Statistical Analysis of Massive Data Streams: Proceedings of a Workshop (2004)

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GLOBAL AND REGIONAL SURFACE WIND FIELD INFERENCES FROM SPACE-BORNE SCATTEROMETER DATA 63 Summary 1. Global and regional surface wind datasets from spaceborne scatterometers are ”massive” and important for climate and weather. Applications require: • regular grids • uniform spatial O(10 km) and temporal O(diurnal) resolution 2. Blended scatterometer and weather-center analyses provide global, realistic high-wavenumber surface winds • impose spectral constraints via multi-resolution wavelets 3. Bayesian Hierarchical Models to exploit massive remote sensing datasets • measurement error models from cal/val studies (likelihoods) • process models from GFD (priors) • advances in MCMC 4. Tropical Winds Example (Wikle et al. 2001) 5. Bayesian Hierarchical Model for Air-Sea Interaction (Berliner et al 2002) • multi-platform data from scatterometer and altimeter • stochastic geostrophy (atmos) and quasi-geostrophy (ocean) priors • MCMC to ISMC linkage for posteriors • term-by-term uncertainty • realistic covariance structures