ON THE BORA BREAKTHROUGH NEAR A MOUNTAIN GAP

This study investigates the onset phase of a strong Adriatic bora on 04 April 2002 with high-resolution numerical modeling and observations. The airborne measurements were taken with the German Aerospace Center’s (DLR) Falcon aircraft within the framework of the EU-funded CAATER Programme 2001. The target area is a ~20-km wide mountain gap embedded in the Dinaric Alps, which favors strong jet-like winds. The model indicates a delay of the bora breakthrough at the coast of up to three hours between the center and the edge of the gap. During this period the wind field downstream of the gap is highly three-dimensional and transient. Near the gap center, a low-level jet is observed with winds exceeding 30 m s−1. Near the edge of the gap, the model shows flow separation and the formation of a low-level rotor with weak but reversed surface winds underneath trapped gravity waves. This complex flow configuration with strong spatial variations in the wind field leads to horizontal and vertical wind shear in the vicinity of Rijeka airport on Krk Island, which represents a potential hazard for air traffic

GAP FLOWS – OUR STATE OF KNOWLEDGE AT THE END OF MAP

A summary of gap flow results within MAP is given including conceptual models, dynamical understanding, observational and modeling tools, and open issues

Fine-Tuning Nonhomogeneous Regression for Probabilistic Precipitation Forecasts: Unanimous Predictions, Heavy Tails, and Link Functions

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. </jats:p

LC-MS/MS-based proteome profiling in Daphnia pulex and Daphnia longicephala: the Daphnia pulex genome database as a key for high throughput proteomics in Daphnia

<p>Abstract</p> <p>Background</p> <p>Daphniids, commonly known as waterfleas, serve as important model systems for ecology, evolution and the environmental sciences. The sequencing and annotation of the <it>Daphnia pulex </it>genome both open future avenues of research on this model organism. As proteomics is not only essential to our understanding of cell function, and is also a powerful validation tool for predicted genes in genome annotation projects, a first proteomic dataset is presented in this article.</p> <p>Results</p> <p>A comprehensive set of 701,274 peptide tandem-mass-spectra, derived from <it>Daphnia pulex</it>, was generated, which lead to the identification of 531 proteins. To measure the impact of the <it>Daphnia pulex </it>filtered models database for mass spectrometry based <it>Daphnia </it>protein identification, this result was compared with results obtained with the Swiss-Prot and the <it>Drosophila melanogaster </it>database. To further validate the utility of the <it>Daphnia pulex </it>database for research on other <it>Daphnia </it>species, additional 407,778 peptide tandem-mass-spectra, obtained from <it>Daphnia longicephala</it>, were generated and evaluated, leading to the identification of 317 proteins.</p> <p>Conclusion</p> <p>Peptides identified in our approach provide the first experimental evidence for the translation of a broad variety of predicted coding regions within the <it>Daphnia </it>genome. Furthermore it could be demonstrated that identification of <it>Daphnia longicephala </it>proteins using the <it>Daphnia pulex </it>protein database is feasible but shows a slightly reduced identification rate. Data provided in this article clearly demonstrates that the <it>Daphnia </it>genome database is the key for mass spectrometry based high throughput proteomics in <it>Daphnia</it>.</p

Circular Regression Trees and Forests with an Application to Probabilistic Wind Direction Forecasting

While circular data occur in a wide range of scientific fields, the methodology for distributional modeling and probabilistic forecasting of circular response variables is rather limited. Most of the existing methods are built on the framework of generalized linear and additive models, which are often challenging to optimize and to interpret. Therefore, we suggest circular regression trees and random forests as an intuitive alternative approach that is relatively easy to fit. Building on previous ideas for trees modeling circular means, we suggest a distributional approach for both trees and forests yielding probabilistic forecasts based on the von Mises distribution. The resulting tree-based models simplify the estimation process by using the available covariates for partitioning the data into sufficiently homogeneous subgroups so that a simple von Mises distribution without further covariates can be fitted to the circular response in each subgroup. These circular regression trees are straightforward to interpret, can capture nonlinear effects and interactions, and automatically select the relevant covariates that are associated with either location and/or scale changes in the von Mises distribution. Combining an ensemble of circular regression trees to a circular regression forest yields a local adaptive likelihood estimator for the von Mises distribution that can regularize and smooth the covariate effects. The new methods are evaluated in a case study on probabilistic wind direction forecasting at two Austrian airports, considering other common approaches as a benchmark

Remember the past: a comparison of time-adaptive training schemes for non-homogeneous regression

Non-homogeneous regression is a frequently used post-processing method for increasing the predictive skill of probabilistic ensemble weather forecasts. To adjust for seasonally varying error characteristics between ensemble forecasts and corresponding observations, different timeadaptive training schemes, including the classical sliding training window, have been developed for non-homogeneous regression. This study compares three such training approaches with the sliding-window approach for the application of post-processing near-surface air temperature forecasts across central Europe. The predictive performance is evaluated conditional on three different groups of stations located in plains, in mountain foreland, and within mountainous terrain, as well as on a specific change in the ensemble forecast system of the European Centre for Medium-Range Weather Forecasts (ECMWF) used as input for the post-processing. The results show that time-adaptive training schemes using data over multiple years stabilize the temporal evolution of the coefficient estimates, yielding an increased predictive performance for all station types tested compared to the classical sliding-window approach based on the most recent days only. While this may not be surprising under fully stable model conditions, it is shown that “remembering the past” from multiple years of training data is typically also superior to the classical sliding-window approach when the ensemble prediction system is affected by certain model changes. Thus, reducing the variance of the non-homogeneous regression estimates due to increased training data appears to be more important than reducing its bias by adapting rapidly to the most current training data only