Abstract
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.
Highlights
Due to their use for regulatory applications and to support legislation, air quality (AQ) models must model correctly and be correctly applied, justifying the need for a thorough evaluation
Based on the experience matured within AQMEII, while the internal model errors are of interest for model development because they are generated by systematic modelling errors, the bias introduced by external drivers is responsible for the largest share of modelling errors
This study presents a novel approach to model evaluation, and aims to combine standard operational statistics with the time allocation of the component error
Summary
Due to their use for regulatory applications and to support legislation, air quality (AQ) models must model correctly and be correctly applied, justifying the need for a thorough evaluation. Despite the increasing relevance of modelling systems for AQ applications, model evaluation continues to rely almost exclusively on operational evaluation, which basically involves gauging the model’s performance using distance, variability and associativity metrics This common practice has little or no impact on model improvement, as it does not target the source of the modelling error and does not discriminate between the reasons for appropriate or inappropriate performance. The main aim is to introduce a novel method that combines operational and diagnostic evaluations This method helps apportion the model error to its components, thereby identifying the space/timescale at which it is most relevant and, when possible, to infer which process/es could have generated it. This work is designed to support the analysis of the currently ongoing third phase of the AQMEII activity (Galmarini et al, 2015)
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