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Potential vorticity (PV) analysis plays a central role in studying atmospheric dynamics and in particular in studying the life cycle of weather systems. The three-dimensional (3-D) structure and temporal evolution of the associated PV features, however, are not yet fully understood. An automated technique to objectively identify 3-D PV features can help to shed light on 3-D atmospheric dynamics in specific case studies as well as facilitate statistical evaluations within climatological studies. Such a technique to identify PV features fully in 3-D, however, does not yet exist. This study presents a novel algorithm for the objective identification of PV anomalies along the dynamical tropopause in gridded data, as commonly output by numerical simulation models. The algorithm is inspired by morphological image processing techniques and can be applied to both two-dimensional (2-D) and 3-D fields on vertically isentropic levels. The method maps input data to a horizontally stereographic projection and relies on an efficient computation of horizontal distances within the projected field. Candidates for PV anomaly features are filtered according to heuristic criteria, and feature description vectors are obtained for further analysis. The generated feature descriptions are well suited for subsequent case studies of 3-D atmospheric dynamics as represented by the underlying numerical simulation. We evaluate our approach by comparison with an existing 2-D technique and demonstrate the full 3-D perspective by means of a case study of an extreme precipitation event that was dynamically linked to a prominent subtropical PV anomaly. The case study demonstrates variations in the 3-D structure of the detected PV anomalies that would not have been captured by a 2-D method. We discuss further advantages of using a 3-D approach, including elimination of temporal inconsistencies in the detected features due to 3-D structural variation and elimination of the need to manually select a specific isentropic level on which the anomalies are assumed to be best captured. These advantages, as well as the suitability of the implementation to process big data sets, also open applications for climatological analyses. The method is made available as open-source for straightforward use by the atmospheric community.

Numerical weather prediction models rely on parameterizations for subgrid-scale processes, e.g., for cloud microphysics, which are a well-known source of uncertainty in weather forecasts. Via algorithmic differentiation, which computes the sensitivities of prognostic variables to changes in model parameters, these uncertainties can be quantified. In this article, we present visual analytics solutions to analyze interactively the sensitivities of a selected prognostic variable to multiple model parameters along strongly ascending trajectories, so-called warm conveyor belt (WCB) trajectories. We propose a visual interface that enables to a) compare the values of multiple sensitivities at a single time step on multiple trajectories, b) assess the spatio-temporal relationships between sensitivities and the trajectories' shapes and locations, and c) find similarities in the temporal development of sensitivities along multiple trajectories. We demonstrate how our approach enables atmospheric scientists to interactively analyze the uncertainty in the microphysical parameterizations, and along the trajectories, with respect to the selected prognostic variable. We apply our approach to the analysis of WCB trajectories within the extratropical cyclone "Vladiana", which occurred between 22–25 September 2016 over the North Atlantic.

Atmospheric fronts are a widely used conceptual model in meteorology, most encountered as two-dimensional (2-D) front lines on surface analysis charts. The three-dimensional (3-D) dynamical structure of fronts has been studied in the literature by means of “standard” 2-D maps and cross-sections and is commonly sketched in 3-D illustrations of idealized weather systems in atmospheric science textbooks. However, only recently the feasibility of objective detection and visual analysis of 3-D frontal structures and their dynamics within numerical weather prediction (NWP) data has been proposed, and such approaches are not yet widely known in the atmospheric science community. In this article, we investigate the benefit of objective 3-D front detection for case studies of extratropical cyclones and for comparison of frontal structures between different NWP models. We build on a recent gradient-based detection approach, combined with modern 3-D interactive visual analysis techniques, and adapt it to handle data from state-of-the-art NWP models including those run at convection-permitting kilometer-scale resolution. The parameters of the detection method (including data smoothing and threshold parameters) are evaluated to yield physically meaningful structures. We illustrate the benefit of the method by presenting two case studies of frontal dynamics within mid-latitude cyclones. Examples include joint interactive visual analysis of 3-D fronts and warm conveyor belt (WCB) trajectories, and identification of the 3-D frontal structures characterising the different stages of a Shapiro-Keyser cyclogenesis event. The 3-D frontal structures show agreement with 2-D fronts from surface analysis charts and augment the surface charts by providing additional pertinent information in the vertical dimension. A second application illustrates the effect of convection on 3-D cold front structure by comparing data from simulations with parameterised and explicit convection and shows that convection could strengthen the cold front. Finally, we consider “secondary fronts” that commonly appear in UK Met Office surface analysis charts. Examination of a case study shows that for this event the secondary front is not a temperature-based but purely a humidity-based feature. We argue that the presented approach has great potential to be beneficial for more complex studies of atmospheric dynamics and for operational weather forecasting.

