Model quality objectives based on measurement uncertainty. Part I: Ozone

Since models are increasingly used for policy support their evaluation is becoming an important issue. One of the possible evaluations is to compare model results to measurements. Statistical performance indicators then provide insight on model performance but do not tell whether model results have reached a sufficient level of quality for a given application. In a previous work Thunis et al. (2012, referred to as T2012) proposed a Model Quality Objective (MQO) based on the root mean square error between measured and modeled concentrations divided by the measurement uncertainty. In T2012 the measurement uncertainty was assumed to remain constant regardless of the concentration level. In the current work this assumption is overcome by quantifying all possible sources of uncertainty for the particular case of O3. Based on these uncertainty source quantifications, a simple relationship is proposed to formulate the measurement uncertainty which is then used to update the MQO and Model Performance Criteria (MPC) proposed in T2012 with more accurate values. The MQO and MPC calculated based on the European monitoring network AIRBASE data provide insight on the expected model results quality for a given application, depending on the geographical area and station type. These station specific MQOs and MPCs have the main advantage of relating expected model performances to the underlying measurement uncertainties.