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

Chapter: Summary

Visit NAP.edu/10766 to get more information about this book, to buy it in print, or to download it as a free PDF.
Previous Chapter: Figure Captions
Page 63 Cite Bookmark
Suggested Citation: "Summary." National Research Council. 2004. Statistical Analysis of Massive Data Streams: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/11098.
Save
Cancel

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

  1. Blended scatterometer and weather-center analyses provide global, realistic high-wavenumber surface winds

    • impose spectral constraints via multi-resolution wavelets

  1. 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

  1. Tropical Winds Example (Wikle et al. 2001)

  2. 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

Page 63 Cite Bookmark
Suggested Citation: "Summary." National Research Council. 2004. Statistical Analysis of Massive Data Streams: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/11098.
Save
Cancel
Page 63
Next Chapter: Report from Breakout Group
Subscribe to Email from the National Academies
Keep up with all of the activities, publications, and events by subscribing to free updates by email.