Atmospheric fronts are a widely used conceptual model in meteorology, most encountered as two-dimensional (2-D) front lines, e.g., on surface analysis charts. The three-dimensional (3-D) dynamical structure of fronts is commonly sketched in 3-D illustrations of idealized weather systems in atmospheric science textbooks. Only recently the feasibility of objective detection and visual analysis of “real” 3-D frontal structures within numerical weather prediction (NWP) data has been proposed, and such approaches are not yet widely known in the atmospheric community. In our work, we investigate the benefit of objective 3-D front analysis for case studies of atmospheric dynamics and forecasting. Our technique builds on a recent gradient-based detection approach, combined with modern 3-D interactive visual analysis techniques, all integrated into the open-source meteorological visualization framework Met.3D. Comparison of detected 3-D frontal structures with 2-D fronts from surface analysis charts of weather services show agreement and augment the surface charts by additional vertical information. In our presentation, we show case studies of extratropical cyclones and their frontal dynamics. Examples include joint interactive visual analysis of 3-D fronts and warm conveyor belt trajectories, and development of the 3-D frontal structure of the characteristic stages of a Shapiro-Keyser cyclone. We also demonstrate the benefit of our technique for comparative analysis of frontal dynamics in different numerical weather prediction model simulations, e.g., of different resolution and simulations with parameterised and permitted convection. We argue that the presented approach has large potential to be beneficial for complex studies of atmospheric dynamics and for operational weather forecasting.

Ensemble forecasting has become a standard means to obtain information about forecast uncertainties in meteorological centres across the world. The large datasets generated by ensemble prediction systems carry much information that is difficult to analyse manually – here, techniques from the field of artificial intelligence can be beneficial to aid the analysis. Cluster analysis is one commonly used (unsupervised machine learning) approach to automatically determine distinct scenarios in numerical weather forecasting ensembles, both in atmospheric research and operational forecasting. Typically, a cluster analysis focusses on a selected meteorological forecast variable, a specific region, and time (or a time window). The dimensionality of the data is reduced by techniques like principal component analysis, and a clustering algorithm – typically k-means – is applied to the reduced data set. Challenges with such an approach arise through the determined clusters often being sensitive to factors including the selected region, forecast variable, and algorithm parameters, and also through the employed algorithms often appearing as a “black box” to the user. In our work, we attempt to make the clustering process more transparent by providing a visual analysis framework to analyse the sensitivity of generated clusters with respect to various factors. The presented framework is coupled to the open-source meteorological ensemble visualization software Met.3D, allowing for interactive specification of clustering parameters and for interactive visual analysis, including 3-D elements. A case study using ensemble prediction data of sudden stratospheric warmings (SSWs) is presented, demonstrating how visualizing similarity between clusterings with different parameters can aid the interpretation of the data.

Potential vorticity (PV) analysis plays a central role in studying atmospheric dynamics and in particular in studying the life cycle of weather systems. The three-dimensional (3-D) structure and temporal evolution of the associated PV anomalies, however, are not yet fully understood. An automated technique to objectively identify 3-D PV anomalies can help to shed light on 3-D atmospheric dynamics in specific case studies, as well as facilitate statistical evaluations within climatological studies. Such a technique to identify PV anomalies fully in 3-D, however, does not yet exist.This study presents a novel algorithm for the objective identification of PV anomalies. The algorithm is inspired by morphological image processing techniques and can be applied to both two-dimensional (2-D) and 3-D fields on vertically isentropic levels.The method maps input data to a horizontally stereographic projection and relies on an efficient computation of horizontal distances within the projected field. Candidates for PV anomaly features are filtered according to heuristic criteria, and feature description vectors are obtained for further analysis. The generated feature descriptions are well suited for subsequent case studies of 3-D atmospheric dynamics, or for generation of climatologies of feature characteristics.We evaluate our approach by comparison with an existing 2-D technique, and demonstrate the full 3-D perspective by means of meteorological case studies comprising tropical cyclogenesis and a subtropical extreme rainfall event. These case studies demonstrate the complexity and variations in the 3-D structure of the detected PV anomalies. Such anomalies are often insufficiently captured by a 2-D method. We discuss further advantages of using a 3-D approach, including elimination of temporal inconsistencies in the detected features due to 3-D structural variation, and elimination of the need to manually select a specific isentropic level on which the anomalies are assumed to be best captured.

