CIRA » Home » Research » Parameter Estimation (Optimization)
Most dynamical models have empirical parameters that have to be specified. Also, the analysis process sometimes requires introduction of parameters that need to be adjusted. In principle, the parameters are difficult to optimize due to their nonlinearity and potential abrupt changes. Estimation of the parameters and their uncertainties in the MLEF is facilitated by using the iterative minimization.
We are interested in estimation of parameters in all scales, ranging from microphysical parameters to climate model parameters.
This research topic is a component of other research topics
- Model Error and Bias
- Predictability
- Applications with Complex Models
- Applications with Simple Models
- Basic Development of Ensemble Data Assimilation
and research projects
- Weak constraint approach to ensemble data assimilation: Application to microwave precipitation observations
- Research and development for GOES-R risk reduction
- Ensemble Kalman filtering for Army-scale meteorology