Year-end Sale % OFF 
Abstract
The aim of this paper is to analyze the impact of initializing GEM-MACH, Environment and Climate Change Canada’s air quality (AQ) forecast model, with multi-pollutant surface objective analyses (MPSOA). A series of 48-h air quality forecasts were launched for July 2012 (summer case) and January 2014 (winter case) for ozone, NO2, and PM2.5. In this setup, the GEM-MACH model (version 1.3.8.2) was initialized with surface analysis increments (from MPSOA) which were projected in the vertical by applying an appropriate fractional weighting in order to obtain 3D analyses in the lower troposphere. Here, we have used a methodology based on sensitivity tests to obtain the optimum vertical correlation length (VCL). Overall, results showed that for PM2.5, more specifically for sulfate and crustal materials, AQ forecasts initialized with MPSOA showed a very significant improvement compared to forecasts without data assimilation, which extended beyond 48 h in all seasons. Initializing the model with ozone analyses also had a significant impact but on a shorter time scale than that of PM2.5. Finally, assimilation of NO2 was found to have much less impact than longer-lived species. The impact of simultaneous assimilation of the three pollutants (PM2.5, ozone, and NO2) was also examined and found very significant in reducing the total error of the Air Quality Health Index (AQHI) over 48 h and beyond. We suggest that the period over which there is a significant improvement due to assimilation could be an adequate measure of the pollutant atmospheric lifetime.
Highlights
It is well known that data assimilation can improve the performance of numerical models (Kalnay 2003)
Models are generally characterized by known deficiencies for prediction of many pollutants whereas measurement systems suffer from representativeness problems and lack of sufficient coverage and, are often best suited for providing local air quality information
This paper is the continuation of a previous scientific project where multiple pollutant surface objective analyses (MPSOA) were prepared using optimal interpolation techniques combining air quality model (GEM-MACH) and AIRNow database supplemented by Canadian surface observations (Robichaud and Ménard 2014; Robichaud et al 2016)
Summary
It is well known that data assimilation can improve the performance of numerical models (Kalnay 2003). One of the current weaknesses of air quality models in Canada and elsewhere is that these are not initialized or constrained by chemical observations and could contain large uncertainties associated with errors in emissions, boundary conditions, and chemistry parameterization (Pagowski et al 2010; Robichaud 2010; Moran et al 2014). These models have meteorological inaccuracies associated with wind (speed and direction), atmospheric instability, solar radiation, characteristics of the boundary layer, and precipitation (Reidmiller et al 2009; Zhang et al 2012; Bosveld et al 2014). The impact on improving AQHI forecast is assessed
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
