The aim of this work is to make a comparison between OLS (Ordinary Least Square) and GWR (Geographically Weighted Regression) technique in order to explain the relationship between air temperature and some climatic and territorial variables. As a matter of fact, the variation of temperature measured at high degree of spatial scale is due to land surface factors, to atmospheric factors and to sea linked factors. GWR has been developed to study local relations and to give a better representation of spatial information. Recently, GWR was applied in many environmental and ecological studies, providing an alternative framework to classical regression analysis. In this work, in an innovative way, it was applied to model the relationship between temperature (dependent variable), precipitation, elevation and distance from sea. The variables temperature and precipitation are monthly mean values collected for the period 2001-2010 from 154 weather Italian stations. The analysis of the results shows that GWR models capture better sample information of our dataset respect to OLS models. In particular, the application of GWR let us obtain a clear identication of precipitation's effect on the air temperature. The identified patterns correlate fairly well with atmospheric circulation which furthers clouds development and, consequently, precipitation.

Local Spatial Modeling of Meteorological Variables

MUCCIARDI, Massimo;BERTUCCELLI, PIETRO;
2012

Abstract

The aim of this work is to make a comparison between OLS (Ordinary Least Square) and GWR (Geographically Weighted Regression) technique in order to explain the relationship between air temperature and some climatic and territorial variables. As a matter of fact, the variation of temperature measured at high degree of spatial scale is due to land surface factors, to atmospheric factors and to sea linked factors. GWR has been developed to study local relations and to give a better representation of spatial information. Recently, GWR was applied in many environmental and ecological studies, providing an alternative framework to classical regression analysis. In this work, in an innovative way, it was applied to model the relationship between temperature (dependent variable), precipitation, elevation and distance from sea. The variables temperature and precipitation are monthly mean values collected for the period 2001-2010 from 154 weather Italian stations. The analysis of the results shows that GWR models capture better sample information of our dataset respect to OLS models. In particular, the application of GWR let us obtain a clear identication of precipitation's effect on the air temperature. The identified patterns correlate fairly well with atmospheric circulation which furthers clouds development and, consequently, precipitation.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11570/2513227
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