Journal: Weather and Forecasting

Abbreviation

Weather forecast.

Publisher

American Meteorological Society

Journal Volumes

ISSN

0882-8156
1520-0434

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Publications 1 - 10 of 11
  • A Warning System for the Laseyer Windstorm in a Narrow and Deep Valley of Northeastern Switzerland
    Item type: Journal Article
    Sprenger, Michael; Mahlstein, Irina; Caratsch, Andrina; et al. (2024)
    Weather and Forecasting
    The Laseyer is a very local and uncommon windstorm in a narrow and steep valley in northeastern Switzerland. Whereas the ambient wind is from west to northwest, the strong surface wind in the valley is from the east, leading to gust speeds that become dangerous for the local train running into the valley to the Wasserauen station. To minimize the risk of derailment and to improve passenger comfort, the train service provider Appenzeller Bahnen (AB) has developed a new warning algorithm in close collaboration with academia (ETH Zurich) and the Swiss national weather service (MeteoSwiss). The aim is not only to accurately predict the Laseyer windstorm several hours in advance but also to reduce the number of false alarms. The new warning system is based on the MeteoSwiss operational ensemble prediction system at 1.1-km horizontal mesh size, which is then used in combination with an observation-based machine learning approach to probabilistically forecast Laseyer events up to 30 h in advance. A particular challenge in developing the new system was to introduce the customer, AB, to the modern concept of probabilistic numerical weather prediction, which requires a careful risk assessment by the customer. Hence, the development of the warning system is a process in which the customer and the warning provider closely collaborate and specify the final warning products to be delivered operationally. The operation of the new warning system during the 2021/22 Laseyer season shows that it is working successfully and also indicates that the warning thresholds in the warning algorithm can be adjusted in the future to minimize false alarms without increasing the number of missed events.
  • Evaluating foehn occurrence in a changing climate based on reanalysis and climate model data using machine learning
    Item type: Journal Article
    Mony, Christoph; Jansing, Lukas; Sprenger, Michael (2021)
    Weather and Forecasting
    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.
  • A Case Study of High-Impact Wet Snowfall in Northwest Germany (25-27 November 2005)
    Item type: Journal Article
    Frick, Claudia; Wernli, Heini (2012)
    Weather and Forecasting
  • Seasonal Forecasting of Precipitation-Relevant Weather Types over the United States
    Item type: Journal Article
    Towler, Erin; Done, James M.; Ge, Ming; et al. (2025)
    Weather and Forecasting
    This paper evaluates seasonal forecasts of weather types (WTs), i.e., recurring large-scale atmospheric patterns, which have been developed using a clustering method using large-scale predictors to represent precipitation variability across the United States. Forecast quality is assessed using two seasonal hindcast products: from the operational weather community, hindcasts from the European Centre for Medium-Range Weather Forecasts (ECMWF), as well as hindcasts from the Community Earth System Model, version 2 (CESM2), a community resource developed by the National Science Foundation (NSF) National Center for Atmospheric Research, a climate research center. WTs are described in terms of their associated precipitation anomalies and examined in light of their relationship with established climate teleconnections. The spatial precipitation patterns associated with each WT are less well captured in the forecasting systems than the large-scale variables from which the WTs are derived. The WT patterns themselves are well represented in both seasonal forecasting systems, though, on average, ECMWF is slightly closer to observations. Forecasted WT frequency results show that both prediction systems have similar skill, with most differences depending on season and WT. Winter WT frequencies are generally more predictable than summer. Both forecast systems capture well the frequency rank order but underestimate the interannual frequency spread, which could be partially due to ensemble averaging. Analysis shows that forecasting of climate teleconnection indices alone would not be sufficient to represent the precipitation variability associated with the WTs. Comparable results from two initialized Earth system prediction models that originate from different sides of the weather–climate and operations–research spectrum are encouraging and contribute to WT forecasting and multimodel initialized prediction efforts.
  • Using a 10-Year Radar Archive for Nowcasting Precipitation Growth and Decay: A Probabilistic Machine Learning Approach
    Item type: Journal Article
    Foresti, Loris; Sideris, Ioannis V.; Nerini, Daniele; et al. (2019)
    Weather and Forecasting
  • A Case Study in Modeling Low-Lying Inversions and Stratocumulus Cloud Cover in the Bay of Biscay
    Item type: Journal Article
    Possner, Anna; Zubler, Elias; Fuhrer, Oliver; et al. (2014)
    Weather and Forecasting
  • Nowcasting Foehn Wind Events Using the AdaBoost Machine Learning Algorithm
    Item type: Journal Article
    Sprenger, Michael; Schemm, Sebastian; Oechslin, Roger; et al. (2017)
    Weather and Forecasting
  • Heavy Precipitation at the Alpine South Side and Saharan Dust over Central Europe
    Item type: Journal Article
    Wiegand, Lars; Twitchett, Arwen; Schwierz, Cornelia; et al. (2011)
    Weather and Forecasting
  • Seamless Multimodel Postprocessing for Air Temperature Forecasts in Complex Topography
    Item type: Journal Article
    Keller, Regula; Rajczak, Jan; Bhend, Jonas; et al. (2021)
    Weather and Forecasting
    Statistical postprocessing is applied in operational forecasting to correct systematic errors of numerical weather prediction models (NWP) and to automatically produce calibrated local forecasts for end-users. Postprocessing is particularly relevant in complex terrain, where even state-of-the-art high-resolution NWP systems cannot resolve many of the small-scale processes shaping local weather conditions. In addition, statistical postprocessing can also be used to combine forecasts from multiple NWP systems. Here we assess an ensemble model output statistics (EMOS) approach to produce seamless temperature forecasts based on a combination of short-term ensemble forecasts from a convection-permitting limited-area ensemble and a medium-range global ensemble forecasting model. We quantify the benefit of this approach compared to only postprocessing the high-resolution NWP. The multimodel EMOS approach (“mixed EMOS”) is able to improve forecasts by 30% with respect to direct model output from the high-resolution NWP. A detailed evaluation of mixed EMOS reveals that it outperforms either one of the single-model EMOS versions by 8%–12%. Temperature forecasts at valley locations profit in particular from the model combination. All forecast variants perform worst in winter (DJF); however, calibration and model combination improves forecast quality substantially. In addition to increasing skill as compared to single-model postprocessing, it also enables us to seamlessly combine multiple forecast sources with different time horizons (and horizontal resolutions) and thereby consolidates short-term to medium-range forecasting time horizons in one product without any user-relevant discontinuity. © 2021 American Meteorological Society
  • A Study in Weather Model Output Postprocessing: Using the Boosting Method for Thunderstorm Detection
    Item type: Journal Article
    Perler, Donat; Marchand, Oliver (2009)
    Weather and Forecasting
Publications 1 - 10 of 11