Modelling of Surface Air Temperature Element in Malaysia

Abstract

In spatial interpolation approaches, attribute data assumes continuous over space and spatially dependence. The surface air temperature is a collection of data, which are observed at discrete locations. Normally, spatial interpolation analysis applies to produce a continuous surface of this discrete data. There are a number of spatial interpolation techniques available to create continuous distribution of surface air temperature. To generate map of surface air temperature, this study examined the interpolation technique of inverse distance weighting (IDW). In applying the IDW technique, two different types of main data were assessed, i.e. mean monthly temperature data of T and estimation error of T – T', where T was observed mean monthly surface air temperature and T' was estimated mean monthly mean surface air temperature from a multiple regression. The multiple regression model was developed based on eight independent variables of elevation, location of latitude and longitude, distances of a station to nearest coastline, four types of land use, which included water bodies, forest, agriculture and build up areas. Cross validation analysis was conducted to calculate five different measured of errors. Inverse distance weighting (IDW) spatial interpolation of T ‐ Tʹ main data was produced acceptable errors and reliable map for mean monthly mean surface air temperature element in Peninsular Malaysia.