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2D-S

2D-S#

In-situ measurements of arctic cloud particle size distribution and volumic cloud particle diffusion properties using the 2D stereoscopic (2D-S, SPEC Inc.) and Polar Nephelometer (Gayet et al., 1997) probes.

Each NetCDF file contains the data obtained during a single flight. The file contains the time, location and XXX of the measurements.

The following example demonstrates the use of the 2D-S data collected during the MOSAIC-ACA and AFLUX 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 2D-S is available:

cat = ac3airborne.get_intake_catalog()

datasets = []
for campaign in ['AFLUX','MOSAiC-ACA']:
    datasets.extend(list(cat[campaign]['P5']['2D_SPEC']))
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 the 2D-S, we load the dataset of the first AFLUX flight AFLUX_P5_RF02.

ds_2ds = cat['AFLUX']['P5']['2D_SPEC']['AFLUX_P5_RF05'].to_dask()
ds_2ds

<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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