Recipe - FAAM core

FAAM core data have been available as NetCDF ever since the 1st FAAM science flight took place back in 2004. However, details of the NetCDF have changed over time and the FAAM_Dataset class helps to iron out those small annoyances. Maybe at some point all FAAM flights will be reprocessed to create an unified format.

import os
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
from faampy.core.faam_data import FAAM_Dataset


ifile = os.path.join(os.path.expanduser('~'),
                     'gdrive',
                     'core_processing',
                     '2017',
                     'c013-may-17',
                     'core_faam_20170517_v004_r0_c013_1hz.nc')

# read in the FAAM netCDF
ds = FAAM_Dataset(ifile)
# get a pandas dataframe
df = ds.as_dataframe()
# strip of data when aircraft is on the ground
df = df[df.WOW_IND == 0]
# Plot a time series of TAT_ND_R
df.TAT_ND_R.plot()
plt.legend()
plt.grid()
# create 2nd y-axis
plt.twinx()
df.ALT_GIN.plot(color='firebrick')
plt.legend()

# The FAAM_Dataset has a coords attribute which contains the lon, lat, alt
# values for the flight. Those values can be used for plotting a flight track

# opening a new plotting window
plt.figure()
# Setting up the map
ax = plt.axes(projection=ccrs.PlateCarree())
ax.coastlines(resolution='10m')
# extract longitude and latitude from the coords
lon, lat, alt = zip(*ds.coords)
# plot the flight track
ax.plot(lon[::10], lat[::10], lw=2)