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Georg J. Mayr and Thomas B. McKee

Abstract

The evolution of low-level flow upstream of the Continental Divide (Rocky Mountains) and the Wasatch Range from being unable to surmount the mountain range, to becoming unblocked and blocked again is studied observationally. During two months in the winter of 1991/92, a transect of three wind profilers measured the wind field every few minutes with unprecedented temporal detail.

The average state of that region during winter is blocked. A total of 47 blocked events were observed. A blocked flow event lasted on the average one and a half days, but the duration varied widely from a few hours to eight days controlled by the synoptic situation. The transition between the two states happened rapidly on the order of 1 h with a minimum of 20 min and a maximum of 4 h. The depth of the blocked layer during one blocking episode fluctuated considerably but reached on the average one-half to two-thirds of the barrier depth (depending on the location).

Previous research of idealized equilibrium situations focused on changes of the cross-barrier wind speed and stability as determining variables to build a mesoscale high over the barrier. Since their values were in the blocked range, other mechanisms had to trigger the transitions to an unblocked state.

A conceptual model proposes synoptic and radiative forcing to drive the blocking evolution. When the mountain-induced mesoscale high blocks the low-level flow, an opposing synoptic cross-barrier pressure gradient can negate the mesoscale high. Therefore unblocking happens most frequently when the trough axis of a short wave is immediately upstream of the harder, but synoptic pressure gradients caused by contrasts in vorticity and differential temperature advection are sometimes also strong enough. The flow returns to its blocked state when the ridge behind the trough approaches the barrier so that the synoptic cross-barrier pressure gradient reinforces the mesoscale high.

For a lower barrier or stronger solar insulation, a well-mixed boundary layer can grow almost to the height of the barrier by afternoon and reconnect the blocked layer with the higher cross-barrier winds above the mountain. After sunset the thermal forcing changes sign as the radiative cooling stabilizes the lower atmosphere again and the transition back to the blocked state occurs.

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Alexander Gohm, Günther Zängl, and Georg J. Mayr

Abstract

A case study of a south foehn windstorm observed across the Brenner Pass in the Wipp Valley near the Austrian–Italian border is presented based on a detailed comparison and verification of high-resolution numerical simulations with observations. The event of 24 through 25 October 1999 was part of the Intensive Observing Period 10 of the Mesoscale Alpine Programme (MAP). The simulations were performed with the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5). The observations were collected with a ground-based scanning Doppler lidar, an airborne aerosol backscatter lidar, a Doppler sodar, several weather stations, and two radiosounding systems. The study provides a synoptic-scale and mesoscale overview of the event and focuses on a comparison of simulated and observed fields for a 9-h period on 24 October 1999. The quantitative agreement between the numerical results and the observations is discussed in terms of root-mean-square error (rmse) and mean error (ME). Rmse values are high during the early stage of the event (∼7 m s−1), have a transient peak for about 1 h at 1400 UTC, and are minimal at the fully developed foehn stage near 1500 UTC (∼5 m s−1). The discrepancies at the beginning are likely to be related to deficiencies in the model profile on the upstream side of the pass, exhibiting a too low inversion and a too shallow southerly flow. The transient error peak at 1400 UTC is related to a mismatch in the timing of the enhancement of the upper-level winds. Moreover, evidence is found for an overestimation of the mass flux through the lower Brenner gap, which is the narrowest and deepest part of the incision in the main Alpine crest, and a subsequent underestimation of the flow descent into the Wipp Valley on the leeward side of the Brenner Pass. Considering mass continuity, the latter effect is probably a result of the former. Nevertheless, the model captures most of the striking foehn features: Simulated isentropes and aerosol backscatter measurements consistently indicate regions of flow descent, across-valley asymmetries, and hydraulic jump–like features. The across-valley asymmetry of the foehn strength near the Wipp Valley exit is particularly well reproduced by the model. The primary reason for the stronger winds on the eastern sidewall is the asymmetry in the position of the mountain ridges protruding into the valley together with the westward bending of the valley axis.

