Flight-phase separation#

The idea behind flight-phase-separation is to easily select segments of research flights based on their properties as required for the user’s research. The selection can be done using any combination of attributes. In the following sections, the stored attributes and the structure of files under flight_phase_files is presented.

Interactive map of flight segments#

This example displays the Polar 5 flight segments from ACLOUD_P5_RF14 on an interactive map.

import ac3airborne
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import to_hex
from matplotlib import cm
import ipyleaflet
from ipyleaflet import Polyline, Map, basemaps, basemap_to_tiles
from simplification.cutil import simplify_coords_idx
plt.style.use("mplstyle/book")


def colors(n):
    """Creates set of random colors of length n"""
    
    cmap = cm.get_cmap('gist_rainbow')
    rnd = np.random.uniform(low=0, high=1, size=n)
    cols = cmap(rnd)
    
    return cols


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)

# load intake catalog and flight segments
cat = ac3airborne.get_intake_catalog()
meta = ac3airborne.get_flight_segments()

mission = 'MOSAiC-ACA'
platform = 'P5'
flight_id = 'MOSAiC-ACA_P5_RF11'

# read gps data of one flight
ds_gps_ins = cat[mission][platform]['GPS_INS'][flight_id].to_dask()
flight = meta[mission][platform][flight_id]

# plot map
m = Map(basemap=basemap_to_tiles(basemaps.NASAGIBS.ModisTerraTrueColorCR, str(flight['date'])),
        center=(80, 10),
        zoom=4,
        scroll_wheel_zoom=True)

col_segments = colors(len(flight['segments']))

for i, segment in enumerate(flight['segments']):
    
    # reduce flight segment
    ds_reduced = simplify_dataset(ds_gps_ins.sel(time=slice(segment['start'], 
                                                            segment['end'])), 
                                  1e-5)
    
    # plot flight segment
    line = Polyline(locations=np.stack([ds_reduced.lat.values, ds_reduced.lon.values], axis=1).tolist(),
                    color=to_hex(col_segments[i]),
                    fill=False,
                    weight=2,
                    name=str(i) + ': ' + segment['name'])
    m.add_layer(line)

m.add_control(ipyleaflet.ScaleControl())
m.add_control(ipyleaflet.LayersControl(position='topright'))
m.add_control(ipyleaflet.FullScreenControl())

display(m)

Flight keywords#

name: research flight number within campaign (e.g. RF10)

mission: name of campaign (ACLOUD, AFLUX, MOSAiC-ACA)

platform: aircraft short name (P5, P6)

flight_id: unique identifier for flight (campaign_platform_name)

contacts: information on mission PI and creator of .yaml file (name and email contact)

date: date of flight (will be read as python datetime.date object)

flight_report: link to the online flight report with detailed information on the flight (flight objectives, quicklooks, photos)

takeoff: flight takeoff at the airport (will be read as python datetime.datetime object)

landing: flight landing at the airport (will be read as python datetime.datetime object)

events: planned flight features, such as patterns, joint flights between aircraft, satellite overpasses, or instrument calibration

remarks: information on atmospheric conditions and instrument status

segments: list of flight segments

Keyword segments#

The following section provides an overview of the flight segments and their properties. They are ordered chronologically in a list, with the first segment starting at flight takeoff and the last segment ending at flight landing. The given start and end times of each segment are included.

Segment keywords#

kinds: list of properties, which describe the flight segment (see next section)

name: name of segment, that is unique within the flight

irregularities: list of irregularities in the flight track or caused by other factors, such as icing

segment_id: unique id of flight segment

start: start of segment (will be read as python datetime.datetime object)

end: end of segment (will be read as python datetime.datetime object)

levels: median flight level(s) or highest and lowest flight level for ascents/descents [in feet]

dropsondes: list of dropsondes released during that segment

parts: list of sub-segments (parts), which belong to a flight pattern (see next section)

Keyword kinds#

Making flight-phase-separation a useful tool for various communities, either working on in-situ or remotely sensed data, the various flight maneuvers are classified and separated. The following section provides an overview of the kinds used to describe the flight segments. Multiple keywords can be used for one flight segment. In total, 33 kinds are defined to classify the segments.

Takeoff and landing maneuvers#

The takeoff and landing maneuvers are labelled as major_ascent (takeoff) and major_descent (landing). Their start and end times correspond to the flight takeoff and landing keyword, respectively.

Name

Description

Ending of segment_id

major_ascent

takeoff from the airport until a constant flight level is reached

ma

major_descent

last descent from a constant flight level before landing at the airport

md

Straight segment with constant flight altitude#

Straight flight segments with constant flight altitude are classified into three height levels.

