Articles | Volume 6, issue 1
https://doi.org/10.5194/ascmo-6-45-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/ascmo-6-45-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
11 May 2020
| 11 May 2020
Postprocessing ensemble forecasts of vertical temperature profiles
David Schoenach
CORRESPONDING AUTHOR
Institute of Atmospheric and Cryospheric Sciences, University
of Innsbruck, Innsbruck, Austria
Finnish Meteorological Institute, Helsinki, Finland
Thorsten Simon
Department of Statistics, University of Innsbruck, Innsbruck, Austria
Institute of Atmospheric and Cryospheric Sciences, University
of Innsbruck, Innsbruck, Austria
Georg Johann Mayr
Institute of Atmospheric and Cryospheric Sciences, University
of Innsbruck, Innsbruck, Austria
Related authors
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Fiona Fix, Georg Johann Mayr, Achim Zeileis, Isabell Kathrin Stucke, and Reto Stauffer
EGUsphere, https://doi.org/10.5194/egusphere-2024-2143, https://doi.org/10.5194/egusphere-2024-2143, 2024
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“Atmospheric deserts” (ADs) are air masses that are transported away from hot, dry regions. Our study introduces this new concept. ADs can suppress or boost thunderstorms, and potentially contribute to the formation of heat waves, which makes them relevant for forecasting extreme events. Using a novel detection method, we follow the AD directly from North Africa to Europe for a case in June 2022, allowing us to analyze the air mass at any time and investigate how it is modified along the way.
Gregor Ehrensperger, Thorsten Simon, Georg Johann Mayr, and Tobias Hell
EGUsphere, https://doi.org/10.48550/arXiv.2210.11529, https://doi.org/10.48550/arXiv.2210.11529, 2024
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Lightning can cause significant damages to infrastructure and pose risks to individuals. As lightning is a short and local event it is not explicitly resolved in atmospheric models. Instead, auxiliary descriptions based on meteorological expert knowledge are used to assess lightning. We used AI that successfully discovered on its own the ingredients that experts know to be essential for lightning in the well-studied region of the Alps. Additionally, it also recognized regional differences.
Thomas Muschinski, Georg J. Mayr, Achim Zeileis, and Thorsten Simon
Nonlin. Processes Geophys., 30, 503–514, https://doi.org/10.5194/npg-30-503-2023, https://doi.org/10.5194/npg-30-503-2023, 2023
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Statistical post-processing is necessary to generate probabilistic forecasts from physical numerical weather prediction models. To allow for more flexibility, there has been a shift in post-processing away from traditional parametric regression models towards modern machine learning methods. By fusing these two approaches, we developed model output statistics random forests, a new post-processing method that is highly flexible but at the same time also very robust and easy to interpret.
Deborah Morgenstern, Isabell Stucke, Georg J. Mayr, Achim Zeileis, and Thorsten Simon
Weather Clim. Dynam., 4, 489–509, https://doi.org/10.5194/wcd-4-489-2023, https://doi.org/10.5194/wcd-4-489-2023, 2023
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Two thunderstorm environments are described for Europe: mass-field thunderstorms, which occur mostly in summer, over land, and under similar meteorological conditions, and wind-field thunderstorms, which occur mostly in winter, over the sea, and under more diverse meteorological conditions. Our descriptions are independent of static thresholds and help to understand why thunderstorms in unfavorable seasons for lightning pose a particular risk to tall infrastructure such as wind turbines.
Thomas Muschinski, Moritz N. Lang, Georg J. Mayr, Jakob W. Messner, Achim Zeileis, and Thorsten Simon
Wind Energ. Sci., 7, 2393–2405, https://doi.org/10.5194/wes-7-2393-2022, https://doi.org/10.5194/wes-7-2393-2022, 2022
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The power generated by offshore wind farms can vary greatly within a couple of hours, and failing to anticipate these ramp events can lead to costly imbalances in the electrical grid. A novel multivariate Gaussian regression model helps us to forecast not just the means and variances of the next day's hourly wind speeds, but also their corresponding correlations. This information is used to generate more realistic scenarios of power production and accurate estimates for ramp probabilities.
