Objective identification of Mediterranean cyclones and their trajectories towards Romania

Abstract

A mathematical algorithm for the automatic detection and tracking of Mediterranean cyclones is presented. The objective of this study is identifying those Mediterranean cyclones that are engaged on various trajectories towards the countries bordering the Black Sea. Mediterranean cyclones with trajectories towards Romania have been identified from a six‐hourly reanalysis of mean sea level pressure (MSLP) data set for a period of 37 years (January 1980–December 2016) within the chosen domain 15 ° W–40 ° E/30 ° N–50 ° N. The cyclone identification was performed using a method known as calculus cyclone identification (CCI), which was adapted for the Mediterranean Sea region. The results obtained using the objective method were compared with the Mediterranean cyclone trajectory data obtained about 55 years ago by Romanian researchers who made use of subjective methods. A summary of the climatology of Mediterranean cyclones affecting Romania was also performed. To demonstrate the accuracy of the objective method adapted for the cyclones engaged on trajectories towards Romania, a subjective analysis using the potential vorticity and geopotential height fields was made. The results confirmed that the method can be successfully implemented by the Romanian Meteorological Service for the detection of Mediterranean cyclones. This method will, therefore, be a useful tool for meteorologists in their forecasting activity.

Recent Changes in Storm Track over the Southeast Europe: A Mechanism for Changes in Extreme Cyclone Variability

“…For the extra tropics, the MSLP or the geostrophic vorticity fields were most often used for these types of studies [52,53]. In addition, for this study the algorithm was verified for the nine main clusters that were found to be representative for the climatology over the target domain [10], by simulating the central cluster event and analyzing the origin, development, and larger-scale context for each (presented in Figure S1).…”

“…The following are available online at https://www.mdpi.com/article/ 10.3390/atmos12101362/s1, Figure S1: Validation of storm track classes from Catrina et al, 2019. Computed trajectories are blue/ red lines for winter/ summer events; plum cross represents the track position from meteorological analysis in (Catrina et al, 2019); orange circle represents secondary cyclonic forcing position present in the same time, interacting with the track, but not identified as cyclone center; orange dot lines indicate larger-scale context previous or during to the track (lead time is written in orange, in hours). Figure S2: Represents the same as in Figure 11 for average months SON.…”

“…Moreover, the region has major teleconnections routes for Europe [3][4][5], being one of the main sources of latent heat [6] and eddy energy enhancement for the mid-latitudes, influencing the weather and climate in Europe [7,8]. Storm track identification often uses subjective/semi-objective [9][10][11] and objective track-computation algorithms, based on various definitions of cyclone centers and tracking criteria [12][13][14][15]. Using these techniques, a significant amount of work has been devoted to storm-track classification [16][17][18] and analysis.…”

“…Here, we use a tracking model [48] developed based on the mean sea level pressure, which was calibrated and validated (Figure S1) for the main track clusters crossing Romania [10]. The aim is to analyze most recent changes in storm tracks over Southeast Europe, with a focus on extreme cyclone events and their regional variability mechanisms.…”

Atmospheric Circulation Patterns during the Summertime Precipitation in Southeastern Iran

“…There are two primary methods for classifying synoptic patterns [63,64]. The first is subjective classification, and the second involves using objective methods, such as correlation analysis, cluster analysis, nonlinear techniques, neural networks, and more [64,65], all used to identify consistent weather patterns [66]. Hierarchical clustering methods are well-suited for the exploratory phase of research [67], with Ward's method [68] being one of the main hierarchical clustering techniques in climatic classifications that has been employed in various studies [66,67,69,70].…”