Statistical Analysis of Massive Data Streams: Proceedings of a Workshop (2004)
Chapter: Bayesian Hierarchical Model for Surface Winds in the Tropics
blended field is consistent with power-law spectral properties observed by the QSCAT. The third panel shows the wind stress curl for the blended field.
The blended winds have been used to drive regional and global ocean model simulations. Milliff et al. (1999) demonstrated realistic enhancements to the response of a relatively coarse-resolution ocean general circulation model (OGCM) to the higher-wavenumber winds in the blended product. Higher resolution OGCM experiments are in progress now.
Bayesian Inference for Surface Winds in the Labrador Sea
The Labrador Sea is one of a very few locations in the world ocean where surface exchanges of heat, momentum and fresh water can drive the process of ocean deep convection. Ocean deep convection can be envisioned as the energetic downward branch of the so-called global ocean conveyor belt cartoon for the thermohaline general circulation that is important in the dynamics of the Earth climate. The energetic exchanges at the surface are often associated with polar low synoptic events in the Labrador Sea.
A Bayesian statistical model has been designed to exploit the areal coverage of scatterometer observations, and provide estimates of uncertainty in the surface vector wind inferences for the Labrador Sea. Here, the scatterometer system is the NASA Scatterometer or NSCAT system that preceded QS-CAT. It has proved convenient to organize the Bayesian model components in stages. Data Model Stage distributions are specified almost directly from precise information that naturally arises in the calibration and validation of satellite observing systems. The Prior Model Stage (stochastic geostrophy) invokes a simple autonomous balance between surface pressure (a hidden process in our model) and the surface winds. The posterior distribution for the surface vector winds is obtained from the output of a Gibbs sampler.
An application of the Labrador Sea model for surface winds will be described at the end of this presentation. The first documentation of this model appears in Royle et al. (1998).
Bayesian Hierarchical Model for Surface Winds in the Tropics
The Bayesian Hierarchical Model (BHM) methodology is extended in a model for tropical surface winds in the Indian and western Pacific Ocean that derives from Chris Wikle’s postdoctoral work (Wikle et al., 2001). Here, the Data Model Stage reflects measurement error distributions for QSCAT in the tropics as well as for the surface winds from the NCEP analysis. The Prior Model Stage is prescribed in two parts. For large scales, the length scales
