Researchers are aware of certain types of problems that arise when modelling interconnections between general circulation and regional processes, such as prediction of regional, local-scale climate variables from large-scale processes, e.g. by means of general circulation model (GCM) outputs. The problem solution is called downscaling. In this paper, a statistical downscaling approach to monthly total precipitation over Turkey, which is an integral part of system identification for analysis of local-scale climate variables, is investigated. Based on perfect prognosis, a new computationally effective working method is introduced by the proper predictors selected from the National Centers for Environmental Prediction-National Center for Atmospheric Research reanalysis data sets, which are simulated as perfectly as possible by GCMs during the period of 1961-98. The Sampson correlation ratio is used to determine the relationships between the monthly total precipitation series and the set of large-scale processes (namely 500 hPa geopotential heights, 700 hPa geopotential heights, sea-level pressures, 500 hPa vertical pressure velocities and 500-1000 hPa geopotential thicknesses). In the study, statistical preprocessing is implemented by independent component analysis rather than principal component analysis or principal factor analysis. The proposed downscaling method originates from a recurrent neural network model of Jordan that uses not only large-scale predictors, but also the previous states of the relevant local-scale variables. Finally, some possible improvements and suggestions for further study are mentioned.
This study proposes the Hurst exponent (H) to detect persistence in the Palmer Drought Severity Index (PDSI) over Turkey. Since a fractal structure admits the behaviour of global determinism and local randomness, the H exponent values could be used to detect self-similar statistical structure of the time series. Additionally, the H value >0.5 and near 1 shows the intensity level of persistence. The term persistence may be assessed as a criterion to be used as a measure of predictability. Moreover, the predictability index, fractal dimension and autocorrelation function of the PDSI values can also be obtained from the H values. Nevertheless, it is not easy to find a spatial meaning of information content of the results obtained by the methods used in the present study. For that reason, the Mann-Kendall (MK) trend test was also used, and the results showed that significant negative trends are widespread throughout the country. As expected, the H values were close to 1 in places where statistically significant trends exist. The high values of the H exponent in all the regions can be explained by the droughts observed there that might be caused due to the possible associations with the large-scale atmospheric circulations. In this context, it is suggested that the droughts can be predictable by constructing the appropriate general climate circulation models.
ABSTRACT:In this study, we suggest the spectral clustering (SC), a hybrid clustering technique based on singular value decomposition (SVD) and K-means for grouping features of precipitation totals of 96 stations in Turkey. Clustering process establishes an exhaustive set of occupied regimes into distinct climatic zones. Results of the SC satisfactorily represent the influences of the synoptic-scale weather systems including such as the mid-latitude and Mediterranean frontal cyclones, and the mid-latitude travelling and eastern Europe high pressures in winter, sub-tropical Azores high pressure and monsoon low in summer. Results of the SC also well display the influences of local-scale atmospheric disturbances, and direct influences of physical geographical features of Turkey (i.e. exposure, topography, orography, land-sea distribution, continentality and the high Anatolian peninsula) on the geographical variability and coherent distribution of the annual precipitation totals over Turkey. Finally, based on the results of the SC method employed to annual precipitation totals of 96 stations in Turkey for the period of 1929-
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