%matplotlib inline

# Copyright (c) 2022 MetPy Developers.
# Distributed under the terms of the BSD 3-Clause License.
# SPDX-License-Identifier: BSD-3-Clause

Mean Pressure Weighted¶

Use metpy.calc.mean_pressure_weighted as well as pint’s unit support to perform calculations.

The code below uses example data from our test suite to calculate the pressure-weighted mean temperature over a depth of 500 hPa.

import pandas as pd

from metpy.calc import mean_pressure_weighted
from metpy.cbook import get_test_data
from metpy.units import units

Upper air data can be obtained using the siphon package, but for this example we will use some of MetPy’s sample data.

# Set column names
col_names = ['pressure', 'height', 'temperature', 'dewpoint', 'direction', 'speed']

# Read in test data using col_names
df = pd.read_fwf(get_test_data('jan20_sounding.txt', as_file_obj=False),
                 skiprows=5, usecols=[0, 1, 2, 3, 6, 7], names=col_names)

Drop any rows with all NaN values for T, Td, winds

df = df.dropna(subset=('temperature', 'dewpoint', 'direction', 'speed'),
               how='all').reset_index(drop=True)

Isolate pressure, temperature, and height and add units

p = df['pressure'].values * units.hPa
T = df['temperature'].values * units.degC
h = df['height'].values * units.meters

Calculate the mean pressure weighted temperature over a depth of 500 hPa

print(mean_pressure_weighted(p, T, height=h, depth=500 * units.hPa))

[<Quantity(272.583109, 'kelvin')>]