Development of a Data Assimilation Capability towards Ecological Forecasting in a Data-Rich Era. NSF, 2009-2012. (Principal Investigator)

We propose to develop an Ecological Platform for Assimilation of Data (EcoPAD) for data assimilation and forecasting in ecology. EcoPAD will include components of (1) core computational algorithms (e.g., ecological models) that are specifically designed to solve ecological issues, (2) a variety of optimizing techniques for data assimilation, (3) various data bases that will feed into EcoPAD, and (4) diverse functions of EcoPAD. The functions enable users to (i) estimate model parameters or state variables, (ii) quantify uncertainty of estimated parameters and projected states of ecosystems (e.g., carbon sinks in USA), (iii) evaluate model structures, (iv) assess sampling strategies, and (v) conduct ecological forecasting. The inverse model of EcoPAD will have functions of (1) parameter estimation, (2) evelauation of different model structures, (3) uncertainty analysis, (4) quantification of information contained in data sets in diagnostic modeling. The forward mode of EcoPAD will incorporate estimated parameter values, their variances, and other sources of information into prognostic models to project future states of ecological systems and associated uncertainties.