A statistical downscaling method for monthly total precipitation over Turkey

Abstract

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.

Simulation of seasonal precipitation and raindays over Greece: a statistical downscaling technique based on artificial neural networks (ANNs)

“…Wilby and Wigley (2000) presented a comparative study on the predictors used for downscaling of precipitation -there is no rule for that selection as it depends on the decision of each researcher. The circulationbased predictors are the most commonly used, such as geopotential fields at different levels (500 hPa, 700 hPa), 1000-500 hPa thickness field, the sea level pressure (SLP) and specific humidity near the surface (SH) (Cavazos, 1997;Crane and Hewitson, 1998;Busuioc et al, 1999;González-Rouco et al, 2000;Trigo and Palutikof, 2001;Tatli et al, 2004).…”

“…Many different statistical methods have been employed, such as: canonical correlation analysis (CCA) (von Storch et al, 1993;Gyalistras et al, 1994;González-Rouco et al, 2000;Busuioc et al, 2001), linear and multiple regression (Kidson and Thompson, 1998;Kilsby et al, 1998;Kyselý, 2002), nonparametric models (Corte-Real et al, 1995), fuzzy logic neural networks (Cavazos, 1997;Crane and Hewitson, 1998;Trigo and Palutikof, 2001;Marzban, 2003;Knutti et al, 2003;Tatli et al, 2004), and singular value decomposition (Busuioc et al, 1999;von Storch and Zwiers, 2001).…”

Surface air temperature variability over turkey and its connection to large-scale upper air circulation via multivariate techniques

“…Here, however, the Sampson (1984) correlation ratio is used to recognize the predictors and predictands (e.g. Tatlı et al, 2004). This is defined as…”

“…Before constructing a downscaling model, a statistical preprocessing is required in order to determine the significant large-scale variables in a compressed manner (such as principal components (PCs) or independent components (Tatlı et al, 2004)). If we plot the time series of the monthly temperature series of 62 stations shown in Table I, and, for instance, of 500 hPa geopotential heights and of 500-1000 hPa geopotential thicknesses, then the irregular noise components emerge.…”

“…However, on local scales or scales near the surface, the outputs of GCMs may not be able to estimate the nature of climate. Since the free troposphere is spatially and temporally more homogeneous than the Earth's surface, the logic of downscaling from large-scale processes of the free troposphere is understandable (Kim et al, 1984;Wilks, 1989;Karl et al, 1990;Wigley et al, 1990;Giorgi and Mearns, 1991;Zorita et al, 1992;von Storch et al, 1993;Cubasch et al, 1996;Hewitson and Crane, 1996;Rummukainen, 1997;Schubert and Henderson-Sellers, 1997;McGregor, 1997;Kidson and Thompson, 1998;Solman and Nunez, 1999;Murphy, 1999Murphy, , 2000Landman and Tennant, 2000;Tatlı et al, 2004).…”