Special Session on:
Linking Observation and Prediction: Frameworks for Data Assimilation, Uncertainty Analysis, and Valuing Information


Patrick Reed, Penn State University
Ming Pan, Princeton University


This session is focused on innovative computational frameworks for improving monitoring and forecasting of the environmental systems under uncertainty. In hydrological and environmental contests, there is a strong and present need to improve observation - prediction feedbacks. These feedbacks will be important for nonlinear systems where critical thresholds could lead to substantial and sustained changes in observable dynamics. This session encourages submissions that advance (1) new data assimilation frameworks, (2) Bayesian or other estimation frameworks characterizing both observation and model uncertainties, (3) decision support frameworks for model-based design and adaptation observation systems, and (4) approaches for reducing uncertainty and improving information quality through data synthesis. Applications in surface hydrology, groundwater, water quality, and meteorology are encouraged.