# On the Regional Temperature Series Evolution in the South-Eastern Part of Romania
###### Abstract  
In the context of reported climate variations in different regions of the world, this work investigates the evolution of the temperature series in the Dobrogea region, Romania, using the maximum, average, and minimum annual temperature series from 1965 to 2005. The Mann-Kendall test and Sen's slope emphasized increasing trends of nine (out of ten) minimum temperature series (nine of them at significance levels less than or equal to 0.05, and two at 0.1), three average temperature series (at a significance level of 0.1), and five maximum temperature series (at significance levels less than or equal to 0.05). The selection of the representative series at the regional scale, called the 'Regional series', was performed using two algorithms proposed by the author that are easy to employ, even by individuals without deep knowledge in the field. The first (called MPPM) was initially introduced for evaluating the 'Regional precipitation series', and the second is a version of MPPM based on clustering the data series. Comparisons with the series from the ROCADA database were performed to prove the algorithms' performances. The best results were obtained by running the second algorithm with two clusters, for the minimum and maximum temperature series, and with three clusters, for the average temperature series. In comparison with the initial data series, the average MAEs were, respectively, 1.39, 0.37, and 0.84 for the minimum, average, and maximum series, and the corresponding average MSEs were, respectively, 1.49, 0.41, and 0.93. Comparison of the 'Regional series' with the series from ROCADA led to a decrease in the modeling errors, with the best ones corresponding to the average 'Regional series'--MAE = 0.36 and average MSE = 0.25.  
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variograms necessary to interpolate the daily rainfall series. Improved versions of IDW using Particle Swarm Optimization (PSO) and genetic algorithms are provided in [11; 12; 13]. Geostatistical methods have been developed primarily in geology, followed by applications in meteorology and forestry [14]. They have restricted use outside the structure-function (variogram). The main limitation of kriging is related to the stationarity hypothesis of data series and the necessity of large data sets for defining spatial autocorrelation [15; 16]. Moreover, the stationarity in mean and variance and the existence of a Gaussian distribution are conditions that should be satisfying when employing radar and in situ data combinations [17]. Discussing what kind of spatial interpolation is adequate for meteorology, [PERSON] et al. [18] conclude that 'the geostatistical methods cannot efficiently use the meteorological data series, whereas the data series makes possible to obtain the necessary information' for the interpolation in meteorology. By comparison, the'mechanical methods' (Thiessen polygons methods, IDW) are widely applied because they do not rely on restrictive conditions and consider the spatial and temporal aspects. Since the existence of climatic data of high quality is necessary for assessing the climate variability in a region, gridded datasets have been created for different regions of the world [19; 20; 21]. The main methods utilized for this aim are data homogenization--with Multiple Analysis of Series for Homogenization (MASH) software [22]--and gridding by Meteorological Interpolation based on Surface Homogenized Data (MISH) software [23]. The gridded datasets represent a valuable input for spatially distributed hydrological agrometeorological models [19]. The interest in spatial interpolation methods resides in the practical goal of detecting the regional evolution of hydro-meteorological time series [24; 25; 26; 27; 28], especially in climate change conditions [3]. Despite their importance, many algorithms can only be performed by people with deep statistical and computational knowledge.