Three-Dimensional Visualization of Ensemble Weather Forecasts – Part 1: the Visualization Tool Met.3D (Version 1.0)

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Three-Dimensional Visualization of Ensemble Weather Forecasts – Part 1: the Visualization Tool Met.3D (Version 1.0) Geosci. Model Dev., 8, 2329–2353, 2015 www.geosci-model-dev.net/8/2329/2015/ doi:10.5194/gmd-8-2329-2015 © Author(s) 2015. CC Attribution 3.0 License. Three-dimensional visualization of ensemble weather forecasts – Part 1: The visualization tool Met.3D (version 1.0) M. Rautenhaus1, M. Kern1, A. Schäfler2, and R. Westermann1 1Computer Graphics & Visualization Group, Technische Universität München, Garching, Germany 2Deutsches Zentrum für Luft- und Raumfahrt, Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany Correspondence to: M. Rautenhaus ([email protected]) Received: 4 February 2015 – Published in Geosci. Model Dev. Discuss.: 27 February 2015 Revised: 23 June 2015 – Accepted: 25 June 2015 – Published: 31 July 2015 Abstract. We present “Met.3D”, a new open-source tool for 1 Introduction the interactive three-dimensional (3-D) visualization of nu- merical ensemble weather predictions. The tool has been de- Weather forecasting requires meteorologists to explore large veloped to support weather forecasting during aircraft-based amounts of numerical weather prediction (NWP) data, and to atmospheric field campaigns; however, it is applicable to fur- assess the uncertainty of the predictions. Visualization meth- ther forecasting, research and teaching activities. Our work ods that facilitate fast and intuitive exploration of the data approaches challenging topics related to the visual analysis hence are of particular importance. In practice, the forecast- of numerical atmospheric model output – 3-D visualization, ing process for the most part relies on two-dimensional (2-D) ensemble visualization and how both can be used in a mean- visualization methods. Meteorologists use weather maps, ingful way suited to weather forecasting. Met.3D builds a vertical cross sections and a multitude of meteorological di- bridge from proven 2-D visualization methods commonly agrams to depict the data. From these image sources, they used in meteorology to 3-D visualization by combining both build “mental models” of the three-dimensional (3-D), time- visualization types in a 3-D context. We address the issue of varying forecast atmosphere inside their heads (Hoffman and spatial perception in the 3-D view and present approaches to Coffey, 2004; Trafton and Hoffman, 2007). using the ensemble to allow the user to assess forecast un- Despite the 3-D nature of the atmosphere, 3-D visualiza- certainty. Interactivity is key to our approach. Met.3D uses tion methods have not found widespread usage, even though modern graphics technology to achieve interactive visual- there have been promising attempts in the 1990s and early ization on standard consumer hardware. The tool supports 2000s that suggested added value (Treinish and Rothfusz, forecast data from the European Centre for Medium Range 1997; Koppert et al., 1998; McCaslin et al., 2000). Various Weather Forecasts (ECMWF) and can operate directly on hindering factors are discussed in the literature, including ECMWF hybrid sigma-pressure level grids. We describe the resistance of forecasters to adapt to new 3-D visualization employed visualization algorithms, and analyse the impact methods that are decoupled from their “familiar” 2-D prod- of the ECMWF grid topology on computing 3-D ensemble ucts (Koppert et al., 1998; Szoke et al., 2003), problems with statistical quantities. Our techniques are demonstrated with spatial perception in 3-D renderings (Szoke et al., 2003), as examples from the T-NAWDEX-Falcon 2012 (THORPEX – well as issues due to limited performance (Treinish and Roth- North Atlantic Waveguide and Downstream Impact Experi- fusz, 1997) and the need for dedicated graphics workstation ment) campaign. hardware (Koppert et al., 1998). In addition to 3-D space and time, forecast visualization has in recent years become more challenging through the in- creased use of ensemble weather predictions – sets of fore- cast runs whose distribution provides information on fore- cast uncertainty (e.g. Gneiting and Raftery, 2005; Leutbecher and Palmer, 2008). The development of visualization meth- Published by Copernicus Publications on behalf of the European Geosciences Union. 2330 M. Rautenhaus et al.: Three-dimensional visualization of ensemble weather forecasts – Part 1: Met.3D ods that depict the uncertainty derived from ensemble data a targeted atmospheric process or feature can be de- is an active topic of research not only for weather forecast rived. Potential flight routes can be planned in regions in ensembles (Obermaier and Joy, 2014). Yet again, ensemble which the probability is high. An open question, how- visualization techniques related to weather forecasting pub- ever, is how can the ensemble data be visualized to im- lished so far mainly focus on two dimensions as well (e.g. prove flight planning in the medium forecast range. Potter et al., 2009; Sanyal et al., 2010). In this paper we introduce a new open-source visualiza- Our objective is to use interactive 