This article surveys the history and current state of the art of visualization in meteorology, focusing on visualization techniques and tools used for meteorological data analysis. We examine characteristics of meteorological data and analysis tasks, describe the development of computer graphics methods for visualization in meteorology from the 1960s to today, and visit the state of the art of visualization techniques and tools in operational weather forecasting and atmospheric research. We approach the topic from both the visualization and the meteorological side, showing visualization techniques commonly used in meteorological practice, and surveying recent studies in visualization research aimed at meteorological applications. Our overview covers visualization techniques from the fields of display design, 3D visualization, flow dynamics, feature-based visualization, comparative visualization and data fusion, uncertainty and ensemble visualization, interactive visual analysis, efficient rendering, and scalability and reproducibility. We discuss demands and challenges for visualization research targeting meteorological data analysis, highlighting aspects in demonstration of benefit, interactive visual analysis, seamless visualization, ensemble visualization, 3D visualization, and technical issues.

We inspect the 3-D structure of Marine Cold Air Outbreaks (MCAOs) and Polar Lows (PLs) in ERA5 data with the aim of improving diagnostic indices to capture these weather events in long-term assessments on seasonal and climatological time-scales. For this study, we designed a workflow that starts with the interactive 3-D visual exploration of single MCAO and PL events, using an extended version of the open-source visualization framework Met.3D, followed by the design and statistical testing of new diagnostic indices in long-term assessments. Results from the interactive visual data exploration provide insights into the complex 3-D shape and dynamics of MCAOs and PLs in ERA5 data. Motivated by the visual analysis of single cases, we extend widely-used diagnostics by conceptualizing a simple index to capture the vertical extent of the lower-level instability induced by MCAOs. Testing the association of diagnostic indices with observed PLs in the Barents and the Nordic Seas (STARS data) shows that, the new MCAO index introduced here has an important advantage: it is a more skillful indicator for distinguishing the times and locations of PLs, compared with previously-used indices. We thus propose the new index for further analyses in seasonal climate predictions and climatological studies. The methods for interactive 3-D visual data analysis presented here are available as a generic open-source tool for investigating atmospheric processes in ERA5 and other gridded meteorological data.

We present the application of interactive 3-D visual analysis techniques using the open-source meteorological visualization framework Met.3D [1] for investigating ERA5 reanalysis data. Our focus lies on inspecting atmospheric conditions favoring the development of extreme weather events in the Arctic. Marine Cold Air Outbreaks (MCAOs) and Polar Lows (PLs) are analyzed with the aim of improving diagnostic indices for capturing extreme weather events in seasonal and climatological assessments. We adopt an integrated workflow starting with the interactive visual exploration of single MCAO and PL events, using an extended version of Met.3D, followed by the design and testing of new diagnostic indices in a climatological assessment. Our interactive visual exploration provides insights into the complex 3-D shape and dynamics of MCAOs and PLs. For instance, we reveal a slow wind eye of a PL that extends from the surface up into the stratosphere. Motivated by the interactive visual analysis of single cases of MCAOs, we design new diagnostic indices, which address shortcomings of previously used indices, by capturing the vertical extent of the lower-level static instability induced by MCAOs. The new indices are tested by comparison with observed PLs in the Barents and the Nordic Seas (as reported in the STARS data set). Results show that the new MCAO index introduced here has an important advantage compared with previously used MCAO indices: it is more successful in indicating the times and locations of PLs. We thus propose the new index for further analyses in seasonal climate predictions and climatological studies. The methods for interactive 3-D visual data analysis presented here are made freely available for public use as part of the open-source tool Met.3D. We thereby provide a generic tool that can be used for investigating atmospheric processes in ERA5 data by means of interactive 3-D visual data analysis. Met.3D can be used, for example, during an initial explorative phase of scientific workflows, as a complement to standard 2-D plots, and for detailed meteorological case-analyses in 3-D. [1] http://met3d.wavestoweather.de, https://collaboration.cen.uni-hamburg.de/display/Met3D/

