Improving regional ozone modeling through systematic evaluation of errors using the aircraft observations during the International Consortium for Atmospheric Research on Transport and Transformation

During the operational phase of the ICARTT field experiment in 2004, the regional air quality model STEM showed a strong positive surface bias and a negative upper troposphere bias (compared to observed DC-8 and WP-3 observations) with respect to ozone. After updating emissions from NEI 1999 to NEI 2001 (with a 2004 large point sources inventory update), and modifying boundary conditions, low-level model bias decreases from 11.21 to 1.45 ppbv for the NASA DC-8 observations and from 8.26 to −0.34 for the NOAA WP-3. Improvements in boundary conditions provided by global models decrease the upper troposphere negative ozone bias, while accounting for biomass burning emissions improved model performance for CO. The covariances of ozone bias were highly correlated to NOz, NOy, and HNO3 biases. Interpolation of bias information through kriging showed that decreasing emissions in SE United States would reduce regional ozone model bias and improve model correlation coefficients. The spatial distribution of forecast errors was analyzed using kriging, which identified distinct features, which when compared to errors in postanalysis simulations, helped document improvements. Changes in dry deposition to crops were shown to reduce substantially high bias in the forecasts in the Midwest, while updated emissions were shown to account for decreases in bias in the eastern United States. Observed and modeled ozone production efficiencies for the DC-8 were calculated and shown to be very similar (7.8) suggesting that recurring ozone bias is due to overestimation of NOx emissions. Sensitivity studies showed that ozone formation in the United States is most sensitive to NOx emissions, followed by VOCs and CO. PAN as a reservoir of NOx can contribute to a significant amount of surface ozone through thermal decomposition

Improving regional ozone modeling through systematic evaluation of errors using the aircraft observations during the International Consortium for Atmospheric Research on Transport and Transformation

During the operational phase of the ICARTT field experiment in 2004, the regional air quality model STEM showed a strong positive surface bias and a negative upper troposphere bias (compared to observed DC-8 and WP-3 observations) with respect to ozone. After updating emissions from NEI 1999 to NEI 2001 (with a 2004 large point sources inventory update), and modifying boundary conditions, low-level model bias decreases from 11.21 to 1.45 ppbv for the NASA DC-8 observations and from 8.26 to -0.34 for the NOAA WP-3. Improvements in boundary conditions provided by global models decrease the upper troposphere negative ozone bias, while accounting for biomass burning emissions improved model performance for CO. The covariances of ozone bias were highly correlated to NOz, NOy, and HNO3biases. Interpolation of bias information through kriging showed that decreasing emissions in SE United States would reduce regional ozone model bias and improve model correlation coefficients. The spatial distribution of forecast errors was analyzed using kriging, which identified distinct features, which when compared to errors in postanalysis simulations, helped document improvements. Changes in dry deposition to crops were shown to reduce substantially high bias in the forecasts in the Midwest, while updated emissions were shown to account for decreases in bias in the eastern United States. Observed and modeled ozone production efficiencies for the DC-8 were calculated and shown to be very similar (7.8) suggesting that recurring ozone bias is due to overestimation of NOxemissions. Sensitivity studies showed that ozone formation in the United States is most sensitive to NOxemissions, followed by VOCs and CO. PAN as a reservoir of NOxcan contribute to a significant amount of surface ozone through thermal decomposition

Relationship between photolysis frequencies derived from spectroscopic measurements of actinic fluxes and irradiances during the IPMMI campaign

[1] The relationship between photolysis frequencies derived from spectroscopic measurements of actinic fluxes and irradiances was determined during a coordinated measurement campaign (International Photolysis Frequency Measurement and Modeling Intercomparison campaign (IPMMI)). When differences in viewing geometries are taken into account, the measurements are in close agreement. An empirical relationship, which is useful for high sun (noon) conditions or for daily integrals, was found to convert irradiance data to photolysis frequencies. For low-sun conditions (large solar zenith angle), model calculations were shown to improve the accuracy. However, the input parameters to the model are site specific and the conversion depends on diffuse/direct ratios. During cloudy conditions, significant improvements in the conversion can be achieved by assuming the radiation field to comprise entirely diffuse isotropic radiation when the UVA transmission by cloud is less than 0.8. Changing cloud conditions remain the greatest limitation, but they tend to bias the results away from the clear-sky case in a systematic way. Furthermore, although the cloud effects on the photolysis rates of nitrogen dioxide (J(NO2)) are rather large, they are much smaller for ozone photolysis (J(O-3 --> O(D-1))), which is of prime importance in tropospheric chemistry. The study shows the potential for deriving historical and geographical differences in actinic fluxes from the extensive records of ground-based measurements of spectral irradiance