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dc.contributor.author
Mony, Christoph
dc.contributor.author
Jansing, Lukas
dc.contributor.author
Sprenger, Michael
dc.date.accessioned
2022-01-28T20:06:52Z
dc.date.available
2022-01-07T10:53:17Z
dc.date.available
2022-01-28T20:06:52Z
dc.date.issued
2021-12-01
dc.identifier.issn
0882-8156
dc.identifier.issn
1520-0434
dc.identifier.other
10.1175/waf-d-21-0036.1
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/523862
dc.description.abstract
This study explores the possibilities of employing machine learning algorithms to predict foehn occurrence in Switzerland at a north Alpine (Altdorf) and south Alpine (Lugano) station from its synoptic fingerprint in reanalysis data and climate simulations. This allows for an investigation on a potential future shift in monthly foehn frequencies. First, inputs from various atmospheric fields from the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERAI) were used to train an XGBoost model. Here, similar predictive performance to previous work was achieved, showing that foehn can accurately be diagnosed from the coarse synoptic situation. In the next step, the algorithm was generalized to predict foehn based on the Community Earth System Model (CESM) ensemble simulations of a present-day and warming future climate. The best generalization between ERAI and CESM was obtained by including the present-day data in the training procedure and simultaneously optimizing two objective functions, namely, the negative log loss and squared mean loss, on both datasets, respectively. It is demonstrated that the same synoptic fingerprint can be identified in CESM climate simulation data. Finally, predictions for present-day and future simulations were verified and compared for statistical significance. Our model is shown to produce valid output for most months, revealing that south foehn in Altdorf is expected to become more common during spring, while north foehn in Lugano is expected to become more common during summer.
en_US
dc.language.iso
en
en_US
dc.publisher
American Meteorological Society
en_US
dc.subject
Downslope winds
en_US
dc.subject
Climate prediction
en_US
dc.subject
Ensembles
en_US
dc.subject
Forecast verification/skill
en_US
dc.subject
Forecasting techniques
en_US
dc.subject
Numerical weather prediction/forecasting
en_US
dc.subject
Probability forecasts/models/distribution
en_US
dc.subject
Statistical forecasting
en_US
dc.subject
Classification
en_US
dc.subject
Data science
en_US
dc.subject
Machine learning
en_US
dc.subject
Model interpretation and visualization
en_US
dc.title
Evaluating foehn occurrence in a changing climate based on reanalysis and climate model data using machine learning
en_US
dc.type
Journal Article
dc.date.published
2021-10-29
ethz.journal.title
Weather and Forecasting
ethz.journal.volume
36
en_US
ethz.journal.issue
6
en_US
ethz.journal.abbreviated
Weather forecast.
ethz.pages.start
2039
en_US
ethz.pages.end
2055
en_US
ethz.grant
An integrated weather-system perspective on the characteristics, dynamics and impacts of extreme seasons
en_US
ethz.grant
Foehn Dynamics - Lagrangian Analysis and Large-Eddy Simulation
en_US
ethz.identifier.wos
ethz.publication.place
Boston, MA
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02350 - Dep. Umweltsystemwissenschaften / Dep. of Environmental Systems Science::02717 - Institut für Atmosphäre und Klima / Inst. Atmospheric and Climate Science::03854 - Wernli, Johann Heinrich / Wernli, Johann Heinrich
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02350 - Dep. Umweltsystemwissenschaften / Dep. of Environmental Systems Science::02717 - Institut für Atmosphäre und Klima / Inst. Atmospheric and Climate Science::03854 - Wernli, Johann Heinrich / Wernli, Johann Heinrich
en_US
ethz.grant.agreementno
787652
ethz.grant.agreementno
181992
ethz.grant.fundername
EC
ethz.grant.fundername
SNF
ethz.grant.funderDoi
10.13039/501100000780
ethz.grant.funderDoi
10.13039/501100001711
ethz.grant.program
H2020
ethz.grant.program
Projekte MINT
ethz.date.deposited
2022-01-07T10:53:25Z
ethz.source
FORM
ethz.eth
yes
en_US
ethz.availability
Metadata only
en_US
ethz.rosetta.installDate
2022-01-28T20:07:04Z
ethz.rosetta.lastUpdated
2022-03-29T18:23:37Z
ethz.rosetta.versionExported
true
ethz.COinS
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