The three-dimensional structure of fronts in mid-latitude weather systems as represented by 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 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.

Identification of high-wind features within extratropical cyclones using a probabilistic random forest – Part 1: Method and case studies

“…For this, analysis charts of the DWD and the UK Met Office were used for orientation. For a more detailed analysis, we also used the 3D front detection of the Met.3D interactive visualisation software (Beckert et al, 2022;Rautenhaus et al, 2015). In our surface parameters, a cold front is then mostly identified through the characteristic change of the sign of p. It is labelled CFC if a larger area of precipitation along it is observed, while high winds ahead of the front within the warm sector are labelled WJ.…”

“…Figure D4. Detected fronts at 850 hPa (green tubes) and wet-bulb potential temperature θ W (shading) for storm Burglind on 3 January 2018 at 06:00 UTC using the Met.3D visualisation software (Beckert et al, 2022;Rautenhaus et al, 2015). Data source: ERA5.…”

“…2007; Fink et al, 2009). The development of extratropical cyclones is usually portrayed on the basis of the Norwegian cyclone (NC; Bjerknes, 1919) or the Shapiro-Keyser cyclone (SKC; Shapiro and Keyser, 1990) model. Both types of cyclones evolve along a frontal wave and include the formation of a warm front followed by a cold front with a warm sector in between.…”

Objective identification of high-wind features within extratropical cyclones using a probabilistic random forest (RAMEFI). Part I: Method and illustrative case studies

“…For this, analysis charts of the DWD and the UK Met Office were used for orientation. For a more detailed analysis, we also used the 3D front detection of the Met.3D interactive visualisation software (Beckert et al, 2022;Rautenhaus et al, 2015). In our surface parameters, a cold front is then mostly identified through the characteristic change of the sign of p. It is labelled CFC if a larger area of precipitation along it is observed, while high winds ahead of the front within the warm sector are labelled WJ.…”

“…2007; Fink et al, 2009). The development of extratropical cyclones is usually portrayed on the basis of the Norwegian cyclone (NC; Bjerknes, 1919) or the Shapiro-Keyser cyclone (SKC; Shapiro and Keyser, 1990) model. Both types of cyclones evolve along a frontal wave and include the formation of a warm front followed by a cold front with a warm sector in between.…”