Atmospheric fronts, a widely used conceptual model in meteorology, describe sharp boundaries between two air masses of different thermal properties. In the mid-latitudes, these sharp boundaries are commonly associated with extratropical cyclones. The passage of a frontal system is accompanied by significant weather changes, and therefore fronts are of particular interest in weather forecasting. Over the past decades, several two-dimensional, horizontal feature detection methods to objectively identify atmospheric fronts in numerical weather prediction (NWP) data were proposed in the literature (e.g. Hewson, Met.Apps. 1998). In addition, recent research (Kern et al., IEEE Trans. Visual. Comput. Graphics, 2019) has shown the feasibility of detecting atmospheric fronts as three-dimensional surfaces representing the full 3D frontal structure. In our work, we build on the studies by Hewson (1998) and Kern et al. (2019) to make front detection usable for forecasting purposes in an interactive 3D visualization environment. We consider the following aspects: (a) As NWP models evolved in recent years to resolve atmospheric processes on scales far smaller than the scale of midlatitude-cyclone- fronts, we evaluate whether previously developed detection methods are still capable to detect fronts in current high-resolution NWP data. (b) We present integration of our implementation into the open-source “Met.3D” software (http://met3d.wavestoweather.de) and analyze two- and three-dimensional frontal structures in selected cases of European winter storms, comparing different models and model resolution. (c) The considered front detection methods rely on threshold parameters, which mostly refer to the magnitude of the thermal gradient within the adjacent frontal zone - the frontal strength. If the frontal strength exceeds the threshold, a so-called feature candidate is classified as a front, while others are discarded. If a single, fixed, threshold is used, unwanted “holes” can be observed in the detected fronts. Hence, we use transparency mapping with fuzzy thresholds to generate continuous frontal features. We pay particular attention to the adjustment of filter thresholds and evaluate the dependence of thresholds and resolution of the underlying data.

Ensemble forecasting has become a standard practice in numerical weather prediction in forecasting centres across the world. The large data sets generated by ensemble forecasting systems carry much information, that is difficult to analyse in short time periods, requiring well-designed workflows in order to be useful. Clustering is one of the ensemble analysis methods that are applied to discover similarities between ensemble members. Cluster analysis involves different steps like dimensionality reduction, core clustering algorithm and evaluation. A large of number of methods have been proposed in the literature for each of these steps, however, only few have been applied to clustering of ensemble forecasts. A major challenge is that for a given ensemble forecast, different choices of methods and data domains can lead to very different clustering results. For example, Kumpf et al. (2018, IEEE Transact. Vis. Comp. Graph.) have demonstrated the sensitivity of clustering results to even small changes in the considered domain. The challenge equally exists for choices in clustering methods and method parameters.In our work, we are attempting to open up the clustering black box by introducing a visualization workflow that makes transparent to the user how different choices in methods and method parameters lead to different clustering results. To achieve this, a clustering analysis library that works in tandem with the ensemble visualization software “Met.3D” () is being developed. We present the current state of the system and demonstrate its use by analysing an ensemble forecast case study.

Over the North Atlantic, the frequency of extreme weather events, such as storms or cold spells, is critically dependent on the prevailing weather regime. In consequence, seasonal predictability of these regimes is important. Currently, the ability of seasonal prediction systems to predict such weather regimes over Europe is limited. Weather regimes and the location of the northern hemisphere polar jet stream, hereinafter referred to as jet stream, interact with each other. Specific weather regimes are associated with a northern, central or southern position of the jet stream. Therefore, we investigate whether the relationship between weather regimes and the location of the jet stream can be used to improve seasonal winter forecasts over Europe. For our analysis, we use a seasonal prediction system based on the Max-Planck-Institute Earth-System- Model (MPI-ESM) and investigate a 30-member ensemble, as well as the global reanalysis ERA-Interim as an observational reference. Our results show that the jet stream’s latitude is predictable per winter month with a seasonal prediction system. We also demonstrate in ERA-Interim that weather regime clusters can be directly identified via the jet stream’s position by using k-mean clustering with monthly data. Moreover our results show that the MPI-ESM reforecast ensemble represents the spatial and temporary variability of these clusters. We analyse whether predictive skill can be improved if the number of clusters represented within the reforecast ensemble at a given time is reduced. Specifically, we test whether the incorporation of the location of the jet stream into the prediction analysis improves the prediction skill of sea level pressure and Z500 in the North Atlantic area.

Visualization of numerical weather prediction data and atmospheric observations has always been an important and ubiquitous tool in weather forecasting. Visualization research has made much progress in recent years, in particular with respect to techniques for ensemble data, interactivity, 3D depiction, and feature-detection. Transfer of new techniques into weather forecasting, however, is slow.In this contribution, we will discuss the potential of recent developments in 3D and ensemble visualization research for weather forecasting. We will introduce our work on 3D feature-detection methods for jet-stream and front features, which facilitate analysis of the evolution of jet-stream core lines and frontal surfaces in an (ensemble) forecast. The techniques have been integrated into the 3D visual ensemble analysis framework Met.3D (https://met3d.wavestoweather.de), in which they can be combined with traditional 2D depictions as well as further 3D visual elements and be displayed in an interactive 3D context. We will present and discuss 3D ensemble forecast products created with Met.3D based on forecast data from ECMWF and DWD, and demonstrate their use in the exploration of example cases including an extratropical transition over the North Atlantic and a European winter storm.In addition, we will introduce new semi-operational 3D forecast products based on our techniques that we provide experimentally on the web, in order to gather user feedback and to initiate discussion about potential benefit of such products for operations.

