Segmentation of HALO flights during EUREC4A

The HALO Research Flights (RFs) during EUREC4A can be divided into different segments. For example circles and straight legs were purposefully conducted maneuvers during which a distinct sampling behaviour of the various instruments can be assumed. For future analyses based on specific kinds of flight segments (e.g. only based on circles) it is desired to use a common set of start- and end-times to assure consistency between the studies. This repository provides the start- and end-times of the flight-segments for each RF together with some very general information about the RF or special events during the RF.

Methodology to determine flight segments

A flight segment is a period of some constant characteristics during a RF. For example during a circle segment, the roll angle and the temporal change in aircraft heading can be assumed to be constant. The circles during EUREC4A were especially associated with the regular launch of dropsondes, most of the time 12 per circle, every 30 ° heading. Such general characteristics of the various flight segments and a first idea about the flight patterns from the flight reports (available on aeris) are used as a starting point to approach the flight phase segmentation. The BAHAMAS datasets and the dropsonde launch times are then analysed to consistently determine the specific flight segment timestamps. For this purpose, BAHAMAS and dropsonde datasets provided by Heike Konow that are projected on a unified (time, lat, lon) grid are used, which are for example available on this ftp server:

ftp-projects.mpimet.mpg.de

The following flight segments are identified, where names in brackets directly correspond to the segment-kinds in the YAML files. The criteria to determine the start- and end-times of the segments are noted below each flight segment.

circle (circle):

• Defined via launch times of first and last dropsonde during each of the circles.
• A circle typically ranges over 330° rather than 360°, because the final dropsonde of a circle is launched 30° ahead of the geometrical circle closure.
• If the first or final dropsonde is missing (e.g. due to instrument failure), the circle starts or closes with the first or last available dropsonde.
• The circle is defined this way because it appears most conservative with regard to comparability between dropsonde and remote sensing data within a circle.

circle break (circle_break):

• Periods between two consecutive circles, during which no dropsondes were launched.
• Circle breaks stand out to breaks between for example a circle and a straight-leg because during these it is assured that the aircraft remained on the circle track.
• Defined by launch times of last dropsonde of previous circle and first dropsonde of next circle.
• Circle breaks may be used to obtain all the available remote sensing data from circles, neglecting availability of dropsonde data.

clover leg (clover_leg):

• Defined as the long legs of a clover flight pattern with close to 2° roll angle.
• Dropsondes were launched every 30° along clover legs.
• The transitions between circle pattern and clover pattern are excluded, because of steep roll angles of about 30°.
• Clover legs are not defined via launch times of first and last dropsonde, because they don't always represent the whole leg.

clover turn (clover_turn):

• Periods between two consecutive clover legs (smooth transition), with steeper roll angles of about 6°.
• These periods are constrained to the periods during the clover pattern where the aircraft roll angle deviates clearly from 2°.
• During these turns no dropsondes were launched.

straight leg (straight_leg):

• Period with constant aircraft heading and close to 0° roll angle.
• Straight legs were flown with various purposes, which are more closely described by the straight leg "name"-Parameter in the YAML files.

lidar calibration (lidar_calibration):

• Maneuver typically conducted during the final descent of most RFs in FL160.
• Defined as the period of the aircraft being in FL160 and close to 0° roll angle.

radar calibration with wiggle (radar_calibration_wiggle):

• Maneuver typically conducted during straight legs, where the aircraft tilts to a roll angle of first -20° and then +20°.
• For these cases, the straight leg is split into three flight segments: 1.) straight_leg, 2.) radar_cal_wiggle, 3.) straight_leg.
• The "rad_cal_wiggle" period is constrained to the period where the aircraft roll angle deviates from 0° and the heading is not constant.

radar calibration with constant bank (radar_calibration_tilted):

• Maneuver typically conducted at the end of a straight leg, where a narrow circle pattern with a constant 10° bank is flown.
• A constant roll angle of 10° is used to define the period of a "radar_cal_tilted" segment.

baccardi calibration square (baccardi_calibration):

• Defined by 4 turns indicated by roll angles of about 25° (1 turn: -25°, 3 turns: +25°).

These criteria are applied by making use of the FlightPhaseTools.py python module in jupyter notebooks for each individual RF. This module rather provides useful functions to search for the start- and end-times of the flight segments than to automate this process. To validate whether the found timestamps for the different segments are plausible, they are scattered into a set of standard BAHAMAS timeseries. An example is given below.

It is clear that this process is to some degree always going to be subjective, but due to the individuality of the different RFs (unforeseeable track-deviations, dropsonde failures, complexity of maneuvers) and the managable amount of them, this methodology appears plausible.

Note on data format: It is strongly encouraged to use the provided YAML files since they best reflect the structure of the gathered meta-data. For python users, the module PyYAML (included in Anaconda) offers an easy to use module to read the data from the yaml files into plane python objects like lists and dictionaries. Here is an example to read a file and print the circle start- and endtimes from that file:

import yaml 
flightinfo = yaml.load(open("HALO_RF04_20200126_info.yaml"))
print([(c["start"], c["end"]) for c in flightinfo["segments"] if c["kind"] == "circle"])

For users still insisting on the use of NetCDF data, please contact the dataset creator.