Deborah Morgenstern, Isabell Stucke, Thorsten Simon, Georg J. Mayr, and Achim Zeileis
Weather Clim. Dynam., 3, 361–375, https://doi.org/10.5194/wcd-3-361-2022, https://doi.org/10.5194/wcd-3-361-2022, 2022
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Wintertime lightning in central Europe is rare but has a large damage potential for tall structures such as wind turbines. We use a data-driven approach to explain why it even occurs when the meteorological processes causing thunderstorms in summer are absent. In summer, with strong solar input, thunderclouds have a large vertical extent, whereas in winter, thunderclouds are shallower in the vertical but tilted and elongated in the horizontal by strong winds that increase with altitude.
Moritz N. Lang, Sebastian Lerch, Georg J. Mayr, Thorsten Simon, Reto Stauffer, and Achim Zeileis
Nonlin. Processes Geophys., 27, 23–34, https://doi.org/10.5194/npg-27-23-2020, https://doi.org/10.5194/npg-27-23-2020, 2020
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Statistical post-processing aims to increase the predictive skill of probabilistic ensemble weather forecasts by learning the statistical relation between historical pairs of observations and ensemble forecasts within a given training data set. This study compares four different training schemes and shows that including multiple years of data in the training set typically yields a more stable post-processing while it loses the ability to quickly adjust to temporal changes in the underlying data.
Christian Mallaun, Andreas Giez, Georg J. Mayr, and Mathias W. Rotach
Atmos. Chem. Phys., 19, 9769–9786, https://doi.org/10.5194/acp-19-9769-2019, https://doi.org/10.5194/acp-19-9769-2019, 2019
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This study presents airborne measurements in shallow convection over land to investigate the dynamic properties of clouds focusing on possible narrow downdraughts in the surrounding of the clouds. A characteristic narrow downdraught region (
subsiding shell) is found directly outside the cloud borders for the mean vertical wind distribution. The
subsiding shellresults from the distribution of the highly variable updraughts and downdraughts in the near vicinity of the cloud.
Moritz N. Lang, Georg J. Mayr, Reto Stauffer, and Achim Zeileis
Adv. Stat. Clim. Meteorol. Oceanogr., 5, 115–132, https://doi.org/10.5194/ascmo-5-115-2019, https://doi.org/10.5194/ascmo-5-115-2019, 2019
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Accurate wind forecasts are of great importance for decision-making processes in today's society. This work presents a novel probabilistic post-processing method for wind vector forecasts employing a bivariate Gaussian response distribution. To capture a possible mismatch between the predicted and observed wind direction caused by location-specific properties, the approach incorporates a smooth rotation of the wind direction conditional on the season and the forecasted ensemble wind direction.
Sebastian J. Dietz, Philipp Kneringer, Georg J. Mayr, and Achim Zeileis
Adv. Stat. Clim. Meteorol. Oceanogr., 5, 101–114, https://doi.org/10.5194/ascmo-5-101-2019, https://doi.org/10.5194/ascmo-5-101-2019, 2019
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Low-visibility conditions reduce the flight capacity of airports and can lead to delays and supplemental costs for airlines and airports. In this study, the forecasting skill and most important model predictors of airport-relevant low visibility are investigated for multiple flight planning horizons with different statistical models.
Manuel Gebetsberger, Reto Stauffer, Georg J. Mayr, and Achim Zeileis
Adv. Stat. Clim. Meteorol. Oceanogr., 5, 87–100, https://doi.org/10.5194/ascmo-5-87-2019, https://doi.org/10.5194/ascmo-5-87-2019, 2019
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This article presents a method for improving probabilistic air temperature forecasts, particularly at Alpine sites. Using a nonsymmetric forecast distribution, the probabilistic forecast quality can be improved with respect to the common symmetric Gaussian distribution used. Furthermore, a long-term training approach of 3 years is presented to ensure the stability of the regression coefficients. The research was based on a PhD project on building an automated forecast system for northern Italy.
Thorsten Simon, Georg J. Mayr, Nikolaus Umlauf, and Achim Zeileis
Adv. Stat. Clim. Meteorol. Oceanogr., 5, 1–16, https://doi.org/10.5194/ascmo-5-1-2019, https://doi.org/10.5194/ascmo-5-1-2019, 2019
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Lightning in Alpine regions is associated with events such as thunderstorms,
extreme precipitation, high wind gusts, flash floods, and debris flows.
