Bayesian Hierarchical Models of Surface Vector Winds: Applications in Ocean Forecasting

Tuesday, March 12, 2013 - 12:00pm - 12:30pm
Keller 3-180
Ralph Milliff (University of Colorado)
The Bayesian Hierarchical Model (BHM) methodology has been applied to the
generation of ensemble ocean surface vector winds in a sequence of increasingly
sophisticated models. This history is briefly reviewed to establish the
approach to BHM development in atmosphere-ocean contexts. Recently,
ensemble surface winds and wind stresses are obtained from BHMs, given data
stage inputs from satellites and weather-center analyses. Process model distributions
are based on leading
order terms from a Rayleigh Friction Equation balance and from formulae for
bulk transfers at the air-sea interface. The forcing ensembles exploit precise
observations and precise specifications of error to infer error in ocean forecasts
based on two different kinds of data assimilation (DA) systems; i.e., a sequential DA system in
the Mediterranean Sea and a variational DA system in the California Current System.
Plans for developments in the next level of sophistication in atmosphere-ocean BHMs
will introduce process model breakthroughs to be discussed in the talk that
follows (Wikle et al.).
MSC Code: