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 cyclone tracks crossing Southeast Europe are investigated for the last few decades (1980–1999 compared with 2000–2019) using a developed objective method. The response in number, severity, and persistence of the tracks are analyzed based on the source of origin (the Mediterranean Sea sub-domains) and the target area (Romania-centered domain). In winter, extreme cyclones became more frequent in the south and were also more persistent in the northeast of Romania. In summer, these became more intense and frequent, mainly over the south and southeast of Romania, where they also showed a significant increase in persistence. The regional extreme changes are related to polar jet displacements and further enhanced by the coupling of the sub-tropical jet in the Euro-Atlantic area, such as southwestwards shift in winter jets and a split-type configuration that shifts northeastwards and southeastwards in the summer. These provide a mechanism for regional variability of extreme cyclones through two paths, respectively, by shifting the origins of the tracks and by shifting the interaction between the anomaly jet streaks and the climatological storm tracks. Large-scale drivers of these changes are analyzed in relation to the main modes of atmospheric variability. The tracks number over the target domain is mainly driven during the cold season through a combined action of AO and Polar–European modes, and in summer by the AMO and East-Asian modes. These links and the circulation mode’s recent variability are consistent with changes found in the jet and storm tracks.
One of the main concerns with quantitative precipitation estimates (QPE) based on weather radar observations is the extent to which nowcasters should believe them as they rush to issue warnings for dangerous weather phenomena that might endanger human lives and goods. This paper aims to improve QPE by adjusting the mean field bias using rain gauge measurements. Radar data used in this research were supplied from a single polarization S‐band Doppler radar, WSR‐98D (Weather Surveillance Radar – 98 Doppler), located almost in the centre of Romania, at Bobohalma, and a network consisting of 27 rain gauges within weather stations belonging to the Romanian National Meteorological Administration. The procedure consisted of two main steps: in step one, the reflectivity data were converted into rain rate using the Z–R relationship; in step two, differences between radar data and gauge data were investigated using four objective functions, the ratio between radar data and gauge data, the root‐mean‐square factor, and Pearson and Spearman correlations. The findings are consistent with previous studies, emphasizing that both the differences and correlations between radar data and rain gauge amounts have local significance rather than general relevance over the studied area.
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