AMALi cloud top height

This example presents the cloud top height derived from Airborne Mobile Aerosol Lidar for Arctic research (AMALi) measurements. The dataset is available on the PANGAEA database.

If you have questions or if you would like to use the data for a publication, please don’t hesitate to get in contact with the dataset authors as stated in the dataset attributes contact or author.

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.

import ac3airborne

cat = ac3airborne.get_intake_catalog()

datasets = []
for campaign in ['ACLOUD', 'AFLUX', 'MOSAiC-ACA']:
    datasets.extend(list(cat[campaign]['P5']['CLOUD_TOP_HEIGHT']))
datasets

['ACLOUD_P5_RF04',
 'ACLOUD_P5_RF05',
 'ACLOUD_P5_RF06',
 'ACLOUD_P5_RF07',
 'ACLOUD_P5_RF08',
 'ACLOUD_P5_RF11',
 'ACLOUD_P5_RF13',
 'ACLOUD_P5_RF14',
 'ACLOUD_P5_RF16',
 'ACLOUD_P5_RF17',
 'ACLOUD_P5_RF18',
 'ACLOUD_P5_RF20',
 'ACLOUD_P5_RF21',
 'ACLOUD_P5_RF23',
 'AFLUX_P5_RF03',
 'AFLUX_P5_RF04',
 'AFLUX_P5_RF05',
 'AFLUX_P5_RF06',
 'AFLUX_P5_RF07',
 'AFLUX_P5_RF08',
 'AFLUX_P5_RF09',
 'AFLUX_P5_RF10',
 'AFLUX_P5_RF11',
 'AFLUX_P5_RF12',
 'AFLUX_P5_RF13',
 'AFLUX_P5_RF14',
 'MOSAiC-ACA_P5_RF05',
 'MOSAiC-ACA_P5_RF06',
 'MOSAiC-ACA_P5_RF07',
 'MOSAiC-ACA_P5_RF08',
 'MOSAiC-ACA_P5_RF10']

Note

Have a look at the attributes of the xarray dataset ds_cloud_top_height for all relevant information on the dataset, such as author, contact, or citation information.

ds_cloud_top_height = cat['ACLOUD']['P5']['CLOUD_TOP_HEIGHT']['ACLOUD_P5_RF07'].to_dask()
ds_cloud_top_height

<xarray.Dataset>
Dimensions:            (time: 11895, cloud_layer: 10)
Coordinates:
  * time               (time) datetime64[ns] 2017-05-27T13:05:00 ... 2017-05-...
  * cloud_layer        (cloud_layer) int8 1 2 3 4 5 6 7 8 9 10
Data variables:
    n_cloud_layer      (time) float64 nan nan nan nan nan ... nan nan nan nan
    cloud_top_height   (time, cloud_layer) float64 ...
    cloud_mask         (time) float64 nan nan nan nan nan ... nan nan nan nan
    instrument_status  (time) int8 0 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
    alt                (time) float64 30.66 30.91 31.05 ... 32.21 30.39 28.8
    lat                (time) float64 78.24 78.25 78.25 ... 78.25 78.25 78.25
    lon                (time) float64 15.49 15.49 15.48 ... 15.42 15.42 15.42
Attributes:
    description:  Cloud top height along flight path based on AMALi observations
    contact:      birte.kulla@uni-koeln.de, mario.mech@uni-koeln.de
    institution:  Alfred Wegener Institute - Research Unit Potsdam, Universit...
    instruments:  AMALi
    version:      v0.1
    author:       Birte Kulla birte.kulla@uni-koeln.de
    title:        Cloud top height along flight path

xarray.Dataset

• Dimensions:
  • time: 11895
  • cloud_layer: 10
• Coordinates: (2)
  • time
    (time)
    datetime64[ns]
    2017-05-27T13:05:00 ... 2017-05-...

    standard_name :

    time

    long_name :

    time in seconds since epoch

    axis :

    T

    array(['2017-05-27T13:05:00.000000000', '2017-05-27T13:05:01.000000000',
           '2017-05-27T13:05:02.000000000', ..., '2017-05-27T16:23:12.000000000',
           '2017-05-27T16:23:13.000000000', '2017-05-27T16:23:14.000000000'],
          dtype='datetime64[ns]')

  • cloud_layer
    (cloud_layer)
    int8
    1 2 3 4 5 6 7 8 9 10

    standard_name :

    cloud_layer

    long_name :

    cloud layer

    description :

    cloud layer counted from instrument towards surface (1 = highest cloud layer)

    array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10], dtype=int8)

• Data variables: (7)
  • n_cloud_layer
    (time)
    float64
    ...

    standard_name :

    n_cloud_layer

    long_name :

    number of cloud layers

    description :

    number of cloud layers (> 1 for multilayer clouds)

    array([nan, nan, nan, ..., nan, nan, nan])

