Ctrl+K

CO, CO2, H2O, O3 sensors

CO, CO2, H2O, O3 sensors#

In-situ measurements of trace gas species such as Carbon monoxide (CO), carbon dioxide (CO2) and water vapor (H2O) and ozone (O3).

Each textfile contains the data obtained during a single flight. The file contains the time, location and mixing ratios for CO, CO2, H2O and O3.

Please note, There are no CO data available for ACLOUD flight #23 because of an instrumental failure. For ACLOUD flight #14, there are no O3 data available after 12:58:37 UTC.

The following example demonstrates the use of the trace gas mixing ratios 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.

import os
from dotenv import load_dotenv

load_dotenv()

ac3cloud_username = os.environ['AC3_USER']
ac3cloud_password = os.environ['AC3_PASSWORD']

credentials = dict(user=ac3cloud_username, password=ac3cloud_password)

# local caching
kwds = {'simplecache': dict(
    cache_storage=os.environ['INTAKE_CACHE'], 
    same_names=True
)}

---------------------------------------------------------------------------
ModuleNotFoundError                       Traceback (most recent call last)
Cell In[1], line 2
      1 import os
----> 2 from dotenv import load_dotenv
      4 load_dotenv()
      6 ac3cloud_username = os.environ['AC3_USER']

ModuleNotFoundError: No module named 'dotenv'

Get data#

import ac3airborne

List of flights, where OPC data is available:

cat = ac3airborne.get_intake_catalog()

datasets = []
for campaign in ['ACLOUD']:
    datasets.extend(list(cat[campaign]['P6']['CO_CO2_H2O']))
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 trace gas sensors, we load the dataset of the first ACLOUD flight ACLOUD_P6_RF07.

ds_co_co2 = cat['ACLOUD']['P6']['CO_CO2_H2O']['ACLOUD_P6_RF07'].read()
ds_co_co2

/usr/lib/python3/dist-packages/dask/dataframe/utils.py:14: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.
  import pandas.util.testing as tm
ID Date/Time Altitude [m] Latitude Longitude CO mix ratio [ppbv] CO2 mix ratio [ppmv] WV mix ratio [ppmv] O3 mix ratio [ppbv]
0 P6_07 2017-05-27T13:01:59 31.0 78.2458 15.4716 108.92 413.86 2983.6 NaN
1 P6_07 2017-05-27T13:02:00 32.0 78.2459 15.4696 108.22 413.83 2921.0 22.28
2 P6_07 2017-05-27T13:02:01 34.0 78.2460 15.4676 107.44 413.82 2864.0 NaN
3 P6_07 2017-05-27T13:02:02 36.0 78.2461 15.4656 106.80 413.83 2901.5 20.71
4 P6_07 2017-05-27T13:02:03 39.0 78.2462 15.4636 107.95 413.90 2923.6 NaN
... ... ... ... ... ... ... ... ... ...
12294 P6_07 2017-05-27T16:26:53 29.0 78.2477 15.4373 109.24 413.71 2910.0 NaN
12295 P6_07 2017-05-27T16:26:54 29.0 78.2476 15.4392 108.98 413.63 2904.0 19.52
12296 P6_07 2017-05-27T16:26:55 29.0 78.2475 15.4411 107.97 413.61 2897.2 NaN
12297 P6_07 2017-05-27T16:26:56 30.0 78.2474 15.4430 107.99 413.69 2900.1 19.23
12298 P6_07 2017-05-27T16:26:57 30.0 78.2473 15.4448 109.15 413.72 2960.1 NaN

12299 rows × 9 columns

View variables#

Contents