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Data assimilation/Fusion Directions for ARQI & AQRD

Data assimilation/Fusion Directions for ARQI & AQRD. V. Bouchet, Manager, AQRD/ARQI. Data assimilation/fusion - Meeting objectives.

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Data assimilation/Fusion Directions for ARQI & AQRD

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  1. Data assimilation/FusionDirections for ARQI & AQRD V. Bouchet, Manager, AQRD/ARQI

  2. Data assimilation/fusion - Meeting objectives To present recent advancements in Canadian science and on-going or upcoming projects associated with the integration of observed (surface, upper air and satellite) and modelled air quality data using techniques such as assimilation, objective analysis or fusion, with the objective to improve the following areas: Emissions air quality forecasting representation/mapping of past and current ambient air quality conditions (including in the context of exposure) and mapping of associated fields (ex: deposition) To discuss projects, approaches/methodologies, gaps, resources and timings with a view to coordinate R&D efforts contributing to Environment Canada’s programs.

  3. ARQI/AQRD Data assimilation/fusion objectives Development of best estimates of the state of ambient air quality (concentrations/emissions) Ambient levels Best atmospheric composition analyses as its relates to AQ (with an emphasis on lower tropospheric levels) Using all sources of data that are informative integration of surface, upper air and satellite as appropriate Use of upper atmospheric level data to inform model Timeframe: real-time and short-term (past year) reanalyses with a view of developing capacity to deliver on-going product RT: reliable and continuous sources of data Short-term: Same method as RT, with validated data - Climatologies to be built from on-going work Emissions Best emission estimates and/or trends and/or corrections Timeframe: targeting monthly at first, and improving overtime With EC model Scale: regional to local

  4. ARQI/AQRD Data assimilation/fusion objectives Improvement to air quality & health forecasting program Analyses as initial conditions or emission corrections Data assimilation Using sources of data that improve skill Integration of surface, upper air and satellite as appropriate Timeframe: real-time - reliable and continuous sources of data Scale: Global as needed to achieve regional scale With EC & assimilation systems 3D-var, EnKF, EnVar, 4D-Var Weather forecast improvements: where they can be achieved with work targeted at AQ program

  5. Point for discussion Performance level of model? Need for further validation? Are there missing processes that are critical gaps in using/assimilated the various sources of data? What data are used for validation? Need for sequencing addition of data? How do we transfer the methodology? Parallel implementation? Periods of cross-comparison?

  6. Points for discussion Data acquisition and archiving for research needs, for operational stream ? Data format/standard for archives and access

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