The predictability of eight southern European tropical‐like cyclones – seven Medicanes and the first‐ever documented case of such a storm in the Bay of Biscay – is studied evaluating European Centre for Medium‐Range Weather Forecasts (ECMWF) operational ensemble forecasts against operational analysis data. Forecast cyclone trajectories are compared with the cyclone trajectory in the analysis by means of a dynamic time warping technique, which allows one to find a match in terms of their overall spatio‐temporal similarity. Each storm is treated as an object and its forecasts are analysed using parameters that describe intensity, symmetry, compactness and upper‐level thermal structure. This object‐based approach allows one to focus on specific storm features, while tolerating their shifts in time and space to some extent. The high compactness and symmetry of the storms are generally poorly predicted, especially at long lead times. However, forecast accuracy tends to improve strongly at short lead times, indicating that the ECMWF ensemble forecast model can adequately reproduce Medicanes, albeit only a few days in advance. In particular, late forecasts which have been initialised when the cyclone has already developed are distinctly more accurate than earlier forecasts in predicting its kinematic and thermal structure, confirming previous findings of high sensitivity of Medicane simulations to initial conditions. Findings reveal a markedly non‐gradual evolution of ensemble forecasts with lead time, which is often far from a progressive convergence towards the analysis value. Specifically, a rapid increase in the probability of cyclone occurrence (a “forecast jump”) is seen in most cases, generally with lead times between 5 and 7 days. Jumps are also found for the forecast distribution of storm thermal structure. This behaviour is consistent with the existence of predictability barriers. On the other hand, storm position forecasts often exhibit a consistent spatial distribution of storm position uncertainty and bias between consecutive forecasts.
Tropical cyclones that evolve from a non-tropical origin may pose a special challenge for predictions, as they often emerge at the end of a multi-scale cascade of atmospheric processes. Climatological studies have shown that the 'tropical transition' (TT) pathway plays a prominent role in cyclogenesis, in particular over the North Atlantic Ocean. Here we use operational European Centre for Medium-Range Weather Forecasts ensemble predictions to investigate the TT of North Atlantic Hurricane Chris (2012), whose formation was preceded by the merger of two potential vorticity (PV) maxima, eventually resulting in the storm-inducing PV streamer. The principal goal is to elucidate the dynamic and thermodynamic processes governing the predictability of cyclogenesis and subsequent TT. Dynamic time warping is applied to identify ensemble tracks that are similar to the analysis track. This technique permits small temporal and spatial shifts in the development. The formation of the pre-Chris cyclone is predicted by those members that also predict the merging of the two PV maxima. The position of the storm relative to the PV streamer determines whether the pre-Chris cyclone follows the TT pathway. The transitioning storms are located inside a favorable region of high equivalent potential temperatures that result from a warm seclusion underneath the cyclonic roll-up of the PV streamer. A systematic investigation of consecutive ensemble forecasts indicates that forecast improvements are linked to specific events, such as the PV merging. The present case exemplifies how a novel combination of Eulerian and Lagrangian ensemble forecast analysis tool allows to infer physical causes of abrupt changes in predictability.
While previous research on sub-seasonal tropical cyclone (TC) occurrence has mostly focused on either the validation of numerical weather prediction (NWP) models, or the development of statistical models trained on past data, the present study combines both approaches to a statistical–dynamical model for probabilistic forecasts in the North Atlantic basin. Although state-of-the-art NWP models have been shown to lack predictive skill with respect to sub-seasonal weekly TC occurrence, they may predict the environmental conditions sufficiently well to generate predictors for a statistical model. Therefore, an extensive predictor set was generated, including predictor groups representing the climatological seasonal cycle (CSC), oceanic, and tropical conditions, tropical wave modes, as well as extratropical influences, respectively. The developed hybrid forecast model is systematically validated for the Gulf of Mexico and Central Main Development Region (MDR) for lead times up to five weeks. Moreover, its performance is compared against a statistical approach trained on past data, as well as against different climatological and NWP benchmarks. For sub-seasonal lead times, the CSC models are found to outperform the NWP models, which quickly loose skill within the first two forecast weeks, even in case of recalibration. The statistical models trained on past data increase skill over the CSC models, whereas even greater improvements in skill are gained by the hybrid approach out to week five. The vast majority of the additional sub-seasonal skill in the hybrid model, relative to the CSC model, could be attributed to the tropical (oceanic) conditions in the Gulf of Mexico (Central MDR).
This paper describes the unprecedented storm Stephanie, which exhibited tropical characteristics over the Bay of Biscay on 15 September 2016. Remote sensing observations reveal a cloud‐free area surrounded by a circular precipitation pattern and an axisymmetric wind field, while buoy observations show an abrupt drop in wind speed during the passage of the storm centre. Model analysis further corroborates an ongoing tropical transition from a frontal cold‐core to a symmetric warm‐core system. By analogy with ‘Medicanes’ (Mediterranean hurricanes), we name this storm a ‘Biscane’ (Biscay hurricane). Weather systems of this kind may become more frequent in a warmer climate.
Abstract. 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.
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