The three-dimensional structure of fronts in mid-latitude weather systems in numerical weather prediction models

Abstract

Abstract. 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 has the feasibility of the objective detection and visual analysis of 3-D frontal structures and their dynamics within numerical weather prediction (NWP) data 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 extra-tropical 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 kilometre-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, as well as identification of the 3-D frontal structures characterizing 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 relation between convection and 3-D cold-front structure by comparing data from simulations with parameterized and explicit convection. 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-dominated but a humidity-dominated 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.

An Appraisal of the Progress in Utilizing Radiosondes and Satellites for Monitoring Upper Air Temperature Profiles

“…Finally, improving the homogeneity, temporal resolution, and accuracy of the upper air temperature records is imperative to make them appropriate for weather forecasting, atmospheric boundary layer, and climate change studies. Significant progress has been made to enhance upper air temperature records acquired utilizing in situ and satellite-based instruments to improve weather forecasting [171], the understanding of atmospheric boundary layer processes [172], and climate change studies [140]. Studies have demonstrated that RSs are still by far the most effective tools for in situ atmospheric temperature observations, but operational meteorological satellites are gradually closing the gap despite their reliance on in situ data for their calibration and validation [77,146,151].…”

Comment on gmd-2024-60

“…The automated detection and tracking of 2-D and 3-D atmospheric features including cyclones, fronts, jet streams, or atmospheric rivers (ARs) in simulation and observation data has multiple applications in meteorology. For example, automatically detected features are used for weather forecasting (e.g., Hewson and Titley, 2010;Mittermaier et al, 2016;Hengstebeck et al, 2018), statistical and climatological studies (e.g., Dawe and Austin, 2012, Pena-Ortiz et al, 2013, Schemm et al, 2015, Sprenger et al, 2017, Lawrence and Manney, 2018, and visual data analysis (e.g., Rautenhaus et al, 2018;Bösiger et al, 2022;Beckert et al 2023). Features are typically objectively detected based on a set of physical and mathematical rules.…”

“…Features are typically objectively detected based on a set of physical and mathematical rules. For example, cyclones can be identified by means of searching for minima or maxima in variables including mean sea level pressure and lower-tropospheric vorticity (Neu et al, 2013;Bourdin et al 2022), atmospheric fronts by means of derivatives of a thermal variable combined with threshold-based filters (Jenkner et al, 2010;Hewson and Titley, 2010;Beckert et al, 2023), and ARs based on thresholding and geometric requirements (Guan and Waliser, 2015;Shields et al, 2018).…”