GPS and INS#

Position and orientation of Polar 5 and Polar 6 are recorded by an on-board GPS sensor and the internal navigation system (INS). The following example presents the variables recored by these instruments.

Data access#

To analyse the data they first have to be loaded by importing the (AC)³ airborne meta data catalogue. To do so the ac3airborne package has to be installed. More information on how to do that and about the catalog can be found here. Since some of the data, like the preliminary data of the HALO-(AC)³ campaign, is stored on the (AC)³ nextcloud server, username and password as credentials (registration) are required and need to be loaded from environment variables.

import os
ac3cloud_username = os.environ['AC3_USER']
ac3cloud_password = os.environ['AC3_PASSWORD']

import ac3airborne

Load flight phase information#

Polar 5 flights are divided into segments to easily access start and end times of flight patterns. For more information have a look at the respective github repository.

At first we want to load the flight segments of (AC)³ airborne

meta = ac3airborne.get_flight_segments() # this happened before, but it doesn't hurt.

The following command lists all flight segments into the dictionary segments:

segments = {s.get("segment_id"): {**s, "flight_id": flight["flight_id"]}
             for campaign in meta.values()
             for platform in campaign.values()
             for flight in platform.values()
             for s in flight["segments"]
            }

In this example, we want to look at a racetrack pattern during ACLOUD_P5_RF10.

seg = segments["AFLUX_P5_RF10_rt01"]

Using the start and end times of the segment, we slice the data to this flight section.

ds_gps_ins_rt = ds_gps_ins.sel(time=slice(seg["start"], seg["end"]))

Plots#

%matplotlib inline
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import matplotlib.colors as mcolors
import numpy as np
import ipyleaflet
from simplification.cutil import simplify_coords_idx
plt.style.use("../../mplstyle/book")

Plot all flights#

def simplify_dataset(ds, tolerance):
    indices_to_take = simplify_coords_idx(np.stack([ds.lat.values, ds.lon.values], axis=1), tolerance)
    return ds.isel(time=indices_to_take)

# define colors for the flight tracks
colors = [mcolors.to_hex(c)
          for c in plt.cm.inferno(np.linspace(0, 1, len(flights_p5)))]

m = ipyleaflet.Map(basemap=ipyleaflet.basemaps.Esri.NatGeoWorldMap,
                   center=(80., 6), zoom=3)

for (flight_id, flight),color in zip(flights_p5.items(),colors):
    mission = flight['mission']
    # read gps dataset of flight
    ds = cat[mission]['P5']['GPS_INS'][flight_id].to_dask()
    
    # slice to takeoff and landing times
    ds = ds.sel(time=slice(meta[mission]['P5'][flight_id]['takeoff'], meta[mission]['P5'][flight_id]['landing']))
    
    # reduce dataset for plotting
    ds_reduced = simplify_dataset(ds, tolerance=1e-5)
    
    track = ipyleaflet.Polyline(
        locations=np.stack([ds_reduced.lat.values, 
                            ds_reduced.lon.values], axis=1).tolist(), 
        color=color,
        fill=False,
        weight=2,
        name=flight_id)
    
    m.add_layer(track)
    
m.add_control(ipyleaflet.ScaleControl(position='bottomleft'))
m.add_control(ipyleaflet.LegendControl(dict(zip(flights_p5.keys(), colors)),
                                       name="Flights",
                                       position="bottomright"))
m.add_control(ipyleaflet.LayersControl(position='topright'))
m.add_control(ipyleaflet.FullScreenControl())
display(m)

Plot time series of one flight#

fig, ax = plt.subplots(8, 1, sharex=True)

kwargs = dict(s=1, linewidths=0, color='k')

ax[0].scatter(ds_gps_ins.time, ds_gps_ins['alt'], **kwargs)
ax[0].set_ylabel('alt [m]')

ax[1].scatter(ds_gps_ins.time, ds_gps_ins['lon'], **kwargs)
ax[1].set_ylabel('lon [°E]')

ax[2].scatter(ds_gps_ins.time, ds_gps_ins['lat'], **kwargs)
ax[2].set_ylabel('lat [°N]')

ax[3].scatter(ds_gps_ins.time, ds_gps_ins['roll'], **kwargs)
ax[3].set_ylabel('roll [°]')

ax[4].scatter(ds_gps_ins.time, ds_gps_ins['pitch'], **kwargs)
ax[4].set_ylabel('pitch [°]')

ax[5].scatter(ds_gps_ins.time, ds_gps_ins['heading'], **kwargs)
ax[5].set_ylim(-180, 180)
ax[5].set_ylabel('heading [°]')

ax[6].scatter(ds_gps_ins.time, ds_gps_ins['gs'], **kwargs)
ax[6].set_ylabel('gs [kts]')

ax[7].scatter(ds_gps_ins.time, ds_gps_ins['vs'], **kwargs)
ax[7].set_ylabel('vs [m/s]')

ax[-1].xaxis.set_major_formatter(mdates.DateFormatter('%H:%M'))

plt.show()

Plot time series of racetrack pattern#

fig, ax = plt.subplots(8, 1, sharex=True)

kwargs = dict(s=1, linewidths=0, color='k')

ax[0].scatter(ds_gps_ins_rt.time, ds_gps_ins_rt['alt'], **kwargs)
ax[0].set_ylabel('alt [m]')

ax[1].scatter(ds_gps_ins_rt.time, ds_gps_ins_rt['lon'], **kwargs)
ax[1].set_ylabel('lon [°E]')

ax[2].scatter(ds_gps_ins_rt.time, ds_gps_ins_rt['lat'], **kwargs)
ax[2].set_ylabel('lat [°N]')

ax[3].scatter(ds_gps_ins_rt.time, ds_gps_ins_rt['roll'], **kwargs)
ax[3].set_ylabel('roll [°]')

ax[4].scatter(ds_gps_ins_rt.time, ds_gps_ins_rt['pitch'], **kwargs)
ax[4].set_ylabel('pitch [°]')

ax[5].scatter(ds_gps_ins_rt.time, ds_gps_ins_rt['heading'], **kwargs)
ax[5].set_ylim(-180, 180)
ax[5].set_ylabel('heading [°]')

ax[6].scatter(ds_gps_ins_rt.time, ds_gps_ins_rt['gs'], **kwargs)
ax[6].set_ylabel('gs [kts]')

ax[7].scatter(ds_gps_ins_rt.time, ds_gps_ins_rt['vs'], **kwargs)
ax[7].set_ylabel('vs [m/s]')

ax[-1].xaxis.set_major_formatter(mdates.DateFormatter('%H:%M'))

plt.show()

how_to_ac3airborne

GPS and INS

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