Three-dimensional visualization of ensemble weather forecasts – Part 1: The visualization tool Met.3D (version 1.0)

Rautenhaus, Marc; Kern, Michael; Schäfler, Andreas; Westermann, Rüdiger

doi:10.5194/gmd-8-2329-2015

Cited by 60 publications

(65 citation statements)

References 73 publications

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“…We want to provide an interface to allow a large number of users with different requirements to obtain useful information for decision making. Many tools are available for meteorologists to gain insight and to communicate the uncertainty in ensembles of forecasts [PWB * 09], [SZD * 10], [HDKS05], [RKSW15], [BGS12]. They proposed informed color encoding for different weather variables and the use of contour plots to display multiple isocontour features across an ensemble.…”

Section: Related Workmentioning

confidence: 99%

Albero: A Visual Analytics Approach for Probabilistic Weather Forecasting

Diehl

Pelorosso

Delrieux³

et al. 2017

Computer Graphics Forum

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Probabilistic weather forecasts are amongst the most popular ways to quantify numerical forecast uncertainties. The analog regression method can quantify uncertainties and express them as probabilities. The method comprises the analysis of errors from a large database of past forecasts generated with a specific numerical model and observational data. Current visualization tools based on this method are essentially automated and provide limited analysis capabilities. In this paper, we propose a novel approach that breaks down the automatic process using the experience and knowledge of the users and creates a new interactive visual workflow. Our approach allows forecasters to study probabilistic forecasts, their inner analogs and observations, their associated spatial errors, and additional statistical information by means of coordinated and linked views. We designed the presented solution following a participatory methodology together with domain experts. Several meteorologists with different backgrounds validated the approach. Two case studies illustrate the capabilities of our solution. It successfully facilitates the analysis of uncertainty and systematic model biases for improved decision-making and process-quality measurements.

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Section: Related Workmentioning

confidence: 99%

Albero: A Visual Analytics Approach for Probabilistic Weather Forecasting

Diehl

Pelorosso

Delrieux³

et al. 2017

Computer Graphics Forum

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“…Met.3D is a three-dimensional visualisation open-source software by Rautenhaus et al (2015). It was initially developed for the Ensemble Prediction System from the European Centre for Medium-Range Weather Forecasts to support weather forecasting during aircraft-based atmospheric field campaigns.…”

Section: Three-dimensional Visualisationmentioning

confidence: 99%

Dynamics of sting‐jet storm Egon over continental Europe: Impact of surface properties and model resolution

Eisenstein

Pantillon

Knippertz

2019

Quart J Royal Meteoro Soc

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Intense Shapiro–Keyser cyclones are often accompanied by a sting jet (SJ), an air stream that descends from the cloud head into the frontal‐fracture region and can cause extreme surface gusts. Previous case‐studies have concentrated on the North Atlantic and the British Isles. Here we present the first‐ever detailed analysis of an SJ over continental Europe and investigate the influence of topography on its dynamical evolution based on observations and high‐resolution simulations using the ICOsahedral Nonhydrostatic model (ICON). Windstorm Egon intensified over the English Channel and then tracked from northern France to Poland on 12–13 January 2017, causing gusts of almost 150 km·h−1 and important damage. ICON reproduces the storm dynamics, although it delays the explosive deepening, shifts the track southward over Belgium and Germany and underestimates gusts over land. Storm characteristics show weak sensitivity to varying grid spacing between 1.6 and 6.5 km, while switching off the convection parametrization at 3.3 km grid spacing improves correlations with surface observations but deteriorates the mean error. Trajectories reveal typical SJ characteristics such as mid‐level descent, strong acceleration and conditional symmetric and other mesoscale instabilities, while evaporative cooling is stronger than in previous cases from the literature, preventing drying during descent. The SJ identification and the occurrence of mesoscale instabilities depend considerably on model resolution, convective parametrization, output frequency and employed thresholds for trajectory selection. Sensitivity experiments with modified surface characteristics show that the combined effects of warm‐air blocking by the Alps, higher roughness over land and reduced surface fluxes cause Egon to fill more quickly and to move on a faster, more northern track across Germany. While the SJ response is complex, showing some compensating effects, surface gusts strongly increase when roughness is reduced. These results suggest that weather forecasters in continental Europe should be more aware of the potential risks associated with SJs.

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“…We have already seen this in Figure 1b. If, on the other hand, graphical elements are added to the terrain rendering, occlusions will occur inevitably [2,14]. To improve the readability of data values, it makes sense to take perceptual issues into account, such as the adjustment of size and orientation of data-encoding objects [15].…”

Section: Presenting Geo-spatial Data In Terrainmentioning

confidence: 99%

Visualizing 3D Terrain, Geo-Spatial Data, and Uncertainty

et al. 2017

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Visualizing geo-spatial data embedded into a three-dimensional terrain is challenging. The problem becomes even more complex when uncertainty information needs to be presented as well. This paper addresses the question of how to visually communicate all three aspects: the 3D terrain, the geo-spatial data, and the data-associated uncertainty. We argue that visualizing all aspects with a high degree of detail will likely exceed the visual budget. Therefore, we propose a visualization strategy based on prioritizing a selected aspect and presenting the remaining two with less detail. We discuss various design options that allow us to obtain differently prioritized visual representations. Our approach has been implemented as a tool for rapid visualization prototyping in the context of avionics applications. Practical solutions are described for a use case related to the visualization of 3D terrain and uncertain weather data.

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Three-dimensional visualization of ensemble weather forecasts – Part 1: The visualization tool Met.3D (version 1.0)

Cited by 60 publications

References 73 publications

Albero: A Visual Analytics Approach for Probabilistic Weather Forecasting

Albero: A Visual Analytics Approach for Probabilistic Weather Forecasting

Dynamics of sting‐jet storm Egon over continental Europe: Impact of surface properties and model resolution

Visualizing 3D Terrain, Geo-Spatial Data, and Uncertainty

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