GLORIA

GEOMAR Library Ocean Research Information Access

Your email was sent successfully. Check your inbox.

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

Proceed reservation?

Export
  • 1
    In: Bulletin of the American Meteorological Society, American Meteorological Society, Vol. 102, No. 2 ( 2021-02), p. E421-E445
    Abstract: The Chequamegon Heterogeneous Ecosystem Energy-Balance Study Enabled by a High-Density Extensive Array of Detectors 2019 (CHEESEHEAD19) is an ongoing National Science Foundation project based on an intensive field campaign that occurred from June to October 2019. The purpose of the study is to examine how the atmospheric boundary layer (ABL) responds to spatial heterogeneity in surface energy fluxes. One of the main objectives is to test whether lack of energy balance closure measured by eddy covariance (EC) towers is related to mesoscale atmospheric processes. Finally, the project evaluates data-driven methods for scaling surface energy fluxes, with the aim to improve model–data comparison and integration. To address these questions, an extensive suite of ground, tower, profiling, and airborne instrumentation was deployed over a 10 km × 10 km domain of a heterogeneous forest ecosystem in the Chequamegon–Nicolet National Forest in northern Wisconsin, United States, centered on an existing 447-m tower that anchors an AmeriFlux/NOAA supersite (US-PFa/WLEF). The project deployed one of the world’s highest-density networks of above-canopy EC measurements of surface energy fluxes. This tower EC network was coupled with spatial measurements of EC fluxes from aircraft; maps of leaf and canopy properties derived from airborne spectroscopy, ground-based measurements of plant productivity, phenology, and physiology; and atmospheric profiles of wind, water vapor, and temperature using radar, sodar, lidar, microwave radiometers, infrared interferometers, and radiosondes. These observations are being used with large-eddy simulation and scaling experiments to better understand submesoscale processes and improve formulations of subgrid-scale processes in numerical weather and climate models.
    Type of Medium: Online Resource
    ISSN: 0003-0007 , 1520-0477
    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 ...
  • 2
    Online Resource
    Online Resource
    American Meteorological Society ; 2021
    In:  Journal of Applied Meteorology and Climatology Vol. 60, No. 4 ( 2021-04), p. 477-491
    In: Journal of Applied Meteorology and Climatology, American Meteorological Society, Vol. 60, No. 4 ( 2021-04), p. 477-491
    Abstract: Various methods have been developed to characterize cloud type, otherwise referred to as cloud regime. These include manual sky observations, combining radiative and cloud vertical properties observed from satellite, surface-based remote sensing, and digital processing of sky imagers. While each method has inherent advantages and disadvantages, none of these cloud-typing methods actually includes measurements of surface shortwave or longwave radiative fluxes. Here, a method that relies upon detailed, surface-based radiation and cloud measurements and derived data products to train a random-forest machine-learning cloud classification model is introduced. Measurements from five years of data from the ARM Southern Great Plains site were compiled to train and independently evaluate the model classification performance. A cloud-type accuracy of approximately 80% using the random-forest classifier reveals that the model is well suited to predict climatological cloud properties. Furthermore, an analysis of the cloud-type misclassifications is performed. While physical cloud types may be misreported, the shortwave radiative signatures are similar between misclassified cloud types. From this, we assert that the cloud-regime model has the capacity to successfully differentiate clouds with comparable cloud–radiative interactions. Therefore, we conclude that the model can provide useful cloud-property information for fundamental cloud studies, inform renewable energy studies, and be a tool for numerical model evaluation and parameterization improvement, among many other applications.
    Type of Medium: Online Resource
    ISSN: 1558-8424 , 1558-8432
    RVK:
    Language: Unknown
    Publisher: American Meteorological Society
    Publication Date: 2021
    detail.hit.zdb_id: 2227779-1
    detail.hit.zdb_id: 2227759-6
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
  • 3
    Online Resource
    Online Resource
    American Geophysical Union (AGU) ; 2023
    In:  Journal of Geophysical Research: Atmospheres Vol. 128, No. 6 ( 2023-03-27)
    In: Journal of Geophysical Research: Atmospheres, American Geophysical Union (AGU), Vol. 128, No. 6 ( 2023-03-27)
    Abstract: Decorrelation length scales varied by cloud regime at the Atmospheric Radiation Measurement Program Southern Great Plains site between 0.04 and 4.58 km Decorrelation length scales varied by site with values ranging from near 1 km in the Arctic to near 3 km in Brazil Decorrelation length scales for the same cloud regime across the sites were similar, although some cloud regimes exhibited differences
    Type of Medium: Online Resource
    ISSN: 2169-897X , 2169-8996
    Language: English
    Publisher: American Geophysical Union (AGU)
    Publication Date: 2023
    detail.hit.zdb_id: 710256-2
    detail.hit.zdb_id: 2016800-7
    detail.hit.zdb_id: 2969341-X
    SSG: 16,13
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
Close ⊗
This website uses cookies and the analysis tool Matomo. More information can be found here...