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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
Manuel Gebetsberger, Jakob W. Messner, Georg J. Mayr, and Achim Zeileis

Abstract

Nonhomogeneous regression models are widely used to statistically postprocess numerical ensemble weather prediction models. Such regression models are capable of forecasting full probability distributions and correcting for ensemble errors in the mean and variance. To estimate the corresponding regression coefficients, minimization of the continuous ranked probability score (CRPS) has widely been used in meteorological postprocessing studies and has often been found to yield more calibrated forecasts compared to maximum likelihood estimation. From a theoretical perspective, both estimators are consistent and should lead to similar results, provided the correct distribution assumption about empirical data. Differences between the estimated values indicate a wrong specification of the regression model. This study compares the two estimators for probabilistic temperature forecasting with nonhomogeneous regression, where results show discrepancies for the classical Gaussian assumption. The heavy-tailed logistic and Student’s t distributions can improve forecast performance in terms of sharpness and calibration, and lead to only minor differences between the estimators employed. Finally, a simulation study confirms the importance of appropriate distribution assumptions and shows that for a correctly specified model the maximum likelihood estimator is slightly more efficient than the CRPS estimator.

Open access
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.

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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.

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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.

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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