Articles | Volume 16, issue 10
Research article
24 May 2016
Research article |  | 24 May 2016

Error apportionment for atmospheric chemistry-transport models – a new approach to model evaluation

Efisio Solazzo and Stefano Galmarini

Abstract. In this study, methods are proposed to diagnose the causes of errors in air quality (AQ) modelling systems. We investigate the deviation between modelled and observed time series of surface ozone through a revised formulation for breaking down the mean square error (MSE) into bias, variance and the minimum achievable MSE (mMSE). The bias measures the accuracy and implies the existence of systematic errors and poor representation of data complexity, the variance measures the precision and provides an estimate of the variability of the modelling results in relation to the observed data, and the mMSE reflects unsystematic errors and provides a measure of the associativity between the modelled and the observed fields through the correlation coefficient. Each of the error components is analysed independently and apportioned to resolved processes based on the corresponding timescale (long scale, synoptic, diurnal, and intra-day) and as a function of model complexity.

The apportionment of the error is applied to the AQMEII (Air Quality Model Evaluation International Initiative) group of models, which embrace the majority of regional AQ modelling systems currently used in Europe and North America.

The proposed technique has proven to be a compact estimator of the operational metrics commonly used for model evaluation (bias, variance, and correlation coefficient), and has the further benefit of apportioning the error to the originating timescale, thus allowing for a clearer diagnosis of the processes that caused the error.

Short summary
A new technique to assess the quality of model results from a regional-scale air quality model is presented. The techniques are based on standard statistical parameters but work on spectral decomposition of model and measurement time series. This allows for the identification of scale-related processes for which the largest divergency between model and observed data is found. The technique is applied to the data collected during the second phase of the AQMEI Initiative.
Final-revised paper