We present a statistical approach to predict lightning counts based on
numerical weather predictions. Lightning counts are considered on a grid
with 18 km mesh size. Skilful prediction is obtained for a forecast horizon
of 5 days over complex terrain.
Jutta Vüllers, Georg J. Mayr, Ulrich Corsmeier, and Christoph Kottmeier
Atmos. Chem. Phys., 18, 18169–18186, https://doi.org/10.5194/acp-18-18169-2018, https://doi.org/10.5194/acp-18-18169-2018, 2018
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This paper investigates frequently occurring foehn at the Dead Sea, which strongly impacts the local climatic conditions, in particular temperature and humidity, as well as evaporation from the Dead Sea, the aerosol load, and visibility. A statistical classification exposes two types of foehn and first-time, high-resolution measurements reveal trigger mechanisms and relevant characteristics, such as wind velocities, affected air layers, and resulting phenomena such as hydraulic jumps and rotors.
Reto Stauffer, Georg J. Mayr, Jakob W. Messner, and Achim Zeileis
Adv. Stat. Clim. Meteorol. Oceanogr., 4, 65–86, https://doi.org/10.5194/ascmo-4-65-2018, https://doi.org/10.5194/ascmo-4-65-2018, 2018
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Snowfall forecasts are important for a range of economic sectors as well as for the safety of people and infrastructure, especially in mountainous regions. This work presents a novel statistical approach to provide accurate forecasts for fresh snow amounts and the probability of snowfall combining data from various sources. The results demonstrate that the new approach is able to provide reliable high-resolution hourly snowfall forecasts for the eastern European Alps up to 3 days ahead.
Thorsten Simon, Nikolaus Umlauf, Achim Zeileis, Georg J. Mayr, Wolfgang Schulz, and Gerhard Diendorfer
Nat. Hazards Earth Syst. Sci., 17, 305–314, https://doi.org/10.5194/nhess-17-305-2017, https://doi.org/10.5194/nhess-17-305-2017, 2017
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The study presents a newly developed statistical method to assess the risk of thunderstorms in complex terrain. Observations of lightning serve as an indicator for thunderstorms. The application of the method is illustrated for Carinthia which is located in Austria, Europe.
F. Oesterle, S. Ostermann, R. Prodan, and G. J. Mayr
Geosci. Model Dev., 8, 2067–2078, https://doi.org/10.5194/gmd-8-2067-2015, https://doi.org/10.5194/gmd-8-2067-2015, 2015
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Three practical meteorological applications with different characteristics highlight the core computer science aspects and applicability
of distributed computing to meteorology. Presenting cloud and grid computing this paper shows use case scenarios fitting a wide range of meteorological applications from operational to research studies. The paper concludes that distributed computing complements and extends existing high performance computing concepts.
S. Gisinger, G. J. Mayr, J. W. Messner, and R. Stauffer
Nonlin. Processes Geophys., 20, 305–310, https://doi.org/10.5194/npg-20-305-2013, https://doi.org/10.5194/npg-20-305-2013, 2013
Related subject area
Atmospheric science
Forecasting 24 h averaged PM2.5 concentration in the Aburrá Valley using tree-based machine learning models, global forecasts, and satellite information
A generalized Spatio-Temporal Threshold Clustering method for identification of extreme event patterns
Nonlinear time series models for the North Atlantic Oscillation
Comparing forecast systems with multiple correlation decomposition based on partial correlation
Using wavelets to verify the scale structure of precipitation forecasts
Automated detection of weather fronts using a deep learning neural network
Low-visibility forecasts for different flight planning horizons using tree-based boosting models
Skewed logistic distribution for statistical temperature post-processing in mountainous areas
Hourly probabilistic snow forecasts over complex terrain: a hybrid ensemble postprocessing approach
A statistical framework for conditional extreme event attribution
Mixture model-based atmospheric air mass classification: a probabilistic view of thermodynamic profiles
A path towards uncertainty assignment in an operational cloud-phase algorithm from ARM vertically pointing active sensors
Characterization of extreme precipitation within atmospheric river events over California
Jhayron S. Pérez-Carrasquilla, Paola A. Montoya, Juan Manuel Sánchez, K. Santiago Hernández, and Mauricio Ramírez
Adv. Stat. Clim. Meteorol. Oceanogr., 9, 121–135, https://doi.org/10.5194/ascmo-9-121-2023, https://doi.org/10.5194/ascmo-9-121-2023, 2023
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This study uses tree-based machine learning (ML) to forecast PM2.5 in a complex terrain region. The models show the potential to predict pollution events with several hours of anticipation, and they integrate multiple sources of information, including in situ stations, satellite data, and deterministic model outputs. The importance analysis helps understand the processes affecting air quality in the region and highlights the relevance of external sources of pollution in PM2.5 predictability.
