Difference between revisions of "Integration of AQ Observation Systems:Challenges and design drivers for observational systems"

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[this section addresses some of the major shaping forces that need to be incorporated in a rethinking of our observational systems.  The section also provides an opportunity to allow for more focused attention on specific areas of interest.]
 
[this section addresses some of the major shaping forces that need to be incorporated in a rethinking of our observational systems.  The section also provides an opportunity to allow for more focused attention on specific areas of interest.]
  

Latest revision as of 13:30, October 9, 2006

Links to: Air Quality Cluster > CENR Monitoring Strategy



[this section addresses some of the major shaping forces that need to be incorporated in a rethinking of our observational systems. The section also provides an opportunity to allow for more focused attention on specific areas of interest.]

4.1 Forecasting and data assimilation

[4.1 Chemical data assimilations (fusing observations and model results to achieve spatially and temporally rich air quality surfaces underpinned by observations] is a common component of various visions embracing strong interagency (e.g., EPA-CDC-NOAA-NASA) partnerships. It’s difficult to envision any data application that would not benefit from data assimilation. Assuming that fusion approaches evolve as dominant assessment tools, an observation strategy should consider the needs attendant with supporting their development and application. These considerations might imply a shifting emphasis on observations which support model evaluations (broad scale representative locations, trace species, vertical profiles) and/or monitoring species ( e.g., dry speciated Hg for which large uncertainties exist in chemical mechanism) and locations (mountain valley interactions) that are difficult for model simulation. And, those observations that are difficult to acquire (cost, measurement artifacts, etc.) perhaps can be supplemented by improvements in model formulation (dry Hg, again; SVOCs, organic aerosols) and/or relied primarily upon model output (e.g., NO2, formaldehyde). These categories bring into play the role of intensive studies to gain better confidence in model predictions for species that are too intractable to measure routinely.

Air quality forecasting is highlighted because it has fostered a monitoring-modeling-communications partnership across EPA and NOAA that is likely to include chemical data assimilation, is incorporated into the GEOSS platform as a near term opportunity and is being proposed as part of an interagency recommendation (based on the 2/06 COMMUNITY WORKSHOP ON AIR QUALITY REMOTE SENSING FROM SPACE: DEFINING AN OPTIMUM OBSERVING STRATEGY) to the NRC’s Decadal Survey for Earth Science and Applications from Space, (http://qp.nas.edu/QuickPlace/decadalsurvey/Main.nsf/h_Toc/4DF38292D748069D0525670800167212/?OpenDocument ).

Furthermore, consideration is being given to leverage the daily CMAQ air quality modeling forecasting runs produced through the existing NOAA-EPA MOA as a modeling resource (fused with with ambient observations) to develop on a routine basis yearly sets of daily gridded estimates of key ambient and deposition species to support a spectrum of data users.]

4.2 LR transport

4.3 Linked bi-directionality with other media

4.4 Hazardous and persistent chemicals

4.5 Accountability and MP assessments