Visualization is an important and ubiquitous tool in the daily work of atmospheric researchers and weather forecasters to analyse data from simulations and observations. Visualization research has made much progress in recent years, in particular with respect to techniques for ensemble data, interactivity, 3D depiction, and feature-detection. Transfer of new techniques into the atmospheric sciences, however, is slow.Met.3D (https://met3d.wavestoweather.de) is an open-source research software aiming at making novel interactive 3D and ensemble visualization techniques accessible to the atmospheric community. Since its first public release in 2015, Met.3D has been used in multiple visualization research projects targeted at atmospheric science applications, and also has evolved into a feature-rich visual analysis tool facilitating rapid exploration of atmospheric simulation data. The software is based on the concept of “building a bridge” between “traditional” 2D visual analysis techniques and interactive 3D techniques and allows users to analyse their data using combinations of 2D maps and cross-sections, meteorological diagrams and 3D techniques including direct volume rendering, isosurfaces and trajectories, all combined in an interactive 3D context.This PICO will provide an overview of the Met.3D project and highlight recent additions and improvements to the software. We will show several examples of how the combination of 2D and 3D visualization elements in an interactive context can be used to explore atmospheric simulation data, including the analysis of forecast errors, analysis of synoptic-scale features including jet-streams and fronts, and analysis of forecast uncertainty in ensemble forecasts.

In meteorology, cluster analysis is frequently used to determine representative trends in ensemble weather predictions in a selected spatio-temporal region, e.g., to reduce a set of ensemble members to simplify and improve their analysis. Identified clusters (i.e., groups of similar members), however, can be very sensitive to small changes of the selected region, so that clustering results can be misleading and bias subsequent analyses. In this article, we — a team of visualization scientists and meteorologists-deliver visual analytics solutions to analyze the sensitivity of clustering results with respect to changes of a selected region. We propose an interactive visual interface that enables simultaneous visualization of a) the variation in composition of identified clusters (i.e., their robustness), b) the variability in cluster membership for individual ensemble members, and c) the uncertainty in the spatial locations of identified trends. We demonstrate that our solution shows meteorologists how representative a clustering result is, and with respect to which changes in the selected region it becomes unstable. Furthermore, our solution helps to identify those ensemble members which stably belong to a given cluster and can thus be considered similar. In a real-world application case we show how our approach is used to analyze the clustering behavior of different regions in a forecast of “Tropical Cyclone Karl”, guiding the user towards the cluster robustness information required for subsequent ensemble analysis.

For an ensemble of iso-contours in multi-dimensional scalar fields, we present new methods to a) visualize their dominant spatial patterns of variability, and b) to compute the conditional probability of the occurrence of a contour at one location given the occurrence at some other location. We first show how to derive a statistical model describing the contour variability, by representing the contours implicitly via signed distance functions and clustering similar functions in a reduced order space. We show that the spatial patterns of the ensemble can then be derived by analytically transforming the boundaries of a confidence interval computed from each cluster into the spatial domain. Furthermore, we introduce a mathematical basis for computing correlations between the occurrences of iso-contours at different locations. We show that the computation of these correlations can be posed in the reduced order space as an integration problem over a region bounded by four hyper-planes. To visualize the derived statistical properties we employ a variant of variability plots for streamlines, now including the color coding of probabilities of joint contour occurrences. We demonstrate the use of the proposed techniques for ensemble exploration in a number of 2D and 3D examples, using artificial and meteorological data sets.

