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.

A comprehensive examination of 2 yr of radiosonde data to determine the surface duct conditions over Istanbul (4°N, 29°E), Turkey, was made. The refractivity of the atmosphere is a function of air temperature and water vapor pressure. Any negative gradient in the modified refractivity results in the presence of a duct in the atmosphere. Therefore, the occurrence of ducts strongly depends upon both the synoptic and the local meteorological conditions that prevail over the region. The characteristics of surface ducts occurring over Istanbul were examined statistically. It was found that most of the ducts occur in May and July. The highest occurrence rate of surface ducts was observed in the summer season, and the lowest rate was observed in the winter season. The median duct thickness and duct strength are found to be the highest and the strongest in summer, whereas they are the lowest and the weakest in winter. When the data are separated into stable and unstable atmospheric subgroups, it is seen that surface duct characteristics show clear seasonal differences. Surface ducts in a stable atmosphere are found to be stronger than those in an unstable atmosphere. Also, daytime (1200 UTC) surface ducts occur more frequently than nighttime (0000 UTC) surface ducts in Istanbul. These statistics are discussed in association with local meteorological conditions and weather systems affecting the Istanbul region, and comments are made on the importance of their possible consequences in the region.

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The problem of statistical linkages between large-scale and local-scale processes is investigated through noise reduction by singular spectrum analysis (SSA) and spatial principal component analysis in order to construct appropriate statistical models for estimating the local-scale climate variables from large-scale climate processes. This paper presents an approach for downscaling monthly temperature series over Turkey by upper air circulations derived from the National Centers for Environmental Prediction-National Center for Atmospheric Research Reanalysis data sets (500 hPa geopotential heights and 500-1000 hPa thicknesses). The proposed approach consists of three stages. First, the available data sets are separated into deterministic, statistical components and random components by SSA. Second, the deterministic components are saved and the random components are eliminated by spatial principal component analysis. Subsequently, the statistical components are combined with the deterministic components constituting a noise-free data set. Furthermore, so-called Sampson correlation patterns are determined between the noise-free large-scale and the local-scale variables for interpreting the large-scale process impacts on local-scale features. Third, the significant redundancy variates based on canonical correlation analysis are extracted in order to identify the statistical downscaling model for temperature series of 62 stations in Turkey. The results show that the interpretation of the local-scale processes with the noise-free data sets is more significant than with the raw data sets.

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