## 2 Materials and Methods
### CMAQ Operational Model Performance Evaluation  
The AMET tool was used to evaluate the CMAQ simulations using concurrent ozone observations. Model performance statistics (i.e., bias, error, and correlation) and time series plots were calculated at monitoring sites. Scatter plots of predicted and observed concentrations were generated as well as spatial maps of site-specific performance statistics. [PERSON] et al. [29] provide a set of statistics and benchmarks to assess PGM performance. They deemed that normalized mean bias (NMB) and error (NME) and correlation (r or COR) statistics have the best characteristics historically. Table 1 presents the statistical measures and goal and criteria thresholds for ozone for NMB, NME, and COR. Goal thresholds represent the statistical values that approximately one-third (i.e., the 33 rd percentile) of top performing past applications have achieved and are considered the best a model can be expected to achieve. The less restrictive criteria are around the 67 th percentile and indicate statistical values that approximately two-thirds of past applications have met. Additional relevant recommendations include: (1) no minimum cutoff for maximum daily 8-h average (MDA8) ozone; (2) temporal scales for ozone statistics should not exceed 1 month; and (3) spatial scales should range from urban to \(\leq\)1000 km. Figure 4: Example of model-ready NOx emissions on the 4-km domain: (**a**) 2020 BAU NOx emissions; (**b**) 2020 BAU/2020 COVID NOx emissions. Figure 5a shows scatter plots of MDA8 ozone performance for all sites in the 4-km domain for June and July for 2019 and 2020 COVID. The \(x\)-axis is observed MDA8 ozone, and the \(y\)-axis is CMAQ MDA8 ozone. Each point represents MDA8 ozone for one site on one day. NMB and NME are displayed in Figure 5a. For each case, the NME is between 15.9% to 17.1%, which easily meets the performance criterion and nearly meets the criteria goal. NMB has more variation between the four cases. For 2019, there is a positive NMB of 5.5% in June and a negative bias of \(-4.3\%\) in July; these values are close to the performance goal. For 2020, there is a positive NMB of 9.6% in June and 1.5% in July. For both years, July exhibits a discernable negative bias at the higher range of observed MDA8 (e.g., >80 ppb). Figure 5b presents spatial plots of site-specific NMB and NME statistical performance metrics for 2019 Base and 2020 COVID. For June, the majority of SoCAB monitors meet the NMB criteria, and all sites meet the NME criteria in both 2019 and 2020. For July, there is an underestimation bias at many monitors in the SoCAB in both 2019 and 2020, but the NME falls within the criteria. Outside of the SoCAB, CMAQ tends to overestimate ozone for both months and both years at some sites. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Statistic/Abbreviation** & **Definition 1** & **Notes** \\ \hline Normalized Mean Bias (NMB) & \(\frac{\Sigma(\frac{P_{i}-O_{i}}{\Sigma O_{i}})}{\times 100}\) & \(-100\%\leq\text{NMB}\leq+\infty\) \\ \hline Normalized Mean Error (NME) & \(\frac{\Sigma[P_{i}-O_{i}]}{\Sigma O_{i}}\times 100\) & \(0\%\leq\text{NME}\leq+\infty\) \\ \hline Correlation Coefficient (r) & \(\frac{\Sigma\left[(P_{i}-\overline{P})\times(O_{i}-\overline{O})\right]}{ \sqrt{\Sigma(P_{i}-\overline{P})^{2}\times\Sigma(O_{i}-\overline{O})^{2}}}\) & Unitless, \(-1\leq r\leq+1\) \\  & \(r=1\) is perfect correlation \\  & \(r=0\) is totally uncorrelated \\ \hline \multicolumn{3}{c}{**Minimum Metrics and Benchmarks**} \\ \hline  & **NMB** & **NME** & **r** \\ \hline Species & Goal & Criteria & Goal & Criteria \\ \hline
1-hr or MDA8 Ozone & \(<\!|\pm\!5\%|\) & \(<\!|\pm\!15\%|\) & \(<\!15\%\) & \(<\!25\%\) & \(>\!0.75\) & \(>\!0.50\) \\ \hline \hline \end{tabular}  
* Subscript \(i\) represents the pairing of \(N\) observations and \(O\) predictions by site and time. Overbars signify means over the site and/or time.  
\end{table} 
Table 1: Statistical measures, goal and criteria thresholds for ozone model performance. Excerpted from [PERSON] et al. (2019).