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ATom: Development of Cloud Indicator Algorithm Using Airborne Observations from CAPS

Overview

DOIhttps://doi.org/10.3334/ORNLDAAC/2250
Version1
Project
Published2024-09-12
Usage25 downloads

Description

This dataset is the basis for the development of the Cloud Indicator, a novel algorithm that automatically detects and classifies measurement periods inside clouds. The included data were used in the analysis and development of figures for the related publication. The Cloud Indicator algorithm was developed based on particle size distribution measurements from a second-generation Cloud, Aerosol, and Precipitation Spectrometer (CAPS) combined with measurements of relative humidity and temperature from other sensors, to automatically detect flight sequences in clouds and classify the cloud type. Measurements were collected on 2016-08-20 as part of the Atmospheric Tomography Mission (ATom-1) Campaign and on 2017-04-20 as part of the Absorbing aerosol layers in a changing climate: aging, LIFEtime and dynamics (A-LIFE) project. As an additional criterion for the Cloud Indicator, a cloud-aerosol volume factor was established to ensure a precise and robust distinction between clouds and aerosol layers such as mineral dust or biomass burning to reduce misclassifications. Data are provided in netCDF (*.nc) format.

Science Keywords

  • CLIMATE INDICATORS
  • ATMOSPHERIC/OCEAN INDICATORS
  • CLOUD INDICATORS

Data Use and Citation

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Crosscite Citation Formatter
Dollner, M., J. Gasteiger, M. Schoeberl, A. Gattringer, N.D. Beres, T.P. Bui, G.S. Diskin, and B.B. Weinzierl. 2023. ATom: Development of Cloud Indicator Algorithm Using Airborne Observations from CAPS. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/2250

This dataset is openly shared, without restriction, in accordance with the EOSDIS Data Use Policy. See our Data Use and Citation Policy for more information.

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Companion Files

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Dataset has 1 companion files.

  • ATom_Clouds_Aerosols.pdf