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ESSPE: Ensemble-based Simultaneous State and Parameter Estimation for Earth System Data-Model Integration and Uncertainty Quantification

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Wednesday, 02 May 2018, 12:00

Wednesday, May 2, 2018. 12:00PM. ESSPE: Ensemble-based Simultaneous State and Parameter Estimation for Earth System Data-Model Integration and Uncertainty Quantification. Fuqing Zhang, Penn State. Sponsored by Geophysical Fluid Dynamics Laboratory. More information here.


Building on advanced data assimilation techniques, we advocate to develop and apply a generalized data assimilation software framework on Ensemble-based Simultaneous State and Parameter Estimation (ESSPE) that will facilitate data-model integration and uncertainty quantification for the broad earth and environmental science communities. This include, but not limited to, atmospheric composition and chemistry, land surface, hydrology, and biogeochemistry, for which many of the physical and chemical processes in their respective dynamic system models rely heavily on parameterizations. Through augmenting uncertain model parameters as part of the state vector, the ESSPE framework will allow for simultaneous state and parameter estimation through assimilating in-situ measurements such as those from the CZO networks and/or remotely sensed observations such as those from radars and satellites. Beyond data model integration and uncertainty quantification, through systematically designed ensemble sensitivity analysis, examples will be given to the application of the ESSPE framework to: (1) identify key physical processes and their significance/impacts and to better represent and parameterize these processes in dynamical models of various earth systems; (2) design better observation strategies in locating the optimum sensitive regions, periods and variables to be measured, and the minimum accuracies and frequencies of these measurements that are required to quantify the physical processes of interest; explore the impacts of heterogeneity and equifinality; (3) understand predictability and nonlinearity of these processes, and parameter identifiability; and (4) facilitate upscale cascading of knowledge from smaller-scale process understanding to larger-scale simplified representation and parameterization.

Location  NOAA GFDL, Smagorinsky Seminar Room, Princeton, NJ.