3-D visualization of en- tion tool, “Met.3D”, that provides interactive 3-D visual- semble predictions from the European Centre for Medium ization techniques for ensemble prediction data. There has Range Weather Forecasts (ECMWF) to improve the forecast been an immense progress in mainstream graphics hardware process for field campaigns. The work has been stimulated capabilities in recent years. Making use of these develop- by the forecast requirements of a specific field campaign, the ments, Met.3D facilitates interactive visualization of present- international T-NAWDEX-Falcon campaign (THORPEX – day NWP data sets on consumer hardware. The tool has been North Atlantic Waveguide and Downstream Impact Exper- developed as a new effort to demonstrate the feasibility of iment – Falcon, hereafter TNF). TNF took place in Octo- using 3-D visualization for forecasting, this time also con- ber 2012 with the objective to take in situ measurements in sidering uncertainty information from ensemble data sets. It warm conveyor belts (WCBs), airstreams in extratropical cy- is intended to be used for actual forecasting tasks, as well as clones that lift warm and moist air from near the surface to a platform to implement and evaluate new 3-D and ensemble the upper troposphere (e.g. Browning and Roberts, 1994). visualization techniques. Schäfler et al.(2014) provided details on the campaign and The work presented in this paper has been inspired by its flight planning. The major forecasting challenge was to a particular application, forecasting the weather situation to predict the likelihood of WCB occurrence within aircraft plan research flights during aircraft-based field campaigns. range. This was expressed by a number of forecast questions We focus on this application throughout the paper at hand. that guided the development of Met.3D: However, Met.3D is applicable to a broader range of fore- A. How will the large-scale weather situation develop over casting and visual data analysis tasks. Both fast exploration the next week, and will conditions occur that favour and uncertainty assessment play a major role in campaign WCB formation? forecasting: 1. When investigating suitable meteorological conditions B. How uncertain are the weather predictions? to specify the route of a research flight (that is, way- C. Where and when, in the medium forecast range and points in 3-D space and time), the forecaster is re- within the spatial range of the aircraft, is a WCB most quired to quickly identify atmospheric features relevant likely to occur? to the flight and to communicate findings to colleagues. Upper-level features typically important to flights with D. How meaningful is the forecast of WCB occurrence? high-flying aircraft are of an inherently 3-D nature (for example, clouds, jet streams or the tropopause). E. Where will the WCB be located relative to cyclonic and From our experience in campaigns with DLR (German dynamic features? Aerospace Centre) involvement, visualization used dur- In a recent ECMWF newsletter article (Rautenhaus et al., ing campaigns has been solely based on 2-D methods, 2014), we provided a brief overview of our work. It is the pur- typically with limited interactivity. We are hence inter- pose of this publication to describe the techniques we have ested in investigating how 3-D visualization methods developed in detail and to present our solutions to particular and interactivity (to quickly navigate the data space) can challenges. be used to aid the exploration. We split our work into two parts, structured as follows. In 2. Assessing the forecast’s uncertainty has become indis- this paper, we introduce Met.3D. We discuss challenges re- pensable as flights frequently have to be planned multi- lated to interactive 3-D visualization and present techniques ple days before take off (typically 3–7 days; the medium that address questions A and B. forecast range) to obtain the required approval from To put our work in the context of the literature, we review air traffic authorities. While the use of ensemble pre- recent work in meteorological and ensemble visualization in dictions has been reported for recent field campaigns Sect.2. Section3 presents Met.3D’s visualization capabili- (e.g. Wulfmeyer et al., 2008; Elsberry and Harr, 2008; ties. When introducing 3-D visualization to forecasting, we Ducrocq et al., 2014; Vaughan et al., 2015), they have, need to consider that the 2-D visualization methods com- to the best of our knowledge, not been used to create monly used in meteorology provide many advantages (for specific interactive forecast products for flight planning. example, spatial perception) and that meteorologists are used However, ensembles provide valuable information; for to working with them. In a 3-D forecast tool to be used in example, 3-D probability fields for the occurrence of practice, we hence have to be careful not to replace proven Geosci. Model Dev., 8, 2329–2353, 2015 www.geosci-model-dev.net/8/2329/2015/ M. Rautenhaus et al.: Three-dimensional visualization of ensemble weather forecasts – Part 1: Met.3D 2331 2-D methods, but to put them into a 3-D context and to use ferent exploration tasks and data formats.
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