Zonda wind classification using machine learning algorithms

Author:

Otero Federico1ORCID,Araneo Diego1

Affiliation:

1. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales, (IANIGLA) CCT Mendoza–CONICET Mendoza Argentina

Publisher

Wiley

Subject

Atmospheric Science

Reference38 articles.

1. Stratified flow over topography: bifurcation fronts and transition to the uncontrolled state

2. False Alarms and Close Calls: A Conceptual Model of Warning Accuracy

3. Conrad V.(1936).Die klimatologischen Elemente und ihre Abhängigkeit von terrestrischen Einflüssen.

4. Courvoisier H. W. andT.Gutermann (1971).Zur praktischen Anwendung des Föhntests von Widmer. MeteoSwiss Report number: 21 7 pp. Available athttp://www.agfoehn.org/doc/Courvoisier_1971.pdf. Accessed 13 October 2019.

5. Weather Regime Prediction Using Statistical Learning

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1. Foehn wind detection using unsupervised machine learning;2024 International Conference on Machine Intelligence for GeoAnalytics and Remote Sensing (MIGARS);2024-04-08

2. Synoptic fingerprints of Zonda wind from a statistical prediction model;International Journal of Climatology;2023-09-15

3. Machine learning-based prediction of Alpine foehn events using GNSS troposphere products: first results for Altdorf, Switzerland;Atmospheric Measurement Techniques;2022-10-14

4. On the High Winds in the Tianshan Grand Canyon in Northwest China: General Features, Synoptic Conditions, and Mesoscale Structures;Frontiers in Earth Science;2022-06-28

5. Forecasting Zonda Wind Occurrence with Vertical Sounding Data;Advances in Atmospheric Sciences;2022-01

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