Part I: Using Verification Metrics

We examine how passive and active observations are useful to evaluate an air quality 9 analysis. By leaving out observations from the analysis, we form passive observations, and the 10 observations used in the analysis are called active observations. We evaluated the surface air quality 11 analysis of O3 and PM2.5 against passive and active observations using standard model verification 12 metrics such as bias, fractional bias, fraction of correct within a factor 2, correlation and variance. The 13 results show that verification of analyses against active observations always give an overestimation of 14 the correlation and variance. Evaluation against passive or any independent observations display a 15 minimum variance and maximum correlation as we vary the observation weight, thus providing a 16 mean to obtain the optimal observation weight. For the time and dates considered, the correlation 17 between (independent) observations and the model is 0.55 for O3 and 0.3 for PM2.5 and for the analysis, 18 with optimal observation weight, increases to 0.74 for O3 and 0.54 for PM2.5. We show that bias can be 19 a misleading measure of evaluation and recommend the use of a fractional bias such as the modified 20 normalized mean bias (MNMB). An evaluation of the model bias and variance as a function of model 21 values also show a clear linear dependence with the model values for both O3 and PM2.5. 22