International audience; Multi-aircraft and ground-based observations were made over the North Atlantic in fall 2016 to investigate the importance of diabatic processes for midlatitude weather.The North Atlantic Waveguide and Downstream Impact Experiment (NAWDEX) explored the impact of diabatic processes on disturbances of the jet stream and their influence on downstream high-impact weather through the deployment of four research aircraft, each with a sophisticated set of remote-sensing and in situ instruments, and coordinated with a suite of ground-based measurements. A total of 49 research flights were performed, including, for the first time, coordinated flights of the four aircraft; the German High Altitude and LOng Range Research Aircraft (HALO), the Deutsches Zentrum für Luft- und Raumfahrt (DLR) Dassault Falcon 20, the French Service des Avions Français Instrumentés pour la Recherche en Environnement (SAFIRE) Falcon 20, and the British Facility for Airborne Atmospheric Measurements (FAAM) BAe 146. The observation period from 17 Sep to 22 Oct 2016 with frequently occurring extratropical and tropical cyclones was ideal to investigate midlatitude weather over the North Atlantic. NAWDEX featured three sequences of upstream triggers of waveguide disturbances, their dynamic interaction with the jet stream, subsequent development, and eventual downstream weather impact on Europe. Examples are presented to highlight the wealth of phenomena that were sampled, the comprehensive coverage and the multi-faceted nature of the measurements. This unique dataset forms the basis for future case studies and detailed evaluations of weather and climate predictions to improve our understanding of diabatic influences on Rossby waves and downstream impact of weather systems affecting Europe.

We present a voxel-based rendering pipeline for large 3D line sets that employs GPU ray-casting to achieve scalable rendering including transparency and global illumination effects that cannot be achieved with GPU rasterization. Even for opaque lines we demonstrate superior rendering performance compared to GPU rasterization of lines, and when transparency is used we can interactively render large amounts of lines that are infeasible to be rendered via rasterization. To achieve this, we propose a direction-preserving encoding of lines into a regular voxel grid, along with the quantization of directions using face-to-face connectivity in this grid. On the regular grid structure, parallel GPU ray-casting is used to determine visible fragments in correct visibility order. To enable interactive rendering of global illumination effects like low-frequency shadows and ambient occlusions, illumination simulation is performed during ray-casting on a level-of-detail (LoD) line representation that considers the number of lines and their lengths per voxel. In this way we can render effects which are very difficult to render via GPU rasterization. A detailed performance and quality evaluation compares our approach to rasterization-based rendering of lines.

Jet-streams, their core lines and their role in atmospheric dynamics have been subject to considerable meteorological research since the first half of the twentieth century. Yet, until today no consistent automated feature detection approach has been proposed to identify jet-stream core lines from 3D wind fields. Such 3D core lines can facilitate meteorological analyses previously not possible. Although jet-stream cores can be manually analyzed by meteorologists in 2D as height ridges in the wind speed field, to the best of our knowledge no automated ridge detection approach has been applied to jet-stream core detection. In this work, we -a team of visualization scientists and meteorologists-propose a method that exploits directional information in the wind field to extract core lines in a robust and numerically less involved manner than traditional 3D ridge detection. For the first time, we apply the extracted 3D core lines to meteorological analysis, considering real-world case studies and demonstrating our method's benefits for weather forecasting and meteorological research.

We propose a new approach for analyzing the temporal growth of the uncertainty in ensembles of weather forecasts which are started from perturbed but similar initial conditions. As an alternative to traditional approaches in meteorology, which use juxtaposition and animation of spaghetti plots of iso-contours, we make use of contour clustering and provide means to encode forecast dynamics and spread in one single visualization. Based on a given ensemble clustering in a specified time window, we merge clusters in time-reversed order to indicate when and where forecast trajectories start to diverge. We present and compare different visualizations of the resulting time-hierarchical grouping, including space-time surfaces built by connecting cluster representatives over time, and stacked contour variability plots. We demonstrate the effectiveness of our visual encodings with forecast examples of the European Centre for Medium-Range Weather Forecasts, which convey the evolution of specific features in the data as well as the temporally increasing spatial variability.

We present a web service based tool for the planning of atmospheric research flights. The tool provides online access to horizontal maps and vertical cross-sections of numerical weather prediction data and in particular allows the interactive design of a flight route in direct relation to the predictions. It thereby fills a crucial gap in the set of currently available tools for using data from numerical atmospheric models for research flight planning. A distinct feature of the tool is its lightweight, web service based architecture, requiring only commodity hardware and a basic Internet connection for deployment. Access to visualisations of prediction data is achieved by using an extended version of the Open Geospatial Consortium Web Map Service (WMS) standard, a technology that has gained increased attention in meteorology in recent years. With the WMS approach, we avoid the transfer of large forecast model output datasets while enabling on-demand generated visualisations of the predictions at campaign sites with limited Internet bandwidth. Usage of the Web Map Service standard also enables access to third-party sources of georeferenced data. We have implemented the software using the open-source programming language Python. In the present article, we describe the architecture of the tool. As an example application, we discuss a case study research flight planned for the scenario of the 2010 Eyjafjalla volcano eruption. Usage and implementation details are provided as Supplement.

