CVI#

In-situ measurements of arctic ambient aerosol particle (outside cloud) and cloud particle residual (inside cloud) aerosol properties using the CVI inlet and PSAP, CPC and UHSAS aerosol instruments.

Each textfile contains the data obtained during a single flight. The file contains the time, location and particle siz distribution of the measurements.

The following example demonstrates the use of the CVI data collected during the ACLOUD campaign.

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.

Data access#

  • To analyse the data they first have to be loaded by importing the (AC)³airborne 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.

Get data#

import ac3airborne

List of flights, where CVI data is available:

cat = ac3airborne.get_intake_catalog()

datasets = []
for campaign in ['ACLOUD']:
    datasets.extend(list(cat[campaign]['P6']['CVI_PSAP_CPC_UHSAS']))
datasets

['ACLOUD_P6_RF07',
 'ACLOUD_P6_RF08',
 'ACLOUD_P6_RF09',
 'ACLOUD_P6_RF10',
 'ACLOUD_P6_RF11',
 'ACLOUD_P6_RF12',
 'ACLOUD_P6_RF13',
 'ACLOUD_P6_RF14',
 'ACLOUD_P6_RF15',
 'ACLOUD_P6_RF16',
 'ACLOUD_P6_RF17',
 'ACLOUD_P6_RF18',
 'ACLOUD_P6_RF19',
 'ACLOUD_P6_RF20',
 'ACLOUD_P6_RF21',
 'ACLOUD_P6_RF22',
 'ACLOUD_P6_RF23',
 'ACLOUD_P6_RF24',
 'ACLOUD_P6_RF25']

Dataset#

To get an overview of the variables recorded by the instruments, we load the dataset of the first ACLOUD flight ACLOUD_P6_RF07.

ds_cvi = cat['ACLOUD']['P6']['CVI_PSAP_CPC_UHSAS']['ACLOUD_P6_RF07'].read()
ds_cvi
Date/Time Altitude [m] (m a.s.l.) Longitude Latitude PPPP [hPa] (CVI) TTT [°C] (CVI, recalculated from K (-27...) Flag (CVI) EF (CVI) Ø [µm] (CVI lower cut-off diameter) ac [1/m] ... dN/dlogDp [#/cm**3] (768.22 nm) dN/dlogDp [#/cm**3] (791.06 nm) dN/dlogDp [#/cm**3] (814.58 nm) dN/dlogDp [#/cm**3] (838.8 nm) dN/dlogDp [#/cm**3] (863.74 nm) dN/dlogDp [#/cm**3] (889.42 nm) dN/dlogDp [#/cm**3] (915.86 nm) dN/dlogDp [#/cm**3] (943.09 nm) dN/dlogDp [#/cm**3] (971.13 nm) dN/dlogDp [#/cm**3] (1000 nm)
0 2017-05-27T13:01:01 25.0 15.50916 78.24371 1019.31 19.58 1.0 1.84 0.0 -0.000000 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
1 2017-05-27T13:01:04 25.0 15.50883 78.24372 1019.47 19.58 1.0 1.99 0.0 -0.000000 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
2 2017-05-27T13:01:07 26.0 15.50856 78.24373 1019.38 19.58 1.0 1.94 0.0 -0.000000 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
3 2017-05-27T13:01:10 26.0 15.50838 78.24374 1019.48 19.52 1.0 1.62 0.0 -0.000001 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
4 2017-05-27T13:01:13 26.0 15.50825 78.24374 1019.61 19.58 1.0 1.54 0.0 -0.000001 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
4203 2017-05-27T16:31:10 26.0 15.50161 78.24588 NaN NaN 0.0 1.00 NaN NaN ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
4204 2017-05-27T16:31:13 26.0 15.50161 78.24588 NaN NaN 0.0 1.00 NaN NaN ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
4205 2017-05-27T16:31:16 26.0 15.50161 78.24588 NaN NaN 0.0 1.00 NaN NaN ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
4206 2017-05-27T16:31:19 26.0 15.50161 78.24588 NaN NaN 0.0 1.00 NaN NaN ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
4207 2017-05-27T16:31:22 26.0 15.50161 78.24588 NaN NaN 0.0 1.00 NaN NaN ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

4208 rows × 108 columns

View variables#