Name

Description

Ending of segment_id

low_level

Constant flight level at < 1 km altitude

ll

mid_level

Constant flight level at 1-2 km altitude

ml

high_level

Constant flight level at > 2 km altitude

hl

Ascents and descents#

Ascents and descents are classified by two properties: (1) the height difference between lowest and highest point, and (2) the vertical velocity. Note that sometimes ascents or descents may be flown within curves.

Name

Description

Ending of segment_id

small_ascent

Ascent with height difference < 1 km

sa

small_descent

Descent with height difference < 1 km

sd

medium_ascent

Ascent with height difference of 1-2 km

ma

medium_descent

Descent with height difference of 1-2 km

md

large_ascent

Ascent with height difference > 2 km

la

large_descent

Descent with height difference > 2 km

ld

profiling

Ascent or descent with vertical speed mostly between 5-10 m/s

Overflights, underflights and co-locations#

During many research flights, the research station in Ny-Alesund and the research vessel Polarstern are overflown, or the satellite constellation A-train is underflown. These segments are marked and easily accessible. Additionally, co-locations between aircraft are marked.

Name

Description

Ending of segment_id

nya_overflight

Overflight over the Ny-Alesund research station within 15 km. Might be combined with cross_pattern or high_level

polarstern_overflight

Overflight over RV Polarstern within 15 km. Might be combined with cross_pattern

a-train_underflight

Underflight of the A-Train satellite constellation. For Polar 5, it is usually combined with high_level. For Polar 6 it might be any profiling pattern

sveabreen_glacier_overflight

Overflight over the Sveabreen glacier, Svalbard, mostly during major_ascent

p6_co-location

Co-location of Polar 5 with Polar 6

p5_co-location

Co-location of Polar 6 with Polar 5

Instrument calibration and testing#

Especially in the beginning of campaigns, maneuvers are flown to test or calibrate instrumentation. The keywords for these segments are listed in the table below.

Name

Description

Ending of segment_id

radiometer_calibration

Segment dedicated for radiometer calibration

instrument_testing

Segment for instrument testing, usually at the beginning of campaigns, where various maneuvers are flown

it

Curves and circles#

Different curves and circles are flown, depending on the angle or flight direction. These segments are not described by further keywords. Note, that segments, which are only curves (without ascent or descent), do not have the keywords irregularities, segment_id, levels, and dropsondes.

Name

Description

Ending of segment_id

short_turn

Turn directly to another flight direction (cutting corner)

long_turn

Turn on the long way to another flight direction (without cutting corner)

procedure_turn

Turn of 180° to be directly on track again in opposite direction

360_turn

360° circle, which is not part of a specific pattern

cross_pattern_turn

Transfer leg during cross_pattern

Patterns#

Different flight patterns with each specific properties were flown during the campaigns. Each pattern segment consists of sub-segments, which are listed under the keyword parts. Every part can be one of the above mentioned segments.

Name

Description

Ending of segment_id

cross_pattern

Rectangular crosses with transfer legs in between, usually flown over Ny-Alesund or Polarstern

cr

racetrack_pattern

Back and forth on the same track, with possible height changes

rt

holding_pattern

Time spending or waiting

ho

stairstep_pattern

Step wise ascenting or descenting along a straight line

ss

sawtooth_pattern

Alternating ascents and descents along a straight line

st

radiation_square

Square pattern at high altitude for radiation measurements

rs

noseboom_calibration_pattern

Pattern dedicated to calibrate the nose boom

np

Template of .yaml file#

Below, a template of a flight-phase-separation .yaml file is shown. A description of the keywords is presented in the previous sections.

name: RF04
mission: ACLOUD
platform: P5
flight_id: ACLOUD_P5_RF04
contacts:
- name:
  email:
  tags:
  - pi  # PI of flight
- name:
  email:
  tags:
  - lc  # list creator
date: 2017-05-23
flight_report: null
takeoff: 2017-05-23 09:11:47
landing: 2017-05-23 14:23:40
events: []
remarks: []
segments:
- kinds:
  - major_ascent
  name: major ascent 1
  irregularities: []
  segment_id: ACLOUD_P5_RF04_ma
  start: 2017-05-23 09:11:47
  end: null
  levels: []
  dropsondes: []
- kinds:
  - add_new_segments_here
  name: null
  irregularities: []
  segment_id: null
  start: null
  end: null
  levels: []
  dropsondes: []
- kinds:
  - major_descent
  name: major descent 1
  irregularities: []
  segment_id: ACLOUD_P5_RF04_md
  start: null
  end: 2017-05-23 14:23:40
  levels: []
  dropsondes: []