcin:.0f~P}', weight='bold',
fontsize=15, color='lightblue', ha='right')
plt.figtext(0.58, 0.29, 'MLCAPE: ', weight='bold', fontsize=15,
color='black', ha='left')
plt.figtext(0.71, 0.29, f'{mlcape:.0f~P}', weight='bold',
fontsize=15, color='orangered', ha='right')
plt.figtext(0.58, 0.26, 'MLCIN: ', weight='bold', fontsize=15,
color='black', ha='left')
plt.figtext(0.71, 0.26, f'{mlcin:.0f~P}', weight='bold',
fontsize=15, color='lightblue', ha='right')
plt.figtext(0.58, 0.21, 'MUCAPE: ', weight='bold', fontsize=15,
color='black', ha='left')
plt.figtext(0.71, 0.21, f'{mucape:.0f~P}', weight='bold',
fontsize=15, color='orangered', ha='right')
plt.figtext(0.58, 0.18, 'MUCIN: ', weight='bold', fontsize=15,
color='black', ha='left')
plt.figtext(0.71, 0.18, f'{mucin:.0f~P}', weight='bold',
fontsize=15, color='lightblue', ha='right')
plt.figtext(0.58, 0.13, 'TT-INDEX: ', weight='bold', fontsize=15,
color='black', ha='left')
plt.figtext(0.71, 0.13, f'{total_totals:.0f~P}', weight='bold',
fontsize=15, color='orangered', ha='right')
plt.figtext(0.58, 0.10, 'K-INDEX: ', weight='bold', fontsize=15,
color='black', ha='left')
plt.figtext(0.71, 0.10, f'{kindex:.0f~P}', weight='bold',
fontsize=15, color='orangered', ha='right')
# now some kinematic parameters
plt.figtext(0.73, 0.37, '0-1km SRH: ', weight='bold', fontsize=15,
color='black', ha='left')
plt.figtext(0.88, 0.37, f'{total_helicity1:.0f~P}',
weight='bold', fontsize=15, color='navy', ha='right')
plt.figtext(0.73, 0.34, '0-1km SHEAR: ', weight='bold', fontsize=15,
color='black', ha='left')
plt.figtext(0.88, 0.34, f'{bshear1:.0f~P}', weight='bold',
fontsize=15, color='blue', ha='right')
plt.figtext(0.73, 0.29, '0-3km SRH: ', weight='bold', fontsize=15,
color='black', ha='left')
plt.figtext(0.88, 0.29, f'{total_helicity3:.0f~P}',
weight='bold', fontsize=15, color='navy', ha='right')
plt.figtext(0.73, 0.26, '0-3km SHEAR: ', weight='bold', fontsize=15,
color='black', ha='left')
plt.figtext(0.88, 0.26, f'{bshear3:.0f~P}', weight='bold',
fontsize=15, color='blue', ha='right')
plt.figtext(0.73, 0.21, '0-6km SRH: ', weight='bold', fontsize=15,
color='black', ha='left')
plt.figtext(0.88, 0.21, f'{total_helicity6:.0f~P}',
weight='bold', fontsize=15, color='navy', ha='right')
plt.figtext(0.73, 0.18, '0-6km SHEAR: ', weight='bold', fontsize=15,
color='black', ha='left')
plt.figtext(0.88, 0.18, f'{bshear6:.0f~P}', weight='bold',
fontsize=15, color='blue', ha='right')
plt.figtext(0.73, 0.13, 'SIG TORNADO: ', weight='bold', fontsize=15,
color='black', ha='left')
plt.figtext(0.88, 0.13, f'{sig_tor:.0f~P}', weight='bold', fontsize=15,
color='orangered', ha='right')
plt.figtext(0.73, 0.10, 'SUPERCELL COMP: ', weight='bold', fontsize=15,
color='black', ha='left')
plt.figtext(0.88, 0.10, f'{super_comp:.0f~P}', weight='bold', fontsize=15,
color='orangered', ha='right')
# Add legends to the skew and hodo
skewleg = skew.ax.legend(loc='upper left')
hodoleg = h.ax.legend(loc='upper left')
# add a quick plot title, this could be automated by
# declaring a station and datetime variable when using
# realtime observation data from Siphon.
plt.figtext(0.45, 0.97, 'OUN | MAY 4TH 1999 - 00Z VERTICAL PROFILE',
weight='bold', fontsize=20, ha='center')
# Show the plot
plt.show()</i></i>
实际上只在数据读取做了小改，其他没怎么动
SBCAPE：这个CAPE值考察了从表面上升的空气团的不稳定性。这个值往往高于MLCAPE。
MLCAPE：在大多数风暴追踪时使用的最佳CAPE版本是MLCAPE，因为它往往是地表或近地表上升气流将摄入的最具代表性的空气。描述MLCAPE的最不技术的方法是，它平均了风暴云基以下的CAPE值。
MUCAPE：这项测量发现了测深中最不稳定的空气块，并记录了该值。通常，当有强烈的地表反转时，这是最有用的，因此风暴可能会升高。
以上机翻自https://www.tornadotitans.com/
还有什么风暴螺旋度、风切变什么就不多介绍了，什么k指数，tt指数想必老师也教过
另外要注意就是单位问题，有什么需要就自行替换
程序在线运行点这里
文章来源于微信公众号：气ython风雨
好久没看到这样的好贴了，支持一下！
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