Start Date: 07/15/2009
End Date: 06/30/2013

This research develops the algorithmic and computational framework needed for a judicious assessment of the observation impact in air quality modeling. Novel algorithms in the framework of model-constrained optimization will allow to account for the data location in the time-space domain, observation type, instrument type, and data interaction in the presence of multiple observing systems. Specifically, the research is focused on: development of high-order adjoint-based observation impact techniques consistent to four-dimensional variational data assimilation schemes; assessment of forecast sensitivity to observation error variances and estimation of the forecast impact of uncertainties in the specification of the input error statistics; development of efficient computational approaches to allow for practical implementations of the observation impact algorithms; validation of the novel techniques through observing system experiments and assessment of the potential impact of new observing systems.

The ability to accurately represent the distribution of atmospheric pollutants in relation to various anthropogenic activities is essential for chemical weather forecasting to protect the population, for answering science questions related to the future of our planet, and for designing sound environmental policies. An accurate representation of the chemical composition of the atmosphere requires a close integration of models and observations through data assimilation.

Data assimilation is the process by which model predictions utilize measurements to produce an optimal representation of the state of the atmosphere. As more observations are becoming available and new measurement networks are being planned, it is of critical importance to develop the capabilities to best utilize the data, to better manage the sensing resources, and to design more effective field experiments and networks to support atmospheric chemistry and air quality studies. This research develops the computational tools required to optimize the information provided by the existing observing systems and to provide guidance for future improvements to the observational network and instruments design. The new developments will also help the design process of new field experiments and of new chemical monitoring networks.

Grant Institution: National Science Foundation

Amount: $418,616

People associated with this grant:

Adrian Sandu