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Suggested Citation:"References." 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 26
Suggested Citation:"References." 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.
×
Page 27
Suggested Citation:"References." 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 28

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References Bowman, K.P. 2005. Comparison of TRMM precipitation retrievals with rain gauge data from ocean buoys. Journal of Climate 18:178-190. Cressie, N., and G. Johannesson. 2006. Spatial prediction for massive datasets. In Mastering the Data Explosion in the Earth and Environmental Sciences: Proceedings of the Australian Academy of Science Elizabeth and Frederick White Conference. Canberra, Australia: Austra- lian Academy of Science. 11 pp. Cressie, N., and G. Johannesson. 2008. Fixed rank kriging for very large spatial data sets. Journal of the Royal Statistical Society, Series B 70:209-226. Daley, R. 1991. Atmospheric Data Analysis. Cambridge, UK: Cambridge University Press. 457 pp. Deng, M., and G.G. Mace. 2008. Cirrus cloud microphysical properties and air motion statis- tics using cloud radar Doppler moments: Water content, particle size, and ­sedimentation relationships. Geophysical Research Letters 35, L17808, doi:10.1029/2008GL035054. Luo, M., C.P. Rinsland, C.D. Rodgers, J.A. Logan, H. Worden, S. Kulawik, A. Eldering, A. Goldman, M.W. Shephard, M. Gunson, and M. Lampel. 2007. Comparison of carbon monoxide measurements by TES and MOPITT: Influence of a priori data and instru- ment characteristics on nadir atmospheric species retrievals. Journal of Geophysical Research 112:D09303, doi:10.1029/2006JD007663. Mace, G.G. 2001. Atmospheric Radiation Measurement Program Southern Great Plains Case Study, 6 March 2001. Available online at http://www.met.utah.edu/mace/homepages/ research/archive/sgp/sgp.html, accessed July 17, 2009. NRC (National Research Council). 2007. Environmental data management at NOAA: Ar- chiving, stewardship, and access. Washington, DC: The National Academies Press. Sanderson, B.M., C. Piani, W.J. Ingram, D.A. Stone, and M.R. Allen. 2008. Towards con- straining climate sensitivity by linear analysis of feedback patterns in thousands of perturbed-physics GCM simulations. Climate Dynamics 30:175-190. Shi, T., and N. Cressie. 2007. Global statistical analysis of MISR aerosol data: A massive data product from NASA’s Terra satellite. Environmetrics 18:665-680. 26

REFERENCES 27 Shi, T., B. Yu, and A.J. Braverman. 2002. MISR Cloud Detection over Ice/Snow Using Linear Correlation Matching. Technical Report 630. Berkeley, CA: University of California Berkeley Department of Statistics. Shi, T., B. Yu, E. Clothiaux, and A. Braverman. 2004. Cloud detection over ice and snow us- ing MISR data. Technical Report 663. Berkeley, CA: University of California Berkeley Department of Statistics. Tobler, W.R. 1970. A computer movie simulating urban growth in the Detroit region. Eco- nomic Geography 46:236.

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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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