Spatio-temporal interpolation of climatic variables over large region of complex terrain using neural networks

Empirical models for seven climatic variables (monthly mean air temperature, monthly mean daily minimum and maximum air temperature, monthly mean relative humidity, monthly precipitation, monthly mean global solar irradiation and monthly potential evapotranspiration) were built using neural networks. Climatic data from 127 weather stations were used, comprising more than 30000 cases for each variable. Independent estimators were elevation, latitude, longitude, month and time series of respective climatic variable observed at two weather stations (coastal and inland), which have long time-series of climatic variables (from mid last century). Goodness of fit by model was very high for all climatic variables (R > 0.98), except for monthly mean relative humidity and monthly precipitation, for which it was somewhat lower (R = 0.84 and R = 0.80, respectively). Differences in residuals around model were insignificant between months, but significant between weather stations, both for all climatic variables. This was the reason for calculation of mean residuals for all stations, which were spatially interpolated by kriging and used as a model correction. Similarly interpolated standard deviation and standard error of residuals are estimators of the model precision and model error, respectively. Goodness of fit after the averaging of monthly values between years was very high for all climatic variables, which enables construction of spatial distributions of average climate (climatic atlas) for a given period. Presented interpolation models provide reliable, both spatial and temporal estimations of climatic variables, especially useful for dendroecological analysis. (C) 2001 Elsevier Science B.V. All rights reserved.