OPTIMAL AVERAGING FOR THE DETERMINATION OF GLOBAL MEAN TEMPERATURE - EXPERIMENTS WITH MODEL DATA

Optimal averaging is a method to estimate some area mean of datasets w ith imperfect spatial sampling. The accuracy of the method is tested b y application to time series of January temperature fields simulated b y the NCAR Community Climate Model. Some restrictions to the applicati on of optimal averaging are given. It is demonstrated that the proper choice of a spatial correlation model is crucial. It is shown that the optimal averaging procedures provide a better approximation to the tr ue mean of a region than simple area-weight averaging does. The inclus ion of measurement errors of realistic size at each observation locati on hardly changes the value of the optimal average nor does it substan tially alter the sampling error of the optimal average.