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Suggested Citation:"Appendix A: Workshop Agenda." National Research Council. 2009. Uncertainty Management in Remote Sensing of Climate Data: Summary of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/12677.
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Page 29
Suggested Citation:"Appendix A: Workshop Agenda." National Research Council. 2009. Uncertainty Management in Remote Sensing of Climate Data: Summary of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/12677.
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Page 30
Suggested Citation:"Appendix A: Workshop Agenda." National Research Council. 2009. Uncertainty Management in Remote Sensing of Climate Data: Summary of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/12677.
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Page 31

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Appendix A Workshop Agenda Workshop on Uncertainty Management in Remote Sensing of Climate Data December 4, 2008 The Doubletree Hotel 1515 Rhode Island Ave., NW Washington, DC 20005 8:30 Welcoming remarks and overall workshop goals Amy Braverman, Jet Propulsion Laboratory Session A: Introduction 8:40 Differences in terminology, techniques, and approaches between statisticians and earth scientists Anna Michalak, University of Michigan 9:00 Remote sensing of surface winds  Ralph Milliff, Northwest Research Associates, Inc., Colorado Research Associates 9:30 Remote sensing and precipitation Tom Bell, NASA 10:00 Discussion Moderator: Amy Braverman, Jet Propulsion Laboratory 10:15 Break 29

30 APPENDIX A Session B: Clouds 10:30 Different types of uncertainties in cloud data sets William Rossow, City College of New York, CUNY 11:00 Machine learning techniques for cloud classification Bin Yu, University of California at Berkeley 11:30  Validation of cloud property measurements from multiple instruments Jay Mace, University of Utah 12:00 Discussion Moderator: Karen Kafadar, Indiana University 12:30 Working Lunch Session C: Aerosols 1:30 Uncertainty issues associated with remotely sensed data sets for aerosols Lorraine Remer, NASA 2:00 Spatial statistics with an emphasis on aerosol data Noel Cressie, Ohio State University 2:30 Discussion Moderator: Steve Platnick, NASA 3:00 Break Session D: Integrating models and data 3:15 Aerosol and cloud representation in global models Joyce Penner, University of Michigan 3:45 Data assimilation as a hierarchical statistical process, interacting dynamically with modeling Christopher Wikle, University of Missouri 4:15 Discussion Moderator: John Bates, NOAA

APPENDIX A 31 Session E: Making progress through practical and institutional barriers 4:30 The practical and institutional barriers for making progress on developing and improving statistical techniques for processing, validating, and analyzing remotely sensed climate data Doug Nychka, NCAR 5:00 Discussion Moderator: Amy Braverman, Jet Propulsion Laboratory 5:25 Wrap-up and final remarks 5:30 Adjourn

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Great advances have been made in our understanding of the climate system over the past few decades, and remotely sensed data have played a key role in supporting many of these advances. Improvements in satellites and in computational and data-handling techniques have yielded high quality, readily accessible data. However, rapid increases in data volume have also led to large and complex datasets that pose significant challenges in data analysis. Uncertainty characterization is needed for every satellite mission and scientists continue to be challenged by the need to reduce the uncertainty in remotely sensed climate records and projections. The approaches currently used to quantify the uncertainty in remotely sensed data lack an overall mathematically based framework. An additional challenge is characterizing uncertainty in ways that are useful to a broad spectrum of end-users.

In December 2008, the National Academies held a workshop, summarized in this volume, to survey how statisticians, climate scientists, and remote sensing experts might address the challenges of uncertainty management in remote sensing of climate data. The workshop emphasized raising and discussing issues that could be studied more intently by individual researchers or teams of researchers, and setting the stage for possible future collaborative activities.

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