10A.4
On the use of model physics parameters as control variables in data assimilation systems
Derek J. Posselt, University of Michigan, Ann Arbor, MI
Model physics (e.g., microphysics, convection, and radiation) schemes represent an important source of uncertainty in numerical models that range in scale from large eddy simulation to general circulation models. Much of this uncertainty is associated with specification of parameters that control the rates and/or characteristics of physical processes. In contrast to errors in forecast initial conditions, the characteristics of model physics uncertainty are not well understood, hence model physics error is not included in most operational data assimilation systems. As ensembles of simulations are increasingly used in data assimilation and probabilistic forecasting, it is desirable to perturb both initial conditions and model physics parameters. To do so properly requires knowledge of which parameters have the greatest effect on model results, as well as the characteristics of the relationship between model output and changes to parameters.
In this presentation, we quantify the functional relationship between model parameters and observations using a Markov chain Monte Carlo (MCMC) algorithm. We examine cloud microphysics and radiation packages from a cloud resolving model that are similar to schemes used in modern regional and general circulation models, and demonstrate how the joint probability distribution returned from MCMC can be used to
• map the functional relationship between changes in model physics parameters and changes in model output,
• identify which parameters have the most significant effect on various model output fields,
• describe the nature of nonlinearity in the parameter-state relationship, and
• explore how changes in the characteristics of observations affect the model state.
The results of the MCMC-based inversion suggest that nonuniqueness in the relationship between changes in parameter values and changes in model output may be the reason for the loss in “parameter identifiability” noted in previous studies. We examine these results in detail and suggest ways in which nonuniqueness can be avoided in the construction of a data assimilation scheme that includes model physics parameters as control variables. We also compare results from simulations of different types of cloud system to how the influence of cloud microphysical parameters changes for different cloud systems.
Session 10A, Advanced Methods for Data Assimilation I
Wednesday, 20 January 2010, 4:00 PM-5:30 PM, B207
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