Flight plan - KA-20240831a

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Flight plan - KA-20240831a#

ec_under ec_track in_cloud insitu_aerosol

Crew#

The flight is planned to take off at 2024-08-31 14:00:00+00:00.

Job

Name

PI

Robert Oscar David

Instrument operator

Sorin Ghemulet

Instrument operator

Marius Corbu

Flight plan#

Leg 1: RAI -> ka_south (arrive @FL065 at 14:45 UTC | 13:45 LT) - Note: Holding pattern until 15:07 UTC

Leg 2: ka_south -> ec2 (cloud top, ~FL065-FL030), 180 kt - Note: Communication with ATR (F-HMTO) via 123.45 MHz to coordinate start of common leg and cloud information

Leg 3 (Start 15:25 UTC): ec2 -> ec1 (via mp1) - porpoising between cloud base and cloud top - 180 kt - Note: Be at mp1 at 15:37 UTC | 14:37 LT for satellite overpass

Leg 4: ec1 -> ka_north (to de-conflict with F-HMTO)

Leg 5: ka_north -> ec1_west (coordinate with F-HMTO when arrival at ec1_west)

Leg 6: ec1_west -> ec2_west (via mp1_west) - estimated FL055 (1000 ft below F-HMTO to de-conflict)

Leg 7: ec2_west -> ka_south (to de-conflict with F-HMTO)

Leg 8: ka_south -> ec2_east (coordinate with F-HMTO when arrival at ec2_east)

Leg 9: ec2_east -> ec1_east (via mp1_east) - estimated FL055 (1000 ft above F-HMTO to de-conflict)

Leg 10: ec1_east -> RAI (de-conflict with F-HMTO for corridor out)

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from orcestra.flightplan import sal, bco, LatLon, IntoCircle, path_preview, plot_cwv
from datetime import datetime
import intake
import easygems.healpix as egh


# Define dates for forecast initialization and flight

issued_time = datetime(2024, 8, 30, 0, 0, 0)
issued_time_str = issued_time.strftime('%Y-%m-%d')

flight_time = datetime(2024, 8, 31, 12, 0, 0)
flight_time_str = flight_time.strftime('%Y-%m-%d')
flight_index = f"KA-{flight_time.strftime('%Y%m%d')}a"

print("Initalization date of IFS forecast: " + issued_time_str + "\nFlight date: " + flight_time_str + "\nFlight index: " + flight_index)

airport = LatLon(lat=14.945, lon=-23.4863889, label='RAI')

ka_south = LatLon(lat=13.813437, lon=-22.586137, label='ka_south')
ec2 = LatLon(lat=14.305833, lon=-22.49, label='ec2')
mp1 = LatLon(lat=14.868333, lon=-22.380833, label='mp1')
ec1 = LatLon(lat=15.435833, lon=-22.27, label='ec1')
ka_north = LatLon(lat=15.95, lon=-22.28, label='ka_north')
ec1_west = LatLon(lat=15.462, lon=-22.37, label='ec1_west')
mp1_west = LatLon(lat=14.8945, lon=-22.480833, label='mp1_west')
ec2_west = LatLon(lat=14.332, lon=-22.59, label='ec2_west')
ec2_east = LatLon(lat=14.279667, lon=-22.39, label='ec2_east')
mp1_east = LatLon(lat=14.842167, lon=-22.280833, label='mp1_east')
ec1_east = LatLon(lat=15.409667, lon=-22.17, label='ec1_east')


leg_out = [
     airport,
     ka_south
]

leg_calval = [
      ka_south,
      ec2,
      mp1,
      ec1
]

leg_transfer = [
      ec1,
      ka_north
]

leg_atr_val_south = [
     ka_north,
     ec1_west,
     mp1_west,
     ec2_west
]

leg_transfer2 = [
      ec2_west,
      ka_south
]

leg_atr_val_north = [
     ka_south,
     ec2_east,
     mp1_east,
     ec1_east
]

leg_home = [
     ec1_east,
     airport
]

path = leg_out + leg_calval + leg_transfer + leg_atr_val_south + leg_transfer2 + leg_atr_val_north + leg_home 

cat = intake.open_catalog("https://tcodata.mpimet.mpg.de/internal.yaml")
ds = cat.HIFS(datetime=issued_time).to_dask().pipe(egh.attach_coords)
cwv_flight_time = ds["tcwv"].sel(time=flight_time, method = "nearest")


ax = path_preview(path)

#ax.scatter(-24.995374, 16.877772, c='red', marker='o', s=200, ec = 'r', alpha = 0.3)

plot_cwv(cwv_flight_time)

Initalization date of IFS forecast: 2024-08-30
Flight date: 2024-08-31
Flight index: KA-20240831a
../_images/ff2607e57a2a8a1c20c46278dc9f783c62fd2b82deaeb832bdbb29c55aa9627e.png

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import pandas as pd
from dataclasses import asdict

pd.DataFrame.from_records(map(asdict, [airport, ka_south, ec2, mp1, ec1, ka_north, ec1_west, mp1_west, ec2_west, ka_south, ec2_east, mp1_east, ec1_east, airport])).set_index("label")
lat lon fl time note
label
RAI 14.945000 -23.486389 None None None
ka_south 13.813437 -22.586137 None None None
ec2 14.305833 -22.490000 None None None
mp1 14.868333 -22.380833 None None None
ec1 15.435833 -22.270000 None None None
ka_north 15.950000 -22.280000 None None None
ec1_west 15.462000 -22.370000 None None None
mp1_west 14.894500 -22.480833 None None None
ec2_west 14.332000 -22.590000 None None None
ka_south 13.813437 -22.586137 None None None
ec2_east 14.279667 -22.390000 None None None
mp1_east 14.842167 -22.280833 None None None
ec1_east 15.409667 -22.170000 None None None
RAI 14.945000 -23.486389 None None None
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