Airborne lidar and in-situ measurements of aerosols and trace gases were performed in volcanic ash plumes over Europe between Southern Germany and Iceland with the Falcon aircraft during the eruption period of the Eyjafjalla volcano between 19 April and 18 May 2010. Flight planning and measurement analyses were supported by a refined Meteosat ash product and trajectory model analysis. The volcanic ash plume was observed with lidar directly over the volcano and up to a distance of 2700 km downwind, and up to 120 h plume ages. Aged ash layers were between a few 100 m to 3 km deep, occurred between 1 and 7 km altitude, and were typically 100 to 300 km wide. Particles collected by impactors had diameters up to 20 μm diameter, with size and age dependent composition. Ash mass concentrations were derived from optical particle spectrometers for a particle density of 2.6 g cm−3 and various values of the refractive index (RI, real part: 1.59; 3 values for the imaginary part: 0, 0.004 and 0.008). The mass concentrations, effective diameters and related optical properties were compared with ground-based lidar observations. Theoretical considerations of particle sedimentation constrain the particle diameters to those obtained for the lower RI values. The ash mass concentration results have an uncertainty of a factor of two. The maximum ash mass concentration encountered during the 17 flights with 34 ash plume penetrations was below 1 mg m−3. The Falcon flew in ash clouds up to about 0.8 mg m−3 for a few minutes and in an ash cloud with approximately 0.2 mg m−3 mean-concentration for about one hour without engine damage. The ash plumes were rather dry and correlated with considerable CO and SO2 increases and O3 decreases. To first order, ash concentration and SO2 mixing ratio in the plumes decreased by a factor of two within less than a day. In fresh plumes, the SO2 and CO concentration increases were correlated with the ash mass concentration. The ash plumes were often visible slantwise as faint dark layers, even for concentrations below 0.1 mg m−3. The large abundance of volatile Aitken mode particles suggests previous nucleation of sulfuric acid droplets. The effective diameters range between 0.2 and 3 μm with considerable surface and volume contributions from the Aitken and coarse mode aerosol, respectively. The distal ash mass flux on 2 May was of the order of 500 (240–1600) kg s−1. The volcano induced about 10 (2.5–50) Tg of distal ash mass and about 3 (0.6–23) Tg of SO2 during the whole eruption period. The results of the Falcon flights were used to support the responsible agencies in their decisions concerning air traffic in the presence of volcanic ash.

Lineshaped contrails were detected with the research aircraft Falcon during the CONCERT – CONtrail and Cirrus ExpeRimenT – campaign in October/November 2008. The Falcon was equipped with a set of instruments to measure the particle size distribution, shape, extinction and chemical composition as well as trace gas mixing ratios of sulfur dioxide (SO2), reactive nitrogen and halogen species (NO, NOy, HNO3, HONO, HCl), ozone (O3) and carbon monoxide (CO). During 12 mission flights over Europe, numerous contrails, cirrus clouds and a volcanic aerosol layer were probed at altitudes between 8.5 and 11.6 km and at temperatures above 213 K. 22 contrails from 11 different aircraft were observed near and below ice saturation. The observed NO mixing ratios, ice crystal and soot number densities are compared to a process based contrail model. On 19 November 2008 the contrail from a CRJ-2 aircraft was penetrated in 10.1 km altitude at a temperature of 221 K. The contrail had mean ice crystal number densities of 125 cm−3 with effective radii reff of 2.6 μm. The presence of particles with r>50 μm in the less than 2 min old contrail suggests that natural cirrus crystals were entrained in the contrail. Mean HONO/NO (HONO/NOy) ratios of 0.037 (0.024) and the fuel sulfur conversion efficiency to H2SO4 (εS↓) of 2.9 % observed in the CRJ-2 contrail are in the range of previous measurements in the gaseous aircraft exhaust. On 31 October 2010 aviation NO emissions could have contributed by more than 40% to the regional scale NO levels in the mid-latitude lowest stratosphere. The CONCERT observations help to better quantify the climate impact from contrails and will be used to investigate the chemical processing of trace gases on contrails.

