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Rasool Porhemmat, Heather Purdie, Peyman Zawar-Reza, Christian Zammit, and Tim Kerr

Abstract

Synoptic-scale moisture transport during large snowfall events in the New Zealand Southern Alps is largely unknown due to a lack of long-term snow observations. In this study, records from three recently developed automatic weather stations (Mahanga, Mueller Hut, and Mt Larkins) near the Main Divide of the Southern Alps were used to identify large snowfall events between 2010 and 2018. The large snowfall events are defined as those events with daily snow depth increase by greater than the 90th percentile at each site. ERA-Interim reanalysis data were used to characterize the hydrometeorological features of the selected events. Our findings show that large snowfall events in the Southern Alps generally coincide with strong fields of integrated vapor transport (IVT) within a northwesterly airflow and concomitant deepening low pressure systems. Considering the frequency of large snowfall events, approximately 61% of such events at Mahanga were associated with narrow corridors of strong water vapor flux, known as atmospheric rivers (ARs). The contributions of ARs to the large snowfall events at Mueller Hut and Mt Larkins were 70% and 71%, respectively. Analysis of the vertical profiles of moisture transport dynamics during the passage of a landfalling AR during 11–12 October 2016 revealed the key characteristics of a snow-generating AR in the Southern Alps. An enhanced presence of low- and midlevel moisture between 700 and 850 hPa and pronounced increases of wind velocities (more than 30 m s−1) with high values of the meridional component between 750 and 850 hPa were identified over the Southern Alps during the event.

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Marwan Katurji, Bob Noonan, Peyman Zawar-Reza, Tobias Schulmann, and Andrew Sturman

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.

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Peyman Zawar-Reza, Marwan Katurji, Iman Soltanzadeh, Tanja Dallafior, Shiyuan Zhong, Daniel Steinhoff, Bryan Storey, and S. Craig Cary

Abstract

Measuring routine vertical profiles of atmospheric temperature is critical in understanding stability and the dynamics of the boundary layer. Routine monitoring in remote areas such as the McMurdo Dry Valleys (MDV) of Antarctica is logistically difficult and expensive. Pseudovertical profiles that were derived from a network of inexpensive ground temperature sensors planted on valley sidewalls (up to 330 m above valley floor), together with data from a weather station and a numerical weather prediction model, provided a long-term climatological description of the evolution of the winter boundary layer over the MDV. In winter, persistent valley cold pools (VCPs) were common, lasting up to 2 weeks. The VCPs were eroded by warm-air advection from aloft associated with strong winds, increasing the temperature of the valley by as much as 25 K. Pseudovertical datasets as described here can be used for model validation.

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Ben Jolly, Adrian J. McDonald, Jack H. J. Coggins, Peyman Zawar-Reza, John Cassano, Matthew Lazzara, Geoffery Graham, Graeme Plank, Orlon Petterson, and Ethan Dale

Abstract

This study compares high-resolution output (1.1-km horizontal grid length) from twice-daily forecasts produced by the Antarctic Mesoscale Prediction System (AMPS) with a dense observational network east of Ross Island. Covering 10 000 km2, 15 SNOWWEB stations significantly increased the number of observation stations in the area to 19 during the 2014–15 austral summer. Collocated “virtual stations” created from AMPS output are combined with observations, producing a single dataset of zonal and meridional wind components used to train a self-organizing map (SOM). The resulting SOM is used to individually classify the observational and AMPS datasets, producing a time series of classifications for each dataset directly comparable to the other. Analysis of class composites shows two dominant weather patterns: low but directionally variable winds and high but directionally constant winds linked to the Ross Ice Shelf airstream (RAS). During RAS events the AMPS and SNOWWEB data displayed good temporal class alignment with good surface wind correlation. SOM analysis shows that AMPS did not accurately forecast surface-level wind speed or direction during light wind conditions where synoptic forcing was weak; however, it was able to forecast the low wind period occurrence accurately. Coggins’s regimes provide synoptic-scale context and show a reduced synoptic pressure gradient during these classes, increasing reliance on the ability of Polar WRF to resolve mesoscale dynamics. Available initialization data have insufficient resolution for the region’s complex topography, which likely impacts performance. The SOM analysis methods used are shown to be effective for model validation and are widely applicable.

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