High spatial resolution interpolation of monthly temperatures of Sardinia

Meteorological applications

article

P. Boi¹  M. Fiori¹  S. Canu¹ 

[1] Dipartimento Specialistico Regionale Idrometeoclimatico

关键词: multilinear regression;    forward selection;    cross-validation;    coefficient of determination;    rmse;   

DOI  :  10.1002/met.243

学科分类：社会科学、人文和艺术（综合）

来源: Wiley

【 摘 要 】

Interpolation of monthly averages of maximum and minimum temperatures for 1 year data on a 250 m grid by multilinear regression his presented here. The principal aim is to find a suitable parameter for interpolation of minimum temperatures in cases of nocturnal inversion. For this purpose a geostatistical parameter has been calculated and tested. It is related to the height relative to the nearest valley. The second aim is to find how the regression equation depends on the sea distance, if in a linear or non linear form and, if nonlinear, what is the best exponent. The procedure has been tested on a 1 year data set from 60 meteorological stations on Sardinia Island. The selection of parameters has been made by the forward selection method. The interpolation errors (RMSE) on independent stations (i.e. not used to calculate the regression coefficient) have been calculated by the cross-validation method using a developmental data set of size n − 1. The parameter that contains most of the variance is the height: the second one, for minimum temperatures, is the relative height. For maximum temperatures the second parameter is the sea distance, but only in summer months. The RMSE on the independent data ranges from 1.0 to 1.5 °C for minimum temperatures and from 0.5 °C (winter months) to 1.4 °C (summer months) for maximum temperatures. The effect of relative elevation in the regression is a 15% increase of the coefficient of determination. At the same time it lowers the RMSE significantly.

【 授权许可】

CC BY|CC BY-NC|CC BY-NC-ND