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- Author or Editor: Johannes Jenkner x
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Abstract
The Eady model with rigid upper lid is considered. The resonant, linearly amplifying solution that exists in the situation of neutral normal modes when an infinitely thin potential vorticity (PV) perturbation is located precisely at the steering level of a zero-PV neutral mode is explicitly derived. This resonant solution is discussed by partitioning the solution into nonzero-PV and zero-PV contributions. It possesses key observed properties of a growing baroclinic structure. The partitioning of the resonant solution clearly demonstrates the existence of modal growth in a short-wave Eady setting. In contrast to the semi-infinite model, a nonamplifying zero-PV contribution is necessary, in addition to the resonant part, to ensure vanishing vertical velocity at both the upper and the lower lid.
Abstract
The Eady model with rigid upper lid is considered. The resonant, linearly amplifying solution that exists in the situation of neutral normal modes when an infinitely thin potential vorticity (PV) perturbation is located precisely at the steering level of a zero-PV neutral mode is explicitly derived. This resonant solution is discussed by partitioning the solution into nonzero-PV and zero-PV contributions. It possesses key observed properties of a growing baroclinic structure. The partitioning of the resonant solution clearly demonstrates the existence of modal growth in a short-wave Eady setting. In contrast to the semi-infinite model, a nonamplifying zero-PV contribution is necessary, in addition to the resonant part, to ensure vanishing vertical velocity at both the upper and the lower lid.
Abstract
The south foehn is a characteristic downslope windstorm in the valleys of the northern Alps in Europe that demands reliable forecasts because of its substantial economic and societal impacts. Traditionally, a foehn is predicted based on pressure differences and tendencies across the Alpine ridge. Here, a new objective method for foehn prediction is proposed based on a machine learning algorithm (called AdaBoost, short for adaptive boosting). Three years (2000–02) of hourly simulations of the Consortium for Small-Scale Modeling’s (COSMO) numerical weather prediction (NWP) model and corresponding foehn wind observations are used to train the algorithm to distinguish between foehn and nonfoehn events. The predictors (133 in total) are subjectively extracted from the 7-km COSMO reanalysis dataset based on the main characteristics of foehn flows. The performance of the algorithm is then assessed with a validation dataset based on a contingency table that concisely summarizes the cooccurrence of observed and predicted (non)foehn events. The main performance measures are probability of detection (88.2%), probability of false detection (2.9%), missing rate (11.8%), correct alarm ratio (66.2%), false alarm ratio (33.8%), and missed alarm ratio (0.8%). To gain insight into the prediction model, the relevance of the single predictors is determined, resulting in a predominance of pressure differences across the Alpine ridge (i.e., similar to the traditional methods) and wind speeds at the foehn stations. The predominance of pressure-related predictors is further established in a sensitivity experiment where ~2500 predictors are objectively incorporated into the prediction model using the AdaBoost algorithm. The performance is very similar to the run with the subjectively determined predictors. Finally, some practical aspects of the new foehn index are discussed (e.g., the predictability of foehn events during the four seasons). The correct alarm rate is highest in winter (86.5%), followed by spring (79.6%), and then autumn (69.2%). The lowest rates are found in summer (51.2%).
Abstract
The south foehn is a characteristic downslope windstorm in the valleys of the northern Alps in Europe that demands reliable forecasts because of its substantial economic and societal impacts. Traditionally, a foehn is predicted based on pressure differences and tendencies across the Alpine ridge. Here, a new objective method for foehn prediction is proposed based on a machine learning algorithm (called AdaBoost, short for adaptive boosting). Three years (2000–02) of hourly simulations of the Consortium for Small-Scale Modeling’s (COSMO) numerical weather prediction (NWP) model and corresponding foehn wind observations are used to train the algorithm to distinguish between foehn and nonfoehn events. The predictors (133 in total) are subjectively extracted from the 7-km COSMO reanalysis dataset based on the main characteristics of foehn flows. The performance of the algorithm is then assessed with a validation dataset based on a contingency table that concisely summarizes the cooccurrence of observed and predicted (non)foehn events. The main performance measures are probability of detection (88.2%), probability of false detection (2.9%), missing rate (11.8%), correct alarm ratio (66.2%), false alarm ratio (33.8%), and missed alarm ratio (0.8%). To gain insight into the prediction model, the relevance of the single predictors is determined, resulting in a predominance of pressure differences across the Alpine ridge (i.e., similar to the traditional methods) and wind speeds at the foehn stations. The predominance of pressure-related predictors is further established in a sensitivity experiment where ~2500 predictors are objectively incorporated into the prediction model using the AdaBoost algorithm. The performance is very similar to the run with the subjectively determined predictors. Finally, some practical aspects of the new foehn index are discussed (e.g., the predictability of foehn events during the four seasons). The correct alarm rate is highest in winter (86.5%), followed by spring (79.6%), and then autumn (69.2%). The lowest rates are found in summer (51.2%).
Abstract
A novel methodology is presented for the identification of the mean cycle of the Madden–Julian oscillation (MJO) along the equator. The methodology is based on a nonlinear principal component (NLPC) computed with a neural network model. The bandpass-filtered input data encompass 30 yr with zonal winds at 850 and 200 hPa plus outgoing longwave radiation (OLR). The NLPC is conditioned on a sufficiently strong MJO activity and is computed both for the pooled dataset and for the dataset stratified into seasons. The NLPC for all data depicts a circular mode formed by the first two linear principal components (LPCs) with marginal contributions by the higher-order LPCs. Hence, the mean MJO cycle throughout the year is effectively captured by the amplitude of the leading two LPCs varying in quadrature. The NLPC for individual seasons shows additional variability, which mainly arises from a subordinate oscillation of the second pair of LPCs superimposed on the annual MJO signal. In reference to the all-year solution, the difference in resolved variability approximately accounts for 9% in solstitial seasons and 3% in equinoctial seasons. The phasing of the third LPC is such that convective activity oscillations over the Maritime Continent as well as wind oscillations over the Indian Ocean appear enhanced (suppressed) during boreal winter (summer). Also, convective activity oscillations appear more pronounced at the date line during both winter and summer. The phasing of the fourth LPC is such that upper-level westerlies over the Atlantic region are more persistent during boreal spring than during other seasons.
Abstract
A novel methodology is presented for the identification of the mean cycle of the Madden–Julian oscillation (MJO) along the equator. The methodology is based on a nonlinear principal component (NLPC) computed with a neural network model. The bandpass-filtered input data encompass 30 yr with zonal winds at 850 and 200 hPa plus outgoing longwave radiation (OLR). The NLPC is conditioned on a sufficiently strong MJO activity and is computed both for the pooled dataset and for the dataset stratified into seasons. The NLPC for all data depicts a circular mode formed by the first two linear principal components (LPCs) with marginal contributions by the higher-order LPCs. Hence, the mean MJO cycle throughout the year is effectively captured by the amplitude of the leading two LPCs varying in quadrature. The NLPC for individual seasons shows additional variability, which mainly arises from a subordinate oscillation of the second pair of LPCs superimposed on the annual MJO signal. In reference to the all-year solution, the difference in resolved variability approximately accounts for 9% in solstitial seasons and 3% in equinoctial seasons. The phasing of the third LPC is such that convective activity oscillations over the Maritime Continent as well as wind oscillations over the Indian Ocean appear enhanced (suppressed) during boreal winter (summer). Also, convective activity oscillations appear more pronounced at the date line during both winter and summer. The phasing of the fourth LPC is such that upper-level westerlies over the Atlantic region are more persistent during boreal spring than during other seasons.
