Note
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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
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')>]
Total running time of the script: ( 0 minutes 0.020 seconds)