Search Results

You are looking at 21 - 26 of 26 items for

  • Author or Editor: Georg J. Mayr x
  • Refine by Access: Content accessible to me x
Clear All Modify Search
Jakob W. Messner, Georg J. Mayr, Daniel S. Wilks, and Achim Zeileis

Abstract

Extended logistic regression is a recent ensemble calibration method that extends logistic regression to provide full continuous probability distribution forecasts. It assumes conditional logistic distributions for the (transformed) predictand and fits these using selected predictand category probabilities. In this study extended logistic regression is compared to the closely related ordered and censored logistic regression models. Ordered logistic regression avoids the logistic distribution assumption but does not yield full probability distribution forecasts, whereas censored regression directly fits the full conditional predictive distributions. The performance of these and other ensemble postprocessing methods is tested on wind speed and precipitation data from several European locations and ensemble forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF). Ordered logistic regression performed similarly to extended logistic regression for probability forecasts of discrete categories whereas full predictive distributions were better predicted by censored regression.

Full access
Susanne Drechsel, Georg J. Mayr, Michel Chong, Martin Weissmann, Andreas Dörnbrack, and Ronald Calhoun

Abstract

During the field campaign of the Terrain-induced Rotor Experiment (T-REX) in the spring of 2006, Doppler lidar measurements were taken in the complex terrain of the Californian Owens Valley for six weeks. While fast three-dimensional (3D) wind analysis from measured radial wind components is well established for dual weather radars, only the feasibility was shown for dual-Doppler lidars. A computationally inexpensive, variational analysis method developed for multiple-Doppler radar measurements over complex terrain was applied. The general flow pattern of the 19 derived 3D wind fields is slightly smoothed in time and space because of lidar scan duration and analysis algorithm. The comparison of extracted wind profiles to profiles from radiosondes and wind profiler reveals differences of wind speed and direction of less than 1.1 m s−1 and 3°, on average, with standard deviations not exceeding 2.7 m s−1 and 27°, respectively. Standard velocity–azimuth display (VAD) retrieval method provided higher vertical resolution than the dual-Doppler retrieval, but no horizontal structure of the flow field. The authors suggest a simple way to obtain a good first guess for a dual-lidar scan strategy geared toward 3D wind retrieval that minimizes scan duration and maximizes spatial coverage.

Full access
Reto Stauffer, Nikolaus Umlauf, Jakob W. Messner, Georg J. Mayr, and Achim Zeileis

Abstract

Probabilistic forecasts provided by numerical ensemble prediction systems have systematic errors and are typically underdispersive. This is especially true over complex topography with extensive terrain-induced small-scale effects, which cannot be resolved by the ensemble system. To alleviate these errors, statistical postprocessing methods are often applied to calibrate the forecasts. This article presents a new full-distributional spatial postprocessing method for daily precipitation sums based on the standardized anomaly model output statistics (SAMOS) approach. Observations and forecasts are transformed into standardized anomalies by subtracting the long-term climatological mean and dividing by the climatological standard deviation. This removes all site-specific characteristics from the data and makes it possible to fit one single regression model for all stations at once. As the model does not depend on the station locations, it directly allows the creation of probabilistic forecasts for any arbitrary location. SAMOS uses a left-censored power-transformed logistic response distribution to account for the large fraction of zero observations (dry days), the limitation to nonnegative values, and the positive skewness of the data. ECMWF reforecasts are used for model training and to correct the ECMWF ensemble forecasts with the big advantage that SAMOS does not require an extensive archive of past ensemble forecasts as only the most recent four reforecasts are needed, and it automatically adapts to changes in the ECMWF ensemble model. The application of the new method to the central Alps shows that the new method is able to depict the small-scale properties and returns accurate fully probabilistic spatial forecasts.

Full access
Thorsten Simon, Peter Fabsic, Georg J. Mayr, Nikolaus Umlauf, and Achim Zeileis

Abstract

A probabilistic forecasting method to predict thunderstorms in the European eastern Alps is developed. A statistical model links lightning occurrence from the ground-based Austrian Lightning Detection and Information System (ALDIS) detection network to a large set of direct and derived variables from a numerical weather prediction (NWP) system. The NWP system is the high-resolution run (HRES) of the European Centre for Medium-Range Weather Forecasts (ECMWF) with a grid spacing of 16 km. The statistical model is a generalized additive model (GAM) framework, which is estimated by Markov chain Monte Carlo (MCMC) simulation. Gradient boosting with stability selection serves as a tool for selecting a stable set of potentially nonlinear terms. Three grids from 64 × 64 to 16 × 16 km2 and five forecast horizons from 5 days to 1 day ahead are investigated to predict thunderstorms during afternoons (1200–1800 UTC). Frequently selected covariates for the nonlinear terms are variants of convective precipitation, convective potential available energy, relative humidity, and temperature in the midlayers of the troposphere, among others. All models, even for a lead time of 5 days, outperform a forecast based on climatology in an out-of-sample comparison. An example case illustrates that coarse spatial patterns are already successfully forecast 5 days ahead.

Open access
Michael Hill, Ron Calhoun, H. J. S. Fernando, Andreas Wieser, Andreas Dörnbrack, Martin Weissmann, Georg Mayr, and Robert Newsom

Abstract

Dual-Doppler analysis of data from two coherent lidars during the Terrain-Induced Rotor Experiment (T-REX) allows the retrieval of flow structures, such as vortices, during mountain-wave events. The spatial and temporal resolution of this approach is sufficient to identify and track vortical motions on an elevated, cross-barrier plane in clear air. Assimilation routines or additional constraints such as two-dimensional continuity are not required. A relatively simple and quick least squares method forms the basis of the retrieval. Vortices are shown to evolve and advect in the flow field, allowing analysis of their behavior in the mountain–wave–boundary layer system. The locations, magnitudes, and evolution of the vortices can be studied through calculated fields of velocity, vorticity, streamlines, and swirl. Generally, observations suggest two classes of vortical motions: rotors and small-scale vortical structures. These two structures differ in scale and behavior. The level of coordination of the two lidars and the nature of the output (i.e., in range gates) creates inherent restrictions on the spatial and temporal resolution of retrieved fields.

Full access
Georg j. Mayr, David Plavcan, Laurence Armi, Andrew Elvidge, Branko Grisogono, Kristian Horvath, Peter Jackson, Alfred Neururer, Petra Seibert, James W. Steenburgh, Ivana Stiperski, Andrew Sturman, Željko Večenaj, Johannes Vergeiner, Simon Vosper, and Günther Zängl

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 the region, the sign of the temperature change at onset, and the depth of the overflowing layer. These winds affect the 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 master’s degree students who had taken mountain meteorology courses, and a further two of objective algorithms. Sixty periods of 48 h were classified for foehn–no foehn conditions 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 the reproducibility of classified foehn duration, which turned out to be 50% or less. The classification dataset can now serve as a test bed for automatic classification algorithms, which—if successful—eliminate the drawbacks of manual classifications: lack of scalability and reproducibility.

Open access