  • cloud_top_height
    (time, cloud_layer)
    float64
    ...

    standard_name :

    cloud_top_height

    long_name :

    cloud top height

    units :

    m

    description :

    cloud top height derived from AMALi instrument for clouds within thick_cloud class

    comment :

    cloud top most likely from liquid layer

    [118950 values with dtype=float64]

  • cloud_mask
    (time)
    float64
    ...

    flag_masks :

    1, 2, 3

    flag_meanings :

    no_cloud thin_cloud_or_haze thick_cloud

    description :

    no_cloud: optical depth of column below 0.3, thin_cloud_or_haze: optical depth of column between 0.3 and 1 (uncertainty +/- 100 %), thick_cloud: optical depth greater than 1

    array([nan, nan, nan, ..., nan, nan, nan])

  • instrument_status
    (time)
    int8
    ...

    flag_masks :

    0, 1

    flag_meanings :

    off on

    description :

    Status of AMALi instrument

    array([0, 0, 0, ..., 0, 0, 0], dtype=int8)

  • alt
    (time)
    float64
    ...

    standard_name :

    height_above_reference_ellipsoid

    long_name :

    WGS84 datum/elliptical height

    positive :

    up

    units :

    m

    axis :

    Z

    array([30.66, 30.91, 31.05, ..., 32.21, 30.39, 28.8 ])

  • lat
    (time)
    float64
    ...

    standard_name :

    latitude

    long_name :

    WGS84 datum/latitude

    units :

    degrees_north

    axis :

    Y

    array([78.244898, 78.245002, 78.245108, ..., 78.248737, 78.24863 , 78.248525])

  • lon
    (time)
    float64
    ...

    standard_name :

    longitude

    long_name :

    WGS84 datum/longitude

    units :

    degrees_east

    axis :

    X

    array([15.487843, 15.485962, 15.484032, ..., 15.419302, 15.421148, 15.42298 ])

• Attributes: (7)

  description :

  Cloud top height along flight path based on AMALi observations

  contact :

  birte.kulla@uni-koeln.de, mario.mech@uni-koeln.de

  institution :

  Alfred Wegener Institute - Research Unit Potsdam, University of Cologne

  instruments :

  AMALi

  version :

  v0.1

  author :

  Birte Kulla birte.kulla@uni-koeln.de

  title :

  Cloud top height along flight path

The dataset includes the cloud top height (cloud_top_height), the number of cloud layers (n_cloud_layer) and a cloud mask derived from the optical depth (cloud_mask). Additionally, the instrument status is provided and positional data of the aircraft (lat, lon, alt).

Load Polar 5 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)³airborne

meta = ac3airborne.get_flight_segments() 

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 high-level segment during ACLOUD RF07

seg = segments["ACLOUD_P5_RF07_hl03"]

Using the start and end times of the segment ACLOUD_P5_RF07_hl03 stored in seg, we slice the MiRAC data to this flight section.

ds_cloud_top_height_sel = ds_cloud_top_height.sel(time=slice(seg["start"], seg["end"]))

Plots

The flight section during ACLOUD RF05 is flown at about 3 km altitude in west-east direction during a cold-air outbreak event perpendicular to the wind field. Clearly one can identify the roll-cloud structure in the radar reflectivity and the 89 GHz brightness temperature.

%matplotlib inline
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
plt.style.use("../../mplstyle/book")

fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True, gridspec_kw=dict(height_ratios=[1, 0.25]))

# 1st: plot flight altitude and cloud top height with seperate colors for each layer
ax1.plot(ds_cloud_top_height_sel.time, ds_cloud_top_height_sel.alt*1e-3, color='k', label='Flight altitude')

stack = ds_cloud_top_height_sel.cloud_top_height.stack({'tl': ['time', 'cloud_layer']})
im = ax1.scatter(x=stack.time, y=stack*1e-3, c=stack.cloud_layer, s=2, vmin=1, vmax=9, cmap='Set1')
fig.colorbar(im, ax=ax1, label='cloud layer (1 = highest cloud top)')
ax1.set_ylim(0, 4)
ax1.set_ylabel('Cloud top height [km]')

ax1.legend(frameon=False, loc='upper left')

# 3rd: plot cloud mask in lower part of the figure
ax2.scatter(ds_cloud_top_height_sel.time, ds_cloud_top_height_sel.cloud_mask, s=2, color='k')
ax2.set_yticks([int(x) for x in ds_cloud_top_height_sel.cloud_mask.attrs['flag_masks'].split(', ')])
ax2.set_yticklabels([x for x in ds_cloud_top_height_sel.cloud_mask.attrs['flag_meanings'].split(' ')])
ax2.set_xlabel('Time (hh:mm) [UTC]')

ax2.xaxis.set_major_formatter(mdates.DateFormatter('%H:%M'))

plt.show()