Vitaly Kholodovsky and Xin-Zhong Liang
Adv. Stat. Clim. Meteorol. Oceanogr., 7, 35–52, https://doi.org/10.5194/ascmo-7-35-2021, https://doi.org/10.5194/ascmo-7-35-2021, 2021
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Consistent definition and verification of extreme events are still lacking. We propose a new generalized spatio-temporal threshold clustering method to identify extreme event episodes. We observe changes in the distribution of extreme precipitation frequency from large-scale well-connected spatial patterns to smaller-scale, more isolated rainfall clusters, possibly leading to more localized droughts and heat waves.
Thomas Önskog, Christian L. E. Franzke, and Abdel Hannachi
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 141–157, https://doi.org/10.5194/ascmo-6-141-2020, https://doi.org/10.5194/ascmo-6-141-2020, 2020
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The North Atlantic Oscillation (NAO) has a significant impact on seasonal climate and surface weather conditions throughout Europe, North America and the North Atlantic. In this paper, we study a number of linear and nonlinear models for a station-based time series of the daily winter NAO. We find that a class of nonlinear models, including both short and long lags, excellently reproduce the characteristic statistical properties of the NAO. These models can hence be used to simulate the NAO.
Rita Glowienka-Hense, Andreas Hense, Sebastian Brune, and Johanna Baehr
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 103–113, https://doi.org/10.5194/ascmo-6-103-2020, https://doi.org/10.5194/ascmo-6-103-2020, 2020
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A new method for weather and climate forecast model evaluation with respect to observations is proposed. Individually added values are estimated for each model, together with shared information both models provide equally on the observations. Finally, shared model information, which is not present in the observations, is calculated. The method is applied to two examples from climate and weather forecasting, showing new perspectives for model evaluation.
Sebastian Buschow and Petra Friederichs
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 13–30, https://doi.org/10.5194/ascmo-6-13-2020, https://doi.org/10.5194/ascmo-6-13-2020, 2020
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Two-dimensional wavelet transformations can be used to analyse the local structure of predicted and observed precipitation fields and allow for a forecast verification which focuses on the spatial correlation structure alone. This paper applies the novel concept to real numerical weather predictions and radar observations. Systematic similarities and differences between nature and simulation can be detected, localized in space and attributed to particular weather situations.
James C. Biard and Kenneth E. Kunkel
Adv. Stat. Clim. Meteorol. Oceanogr., 5, 147–160, https://doi.org/10.5194/ascmo-5-147-2019, https://doi.org/10.5194/ascmo-5-147-2019, 2019
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A deep learning convolutional neural network (DL-FRONT) was around 90 % successful in determining the locations of weather fronts over North America when compared against front locations determined manually by forecasters. DL-FRONT detects fronts using maps of air pressure, temperature, humidity, and wind from historical observations or climate models. DL-FRONT makes it possible to do science that was previously impractical because manual front identification would take too much time.
Sebastian J. Dietz, Philipp Kneringer, Georg J. Mayr, and Achim Zeileis
Adv. Stat. Clim. Meteorol. Oceanogr., 5, 101–114, https://doi.org/10.5194/ascmo-5-101-2019, https://doi.org/10.5194/ascmo-5-101-2019, 2019
Short summary
Short summary
Low-visibility conditions reduce the flight capacity of airports and can lead to delays and supplemental costs for airlines and airports. In this study, the forecasting skill and most important model predictors of airport-relevant low visibility are investigated for multiple flight planning horizons with different statistical models.
