Description: All observing and assimilation systems contain a wide-variety of parameters.  These parameters could control the quality of retrievals from observing systems.  They might be used to represent background/observation error variances or even the structure of ensemble localization/inflation in assimilation systems.  In all cases these parameters are typically estimated from training sets using statistical methods.  This session intends to provide a broad overview of both theory and practice in the application of statistical parameter estimation for integrated observing and assimilation systems for the atmosphere, oceans, and land surface.  Subject areas of particular interest include, but are not limited to, the following: statistical estimation methods for observation operator development; estimation methods for observation and background error covariance matrices (e.g. Desroziers, Höllingsworth-Lonnberg, etc.); estimation methods for prior/posterior inflation as well as ensemble covariance localization parameters; as well as the application of artificial intelligence/machine learning methods to the tuning/configuration/diagnosis of observing and assimilation systems.