Characteristics of the Springtime Alpine Valley Atmospheric Boundary Layer Using Self-Organizing Maps

Marwan Katurji Centre for Atmospheric Research, University of Canterbury, Christchurch, New Zealand

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Bob Noonan Centre for Atmospheric Research, University of Canterbury, Christchurch, New Zealand

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Peyman Zawar-Reza Centre for Atmospheric Research, University of Canterbury, Christchurch, New Zealand

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Tobias Schulmann Centre for Atmospheric Research, University of Canterbury, Christchurch, New Zealand

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Andrew Sturman Centre for Atmospheric Research, University of Canterbury, Christchurch, New Zealand

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Abstract

Vertical profiles of wind velocity and air temperature from a sound detection and ranging (sodar) radio acoustic sounding system (RASS)-derived dataset within an alpine valley of the New Zealand Southern Alps were analyzed. The data covered the month of September 2013, and self-organizing maps (SOM; a data-clustering approach that is based on an unsupervised machine-learning algorithm) are used to detect topological relationships between profiles. The results of the SOM were shown to reflect the physical processes within the valley boundary layer by preserving valley boundary layer dynamics and its response to wind shear. By examining the temporal evolution of ridgetop wind speed and direction and SOM node transitions, the sensitivity of the valley boundary layer to ridgetop weather conditions was highlighted. The approach of using a composite variable (wind speed and potential temperature) with SOM was successful in revealing the coupling of dynamics and atmospheric stability. The results reveal the capabilities of SOM in analyzing large datasets of atmospheric boundary layer measurements and elucidating the connectivity of ridgetop wind speeds and valley boundary layers.

Corresponding author address: Marwan Katurji, University of Canterbury Center for Atmospheric Research, 4th Floor Geography Building, Ilam, Christchurch 8041, New Zealand. E-mail: marwan.katurji@canterbury.ac.nz

Abstract

Vertical profiles of wind velocity and air temperature from a sound detection and ranging (sodar) radio acoustic sounding system (RASS)-derived dataset within an alpine valley of the New Zealand Southern Alps were analyzed. The data covered the month of September 2013, and self-organizing maps (SOM; a data-clustering approach that is based on an unsupervised machine-learning algorithm) are used to detect topological relationships between profiles. The results of the SOM were shown to reflect the physical processes within the valley boundary layer by preserving valley boundary layer dynamics and its response to wind shear. By examining the temporal evolution of ridgetop wind speed and direction and SOM node transitions, the sensitivity of the valley boundary layer to ridgetop weather conditions was highlighted. The approach of using a composite variable (wind speed and potential temperature) with SOM was successful in revealing the coupling of dynamics and atmospheric stability. The results reveal the capabilities of SOM in analyzing large datasets of atmospheric boundary layer measurements and elucidating the connectivity of ridgetop wind speeds and valley boundary layers.

Corresponding author address: Marwan Katurji, University of Canterbury Center for Atmospheric Research, 4th Floor Geography Building, Ilam, Christchurch 8041, New Zealand. E-mail: marwan.katurji@canterbury.ac.nz
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