Improving ozone modeling in regions of complex terrain using observational nudging in a prognostic meteorological model

Abstract

Three air quality simulations are presented for an ozone episode that occurred in the Cascadia region of the Pacific Northwest during 11–14 July 1996. These three scenarios were the result of three different fine-scale (5 km grid) wind field predictions that were input into the air quality model. Wind field modeling in the Cascadia region is difficult due to the complex terrain that exists within the domain. Wind fields were developed using the prognostic model MM5 and the diagnostic model CALMET. The first wind field was created by MM5 and did not employ observational nudging in the data assimilation process. Results were often poor, especially in mountainous regions. The second wind field combined the first MM5 solution with CALMET’s objective analysis feature to adjust the MM5 predictions toward the observed winds. This was an iterative approach that yielded acceptable results both in terms of the wind field prediction and subsequent air quality prediction, but was very time consuming to apply. The final wind field was an MM5 simulation that incorporated observational nudging, using hourly surface wind measurements. This approach yielded better wind field predictions relative to the MM5 simulation that did not employ observational nudging. In addition, ozone predictions were improved when this wind field was input into the air quality model, relative to the other two ozone simulations. These results illustrate the importance of applying observational nudging in a prognostic meteorological model when simulating air quality in regions of complex terrain.