Manuel Gebetsberger, Reto Stauffer, Georg J. Mayr, and Achim Zeileis
Adv. Stat. Clim. Meteorol. Oceanogr., 5, 87–100, https://doi.org/10.5194/ascmo-5-87-2019, https://doi.org/10.5194/ascmo-5-87-2019, 2019
Short summary
Short summary
This article presents a method for improving probabilistic air temperature forecasts, particularly at Alpine sites. Using a nonsymmetric forecast distribution, the probabilistic forecast quality can be improved with respect to the common symmetric Gaussian distribution used. Furthermore, a long-term training approach of 3 years is presented to ensure the stability of the regression coefficients. The research was based on a PhD project on building an automated forecast system for northern Italy.
Reto Stauffer, Georg J. Mayr, Jakob W. Messner, and Achim Zeileis
Adv. Stat. Clim. Meteorol. Oceanogr., 4, 65–86, https://doi.org/10.5194/ascmo-4-65-2018, https://doi.org/10.5194/ascmo-4-65-2018, 2018
Short summary
Short summary
Snowfall forecasts are important for a range of economic sectors as well as for the safety of people and infrastructure, especially in mountainous regions. This work presents a novel statistical approach to provide accurate forecasts for fresh snow amounts and the probability of snowfall combining data from various sources. The results demonstrate that the new approach is able to provide reliable high-resolution hourly snowfall forecasts for the eastern European Alps up to 3 days ahead.
Pascal Yiou, Aglaé Jézéquel, Philippe Naveau, Frederike E. L. Otto, Robert Vautard, and Mathieu Vrac
Adv. Stat. Clim. Meteorol. Oceanogr., 3, 17–31, https://doi.org/10.5194/ascmo-3-17-2017, https://doi.org/10.5194/ascmo-3-17-2017, 2017
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The attribution of classes of extreme events, such as heavy precipitation or heatwaves, relies on the estimate of small probabilities (with and without climate change). Such events are connected to the large-scale atmospheric circulation. This paper links such probabilities with properties of the atmospheric circulation by using a Bayesian decomposition. We test this decomposition on a case of extreme precipitation in the UK, in January 2014.
Jérôme Pernin, Mathieu Vrac, Cyril Crevoisier, and Alain Chédin
Adv. Stat. Clim. Meteorol. Oceanogr., 2, 115–136, https://doi.org/10.5194/ascmo-2-115-2016, https://doi.org/10.5194/ascmo-2-115-2016, 2016
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Here, we propose a classification methodology of various space-time atmospheric datasets into discrete air mass groups homogeneous in temperature and humidity through a probabilistic point of view: both the classification process and the data are probabilistic. Unlike conventional classification algorithms, this methodology provides the probability of belonging to each class as well as the corresponding uncertainty, which can be used in various applications.
Laura D. Riihimaki, Jennifer M. Comstock, Kevin K. Anderson, Aimee Holmes, and Edward Luke
Adv. Stat. Clim. Meteorol. Oceanogr., 2, 49–62, https://doi.org/10.5194/ascmo-2-49-2016, https://doi.org/10.5194/ascmo-2-49-2016, 2016
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Between atmospheric temperatures of 0 and −38 °C, clouds contain ice crystals, super-cooled liquid droplets, or a mixture of both, impacting how they influence the atmospheric energy budget and challenging our ability to simulate climate change. Better cloud-phase measurements are needed to improve simulations. We demonstrate how a Bayesian method to identify cloud phase can improve on currently used methods by including information from multiple measurements and probability estimates.
S. Jeon, Prabhat, S. Byna, J. Gu, W. D. Collins, and M. F. Wehner
Adv. Stat. Clim. Meteorol. Oceanogr., 1, 45–57, https://doi.org/10.5194/ascmo-1-45-2015, https://doi.org/10.5194/ascmo-1-45-2015, 2015
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This paper investigates the influence of atmospheric rivers on spatial coherence of extreme precipitation under a changing climate. We use our TECA software developed for detecting atmospheric river events and apply statistical techniques based on extreme value theory to characterize the spatial dependence structure between precipitation extremes within the events. The results show that extreme rainfall caused by atmospheric river events is less spatially correlated under the warming scenario.
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Short summary
State-of-the-art statistical methods are applied to postprocess an ensemble of numerical forecasts for vertical profiles of air temperature. These profiles are important tools in weather forecasting as they show the stratification and the static stability of the atmosphere. Flexible regression models combined with the multi-dimensionality of the data lead to better calibration and representation of uncertainty of the vertical profiles.
State-of-the-art statistical methods are applied to postprocess an ensemble of numerical...
