A network-based detection scheme for the jet stream core

The polar and subtropical jet streams are strong upper-level winds with a crucial influence on weather throughout the Northern Hemisphere midlatitudes. In particular, the polar jet is located between cold arctic air to the north and warmer subtropical air to the south. Strongly meandering states therefore often lead to extreme surface weather. Some algorithms exist which can detect the 2-D (latitude and longitude) jets’ core around the hemisphere, but all of them use a minimal threshold to determine the subtropical and polar jet stream. This is particularly problematic for the polar jet stream, whose wind velocities can change rapidly from very weak to very high values and vice versa. We develop a network-based scheme using Dijkstra’s shortest-path algorithm to detect the polar and subtropical jet stream core. This algorithm not only considers the commonly used wind strength for core detection but also takes wind direction and climatological latitudinal position into account. Furthermore, it distinguishes between polar and subtropical jet, and between separate and merged jet states. The parameter values of the detection scheme are optimized using simulated annealing and a skill function that accounts for the zonal-mean jet stream position (Rikus, 2015). After the successful optimization process, we apply our scheme to reanalysis data covering 1979–2015 and calculate seasonal-mean probabilistic maps and trends in wind strength and position of jet streams. We present longitudinally defined probability distributions of the positions for both jets for all on the Northern Hemisphere seasons. This shows that winter is characterized by two well-separated jets over Europe and Asia (ca. 20Wto 140 E). In contrast, summer normally has a single merged jet over the western hemisphere but can have both merged and separated jet states in the eastern hemisphere. With this algorithm it is possible to investigate the position of the jets’ cores around the hemisphere and it is therefore very suitable to analyze jet stream patterns in observations and models, enabling more advanced model-validation

Semi-Automatic Classification of Weather Maps

In this paper we analyze weather maps to distinguish between the three main circulation forms which are essential factors for weather composition and are fundamental for weather forecasters. We propose a set of features specifically tailored for the classification of these circulation forms in General Weather Situations and use these to train a support vector machine for classification. Additionally, we propose a semi-automatic algorithm to extract the necessary data directly from the weather maps itself. This enables us to also analyze historic map material for which the original data is not available anymore. In order to reconstruct the weather data, we extract and analyze the isolines from the weather maps based on color and line thickness as well as symbolic and numerical features using template matching techniques. We reconstruct the dense wind alignment field and air velocity field from these sparse data and extract expressive feature vectors to classify the presented main circulation forms. Our algorithm shows an overall classification success rate of 61% for the three main circulation forms zonal, meridional and mixed

Methods for the visualization of clustered climate data

Visualization, Cluster Analysis, Climate Impact Research,

Enhancing the Visualization Process with Principal Component Analysis To Support the . . .

This paper describes the integration of the Principal Component Analysis into the Visualization Process. Although, the combination of Principal Component Analysis (PCA) and visual methods is a common approach to the analysis of high-dimensional datasets, it is mostly limited to a pure preprocessing step for dimension reduction. In this paper we will discuss, how PCA results can be used to control all steps of the visualization pipeline to generate more effective visual representations, and thus, a higher degree of understanding of the PCA values as well as of original data

Iconbased Visualization using Mosaic Metaphors

This paper introduces a new approach to extend iconbased visualization methods by using a mosaic-based paradigm. We discuss, how image metaphors closely related to the application domain can be applied for icon-based representations. Therefore, we enhance visualizations by well-known Image Mosaic techniques, such as image layouts, image selection and color adaption. Furthermore, we present the results of our approach by discussing an example of a clustered real-world climate data set

Interactive Presentation of Geo-Spatial Climate Data in Multi-Display Environments

The visual analysis of complex geo-spatial data is a challenging task. Typically, different views are used to communicate different aspects. With changing topics of interest, however, novel views are required. This leads to dynamically changing presentations of multiple views. This paper introduces a novel approach to support such scenarios. It allows for a spontaneous incorporation of views from different sources and to automatically layout these views in a multi-display environment. Furthermore, we introduce an enhanced undo/redo mechanism for this setting, which records user interactions and, in this way, enables swift reconfigurations of displayed views. Hence, users can fluently switch the focus of visual analysis without extensive manual interactions. We demonstrate our approach by the particular use case of discussing geo-spatial climate data