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Reflections by One Statistician Jarrett Barber University of Wyoming Department of Statistics

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Data Models Assimilation Integration Fusion Assintegrofussatamodeling.

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“New” Modeling Framework [Data|process] *[process|parameters]*[parameters] Basic elements –Fundamental probability rules: conditional specification: model locally, infer globally –Process modeling and more empirical (“regression”) expertise –Technical methodologies (MCMC) Nice thing: it’s more plug ‘n’ play Bad thing: it’s more plug ‘n’ play

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Issues A Reasonable Perspective: Models (mean or covariance) are wrong. Check your models. (More than ever.) –Model comparisons: information criteria –Observed versus predicted –Many model components. How check? Education –Traditional statistical methods verses probability modeling. –Substantive area expertise (process modeling) –Computational/Mathematical techniques Just the beginning –Need some (new) way to facilitate modeling related activities –“NEON:” More than more data?

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Really Big Models When your predictions (forecasts) given by your best model still don’t behave then use data to “adjust” states (i.e., the outputs) by optimal (often linear) prediction: –objective analysis (“Kriging”) –KF and variants –Adjoint method Often not feasible to do do inference for parameters inside the black box because of model complexity (time/computing power limitations). Uncertainty is a problem. –Computer experiments: carefully select a set of parameters at which to run the model and then model the model parameters to find the top of the hill in parameter space.

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NEON, etc. More data! And it will be easy to get (once someone figures out how to make it easy). Where/how do models or model components fit here? Do we want more than facilitated data sharing?

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Uncertainty/Variability Model framework that promotes explicit accounting of uncertainty/variability while incorporating information in the form of a process (or other) model components –Currently seems to be favoring Bayes E.g., Andrew Latimer charismatic “shrubs” –Priors are important for complex models to behave. Update the priors as we learn.

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Data and Users Data: NEON, LTER, P2ERLS, … Assimilators: –Mat Williams, Kelvin Droegemeier, … Integrators –Alan Hastings, Paul Moorcroft, Andrew Latimer, Jizhong (Joe) Zhu, Kiona Ogle, … Modelers –Forward (simulation). Inverse (inference on parameters).

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Models Embody theoretical, empirical, phenomenological, semi-mechanistic, mechanistic (mis)understanding of biological phenomenon. –Range of understanding (model components) that go into such models: empirical regression relationships (light response curves) to Big Science fluid flow differential equations. –Forward modeling, parameter “tuning.” –Recent (10-15 years) opportunities for more “formal” parameter estimation and characterization of uncertainty.

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Classic Assintegrofussatamodel ModelH Operator Light Response Curves Initial States State Forecast Data Adjusted States

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