Evaluating the performance of regional-scale meteorological models: effect of clouds simulation on temperature prediction

In this paper, we apply two new techniques for evaluating the ability of meteorological models to reproduce the spatial and non-linear temporal features embedded in the observations. The use of these methods is illustrated by comparing the temperature simulations from two mesoscale meteorological models commonly used in air quality modeling. The results reveal that the treatment of cloud processes in the models has a strong impact on their ability to simulate the spatial coherency and the degree of non-linearity reflected in the observed temperature fluctuations. The results indicate that these statistical techniques can provide insights into the modelʹs behavior in reproducing the key spatio-temporal features embedded in the observations.