We present the application of interactive 3-D visualization of ensemble weather predictions to forecasting warm conveyor belt situations during aircraft-based atmospheric research campaigns. Motivated by forecast requirements of the T-NAWDEX-Falcon 2012 campaign, a method to predict 3-D probabilities of the spatial occurrence of warm conveyor belts has been developed. Probabilities are derived from Lagrangian particle trajectories computed on the forecast wind fields of the ECMWF ensemble prediction system. Integration of the method into the 3-D ensemble visualization tool Met.3D, introduced in the first part of this study, facilitates interactive visualization of WCB features and derived probabilities in the context of the ECMWF ensemble forecast. We investigate the sensitivity of the method with respect to trajectory seeding and forecast wind field resolution. Furthermore, we propose a visual analysis method to quantitatively analyse the contribution of ensemble members to a probability region and, thus, to assist the forecaster in interpreting the obtained probabilities. A case study, revisiting a forecast case from T-NAWDEX-Falcon, illustrates the practical application of Met.3D and demonstrates the use of 3-D and uncertainty visualization for weather forecasting and for planning flight routes in the medium forecast range (three to seven days before take-off).

In 2008 Mount Okmok and Mount Kasatochi started erupting on 12 July and 7 August, respectively, in the Aleutians, depositing emissions of trace gases and aerosols as high as 15.2 km into the atmosphere. During an aircraft campaign, conducted over Europe in between 27 October and 2 November 2008, the volcanic aerosol was measured by an Aerodyne aerosol mass spectrometer, capable of particle chemical composition measurements covering a size diameter range between 40 nm and 1 mm. In the volcanic aerosol layer enhanced submicron particulate sulfate concentrations of up to 2.0 mg m−3 standard temperature and pressure (STP) were observed between 8 and 12 km altitude, while background values did not exceed 0.5 mg m−3 (STP). Twenty‐one percent of the volcanic aerosol consisted of carbonaceous material that increased by a factor of 1.9 in mass compared to the free troposphere. Enhanced gaseous sulfur dioxide concentrations measured by an ion trap chemical ionization mass spectrometer of up to 1.3 mg m−3 were encountered. An onboard radiation measurement system simultaneously detected an enhanced aerosol signal. Furthermore, two German lidar stations identified an aerosol layer before and after the campaign. Data analysis shows that the aerosol layer was observed mainly in the lowermost stratosphere. Correlation of particulate sulfate concentration and sulfur dioxide mixing ratios indicates that after a 3 month residence time in the stratosphere, not all sulfur dioxide has been converted into sulfate aerosol. The significant fraction of organic material might have implications on heterogeneous chemistry in the stratosphere, which need to be explored more thoroughly.

The frequency of occurrence of cirrus clouds and contrails, their life time, ice crystal size spectra and thus their radiative properties depend strongly on the ambient distribution of the relative humidity with respect to ice (RHice). Ice clouds do not form below a certain supersaturation and both cirrus and contrails need at least saturation conditions to persist over a longer period. Under subsaturated conditions, cirrus and contrails should dissipate. During the mid-latitude aircraft experiment CONCERT 2008 (CONtrail and Cirrus ExpeRimenT), RHice and ice crystals were measured in cirrus and contrails. Here, we present results from 2.3/1.7 h of observation in cirrus/contrails during 6 flights. Thin and subvisible cirrus with contrails embedded therein have been detected frequently in a subsaturated environment. Nevertheless, ice crystals up to radii of 50 μm and larger, but with low number densities were often observed inside the contrails as well as in the cirrus. Analysis of the meteorological situation indicates that the crystals in the contrails were entrained from the thin/subvisible cirrus clouds, which emerged in frontal systems with low updrafts. From model simulations of cirrus evaporation times it follows that such thin/subvisible cirrus can exist for time periods of a couple of hours and longer in a subsaturated environment and thus may represent a considerable part of the cirrus coverage.

Atmospheric fronts play a central role in meteorology, as the boundaries between different air masses and as fundamental features of extra-tropical cyclones. They appear in numerous conceptual model depictions of extra-tropical weather systems. Conceptually, fronts are three-dimensional surfaces in space possessing an innate structural complexity, yet in meteorology, both manual and objective identification and depiction have historically focused on the structure in two dimensions. In this work, we –a team of visualization scientists and meteorologists – propose a novel visualization approach to analyze the three-dimensional structure of atmospheric fronts and related physical and dynamical processes. We build upon existing approaches to objectively identify fronts as lines in two dimensions and extend these to obtain frontal surfaces in three dimensions, using the magnitude of temperature Change along the gradient of a moist potential temperature field as the primary identifying factor. We introduce the use of normal curves in the temperature gradient field to visualize a frontal zone (i.e., the transitional zone between the air masses) and the distribution of atmospheric variables in such zones. To enable for the first time a statistical analysis of frontal zones, we present a new approach to obtain the volume enclosed by a zone, by classifying grid boxes that intersect with normal curves emanating from a selected front. We introduce our method by means of an idealized numerical simulation and demonstrate its use with two real-world cases using numerical weather prediction data.