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Jakob W. Messner and Georg J. Mayr

Abstract

Three methods to make probabilistic weather forecasts by using analogs are presented and tested. The basic idea of these methods is that finding similar NWP model forecasts to the current one in an archive of past forecasts and taking the corresponding analyses as prediction should remove all systematic errors of the model. Furthermore, this statistical postprocessing can convert NWP forecasts to forecasts for point locations and easily turn deterministic forecasts into probabilistic ones. These methods are tested in the idealized Lorenz96 system and compared to a benchmark bracket formed by ensemble relative frequencies from direct model output and logistic regression. The analog methods excel at longer lead times.

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Jakob W. Messner and Georg J. Mayr
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Markus Dabernig, Georg J. Mayr, Jakob W. Messner, and Achim Zeileis

Abstract

Separate statistical models are typically fit for each forecasting lead time to postprocess numerical weather prediction (NWP) ensemble forecasts. Using standardized anomalies of both NWP values and observations eliminates most of the lead-time-specific characteristics so that several lead times can be forecast simultaneously. Standardized anomalies are formed by subtracting a climatological mean and dividing by the climatological standard deviation. Simultaneously postprocessing forecasts between +12 and +120 h increases forecast coherence between lead times, yields a temporal resolution as high as the observation interval (e.g., up to 10 min), and speeds up computation times while achieving a forecast skill comparable to the conventional method.

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

Abstract

Raw ensemble forecasts of precipitation amounts and their forecast uncertainty have large errors, especially in mountainous regions where the modeled topography in the numerical weather prediction model and real topography differ most. Therefore, statistical postprocessing is typically applied to obtain automatically corrected weather forecasts. This study applies the nonhomogenous regression framework as a state-of-the-art ensemble postprocessing technique to predict a full forecast distribution and improves its forecast performance with three statistical refinements. First of all, a novel split-type approach effectively accounts for unanimous zero precipitation predictions of the global ensemble model of the ECMWF. Additionally, the statistical model uses a censored logistic distribution to deal with the heavy tails of precipitation amounts. Finally, it is investigated which are the most suitable link functions for the optimization of regression coefficients for the scale parameter. These three refinements are tested for 10 stations in a small area of the European Alps for lead times from +24 to +144 h and accumulation periods of 24 and 6 h. Together, they improve probabilistic forecasts for precipitation amounts as well as the probability of precipitation events over the default postprocessing method. The improvements are largest for the shorter accumulation periods and shorter lead times, where the information of unanimous ensemble predictions is more important.

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

Abstract

Nonhomogeneous regression is often used to statistically postprocess ensemble forecasts. Usually only ensemble forecasts of the predictand variable are used as input, but other potentially useful information sources are ignored. Although it is straightforward to add further input variables, overfitting can easily deteriorate the forecast performance for increasing numbers of input variables. This paper proposes a boosting algorithm to estimate the regression coefficients, while automatically selecting the most relevant input variables by restricting the coefficients of less important variables to zero. A case study with ensemble forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) shows that this approach effectively selects important input variables to clearly improve minimum and maximum temperature predictions at five central European stations.

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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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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.

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Jakob W. Messner, Georg J. Mayr, Achim Zeileis, and Daniel S. Wilks

Abstract

To achieve well-calibrated probabilistic forecasts, ensemble forecasts are often statistically postprocessed. One recent ensemble-calibration method is extended logistic regression, which extends the popular logistic regression to yield full probability distribution forecasts. Although the purpose of this method is to postprocess ensemble forecasts, usually only the ensemble mean is used as the predictor variable, whereas the ensemble spread is neglected because it does not improve the forecasts. In this study it is shown that when simply used as an ordinary predictor variable in extended logistic regression, the ensemble spread affects the location but not the variance of the predictive distribution. Uncertainty information contained in the ensemble spread is therefore not utilized appropriately. To solve this drawback a new approach is proposed where the ensemble spread is directly used to predict the dispersion of the predictive distribution. With wind speed data and ensemble forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) it is shown that by using this approach, the ensemble spread can be used effectively to improve forecasts from extended logistic regression.

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