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OPC

OPC#

In-situ measurements of the particle number concentration and size distribution of ambient aerosol particles and cloud residual particles using an optical particle counter (OPC).

Each textfile contains the data obtained during a single flight. The file contains the time, location and size distribution between 0.25 µm and 40 µm, two flags indicating the used inlet system and a flag for cloud residual sampling.

The following example demonstrates the use of the OPC 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.

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']['OPC']))
datasets

['ACLOUD_P5_RF05',
 'ACLOUD_P5_RF06',
 'ACLOUD_P5_RF07',
 'ACLOUD_P5_RF10',
 'ACLOUD_P5_RF11',
 'ACLOUD_P5_RF13',
 'ACLOUD_P5_RF14',
 'ACLOUD_P5_RF16',
 'ACLOUD_P5_RF17',
 'ACLOUD_P5_RF18',
 'ACLOUD_P5_RF19',
 'ACLOUD_P5_RF20',
 'ACLOUD_P5_RF21',
 'ACLOUD_P5_RF22',
 'ACLOUD_P5_RF23',
 'AFLUX_P5_RF02',
 'AFLUX_P5_RF04',
 'AFLUX_P5_RF05',
 'AFLUX_P5_RF06',
 'AFLUX_P5_RF07',
 'AFLUX_P5_RF08',
 'AFLUX_P5_RF09',
 'AFLUX_P5_RF10',
 'AFLUX_P5_RF15',
 'MOSAiC-ACA_P5_RF05',
 'MOSAiC-ACA_P5_RF06',
 'MOSAiC-ACA_P5_RF07',
 'MOSAiC-ACA_P5_RF08',
 'MOSAiC-ACA_P5_RF09',
 'MOSAiC-ACA_P5_RF10',
 'MOSAiC-ACA_P5_RF11']

Dataset#

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

ds_opc = cat['ACLOUD']['P6']['OPC']['ACLOUD_P6_RF07'].read()
ds_opc

<xarray.Dataset>
Dimensions:     (z: 529)
Dimensions without coordinates: z
Data variables:
    GPS_Alt     (z) float32 2.725e+03 2.723e+03 2.72e+03 ... 20.91 16.24 8.88
    Baro_Alt    (z) float32 2.853e+03 2.846e+03 2.838e+03 ... 12.64 7.71 0.0
    Time        (z) float32 9.153 9.153 9.153 9.154 ... 9.227 9.227 9.227 9.227
    Lat         (z) float32 79.68 79.68 79.68 79.68 ... 79.66 79.66 79.66 79.66
    Lon         (z) float32 8.531 8.531 8.531 8.531 ... 8.513 8.513 8.513 8.513
    Pressure    (z) float32 700.9 701.6 702.2 ... 1.017e+03 1.018e+03 1.019e+03
    Temp        (z) float32 nan nan nan nan nan ... -9.76 -9.67 -9.59 -9.54
    Temp_recon  (z) float32 nan nan nan nan nan ... -9.55 -9.48 -9.41 -9.32 -9.2
    RHum        (z) float32 nan nan nan nan nan ... 83.59 83.55 83.63 83.67
    RHum_recon  (z) float32 nan nan nan nan nan ... 81.89 81.38 81.14 80.85
    Wind_vel    (z) float32 nan nan nan nan nan nan ... 8.51 8.41 8.26 8.11 nan
    Wind_dir    (z) float32 nan nan nan nan nan nan ... 7.55 8.11 8.28 8.48 nan
Attributes:
    Dropsonde_number_of_flight:  1
    Launch_Time_UTC:             09:09:10
    Sonde_ID:                    162715269/RS904

View variables#

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