GLORIA

GEOMAR Library Ocean Research Information Access

X

Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
  • 1
    Online Resource
    Online Resource
    Observations of Aerosol, Cloud, Turbulence, and Radiation Properties at the Top of the Marine Boundary Layer over the Eastern North Atlantic Ocean: The ACORES Campaign
    American Meteorological Society ; 2021
    In:  Bulletin of the American Meteorological Society Vol. 102, No. 1 ( 2021-01), p. E123-E147
    Details
    In: Bulletin of the American Meteorological Society, American Meteorological Society, Vol. 102, No. 1 ( 2021-01), p. E123-E147
    Abstract: We report on the Azores Stratocumulus Measurements of Radiation, Turbulence and Aerosols (ACORES) campaign, which took place around Graciosa and Pico Islands/Azores in July 2017. The main objective was to investigate the vertical distribution of aerosol particles, stratocumulus microphysical and radiative properties, and turbulence parameters in the eastern North Atlantic. The vertical exchange of mass, momentum, and energy between the free troposphere (FT) and the cloudy marine boundary layer (MBL) was explored over a range of scales from submeters to kilometers. To cover these spatial scales with appropriate measurements, helicopter-borne observations with unprecedented high resolution were realized using the Airborne Cloud Turbulence Observation System (ACTOS) and Spectral Modular Airborne Radiation Measurement System–Helicopter-Borne Observations (SMART-HELIOS) instrumental payloads. The helicopter-borne observations were combined with ground-based aerosol measurements collected at two continuously running field stations on Pico Mountain (2,225 m above sea level, in the FT), and at the Atmospheric Radiation Measurement (ARM) station on Graciosa (at sea level). First findings from the ACORES observations we are discussing in the paper are as follows: (i) we have observed a high variability of the turbulent cloud-top structure on horizontal scales below 100 m with local temperature gradients of up to 4 K over less than 1 m vertical distance, (ii) we have collected strictly collocated radiation measurements supporting the relevance of small-scale processes by revealing significant inhomogeneities in cloud-top brightness temperature to scales well below 100 m, and (iii) we have concluded that aerosol properties are completely different in the MBL and FT with often-complex stratification and frequently observed burst-like new particle formation.
    Type of Medium: Online Resource
    ISSN: 0003-0007 , 1520-0477
    URL: Article
    DOI: 10.1175/BAMS-D-19-0191.1
    Language: Unknown
    Publisher: American Meteorological Society
    Publication Date: 2021
    detail.hit.zdb_id: 2029396-3
    detail.hit.zdb_id: 419957-1
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 2
    Online Resource
    Online Resource
    Automated Identification of Characteristic Droplet Size Distributions in Stratocumulus Clouds Utilizing a Data Clustering Algorithm
    American Meteorological Society ; 2022
    In:  Artificial Intelligence for the Earth Systems Vol. 1, No. 3 ( 2022-07)
    Details
    In: Artificial Intelligence for the Earth Systems, American Meteorological Society, Vol. 1, No. 3 ( 2022-07)
    Abstract: Droplet-level interactions in clouds are often parameterized by a modified gamma fitted to a “global” droplet size distribution. Do “local” droplet size distributions of relevance to microphysical processes look like these average distributions? This paper describes an algorithm to search and classify characteristic size distributions within a cloud. The approach combines hypothesis testing, specifically, the Kolmogorov–Smirnov (KS) test, and a widely used class of machine learning algorithms for identifying clusters of samples with similar properties: density-based spatial clustering of applications with noise (DBSCAN) is used as the specific example for illustration. The two-sample KS test does not presume any specific distribution, is parameter free, and avoids biases from binning. Importantly, the number of clusters is not an input parameter of the DBSCAN-type algorithms but is independently determined in an unsupervised fashion. As implemented, it works on an abstract space from the KS test results, and hence spatial correlation is not required for a cluster. The method is explored using data obtained from the Holographic Detector for Clouds (HOLODEC) deployed during the Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA) field campaign. The algorithm identifies evidence of the existence of clusters of nearly identical local size distributions. It is found that cloud segments have as few as one and as many as seven characteristic size distributions. To validate the algorithm’s robustness, it is tested on a synthetic dataset and successfully identifies the predefined distributions at plausible noise levels. The algorithm is general and is expected to be useful in other applications, such as remote sensing of cloud and rain properties. Significance Statement A typical cloud can have billions of drops spread over tens or hundreds of kilometers in space. Keeping track of the sizes, positions, and interactions of all of these droplets is impractical, and, as such, information about the relative abundance of large and small drops is typically quantified with a “size distribution.” Droplets in a cloud interact locally, however, so this work is motivated by the question of whether the cloud droplet size distribution is different in different parts of a cloud. A new method, based on hypothesis testing and machine learning, determines how many different size distributions are contained in a given cloud. This is important because the size distribution describes processes such as cloud droplet growth and light transmission through clouds.
    Type of Medium: Online Resource
    ISSN: 2769-7525
    URL: Article
    URL: Article
    DOI: 10.1175/AIES-D-22-0003.1
    DOI: 10.1175/AIES-D-22-0003.s1
    Language: Unknown
    Publisher: American Meteorological Society
    Publication Date: 2022
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 3
    Online Resource
    Online Resource
    Performance of optical sensors for cloud measurements deployed by the ARM Aerial Facility during ACE-ENA
    Optica Publishing Group ; 2023
    In:  Applied Optics Vol. 62, No. 19 ( 2023-07-01), p. 5282-
    Details
    In: Applied Optics, Optica Publishing Group, Vol. 62, No. 19 ( 2023-07-01), p. 5282-
    Abstract: During the Aerosol and Cloud Experiment in the Eastern North Atlantic (ACE-ENA), a variety of in situ optical sensors using shadow imaging, scattering and holography were deployed by the Atmospheric Radiation Measurement (ARM) Aerial Facility to determine cloud properties. Taking advantage of the wide, overlapping range of instrumentation, we compare in situ cloud data from several different measurement methods for droplets up to 100 µm. Data processing was tailored to the encountered conditions, leading to good agreement. Improvements include noise reduction for holography and better out-of-focus correction for shadow imaging. Comparison between direct liquid water content measurements and optical sensors showed better agreement at higher droplet number concentrations ( 〉 120/cm 3 ).
    Type of Medium: Online Resource
    ISSN: 1559-128X , 2155-3165
    URL: Article
    DOI: 10.1364/AO.491401
    Language: English
    Publisher: Optica Publishing Group
    Publication Date: 2023
    detail.hit.zdb_id: 207387-0
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
Close ⊗
This website uses cookies and the analysis tool Matomo. More information can be found here...