The Community Foehn Classification Experiment

Mayr, Georg J., Plavcan, David, Armi, Laurence, Elvidge, Andrew, Grisogono, Branko, Horvath, Kristian, Jackson, Peter, Neururer, Alfred, Seibert, Petra, Steenburgh, James W, Stiperski, Ivana, Sturman, Andrew, Večenaj, Željko, Vergeiner, Johannes, Vosper, Simon and Zängl, Günther (2018) The Community Foehn Classification Experiment. Bulletin of the American Meteorological Society, 99 (11). pp. 2229-2236. ISSN 0003-0007

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Abstract

Strong winds crossing elevated terrain and descending to its lee occur over mountainous areas worldwide. Winds fulfilling these two criteria are called “foehn” in this paper although different names exist depending on region, sign of temperature change at onset, and depth of overflowing layer. They affect local weather and climate and impact society. Classification is difficult because other wind systems might be superimposed on them or share some characteristics. Additionally, no unanimously agreed-upon name, definition nor indications for such winds exist. The most trusted classifications have been performed by human experts. A classification experiment for different foehn locations in the Alps and different classifier groups addressed hitherto unanswered questions about the uncertainty of these classifications, their reproducibility and dependence on the level of expertise. One group consisted of mountain meteorology experts, the other two of Masters degree students who had taken mountain meteorology courses, and a further two of objective algorithms. Sixty periods of 48 hours were classified for foehn/no foehn at five Alpine foehn locations. The intra-human-classifier detection varies by about 10 percentage points (interquartile range). Experts and students are nearly indistinguishable. The algorithms are in the range of human classifications. One difficult case appeared twice in order to examine reproducibility of classified foehn duration, which turned out to be 50% or less. The classification dataset can now serve as a testbed for automatic classification algorithms, which - if successful - eliminate the drawbacks of manual classifications: lack of scalability and reproducibility.

Item Type: Article
Faculty \ School: Faculty of Science > School of Environmental Sciences
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Depositing User: LivePure Connector
Date Deposited: 11 Sep 2018 11:30
Last Modified: 25 Jun 2020 00:26
URI: https://ueaeprints.uea.ac.uk/id/eprint/68219
DOI: 10.1175/BAMS-D-17-0200.1

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