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Suggested Citation:"3 Concluding Thoughts." 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 24
Suggested Citation:"3 Concluding Thoughts." 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 25

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3 Concluding Thoughts A s conveyed by many participants at the National Academies’ workshop on uncertainty management for remotely sensed cli- mate data, it would be helpful if the climate research community as a whole could settle on priority questions where collaboration with the statistics community would be most beneficial. The advancement of statistical techniques could then focus on these fundamental science ques- tions. For example, the importance of climate models for policy making suggests that improved statistical techniques for improving their param- eterizations and analyzing their output could have substantial benefits for both the scientific community and society as a whole. There are historical precedents for this type of progress; for example, in the 1970s and 1980s the community recognized that it was critical to gain a better understand- ing of how clouds affect the earth’s radiation budget. Today, we have a much clearer picture of how clouds alter the transfer of radiation through the atmosphere on a variety of timescales, and progress on this topic has allowed scientists to develop a more comprehensive, although still incom- plete, understanding of the feedbacks between clouds and other aspects of the climate system. As described throughout the workshop, the components of the climate system are coupled and interact in multiple ways and at multiple scales, which makes it difficult to discern which components are contributing the most to the uncertainties in our knowledge. In addition, the chal- lenges associated with quantifying uncertainties in a certain geophysical parameter (e.g., precipitation) are typically unique, and cannot necessarily 24

CONCLUDING THOUGHTS 25 be readily translated to other system components. However, a statistical framework for evaluating uncertainties throughout the system can help lead to a logical roadmap for a research enterprise. As conveyed by many workshop participants, stronger collaboration between the earth science and statistics communities would likely result in many benefits. Workshop participants suggested that additional, more focused workshops might be organized as a way of spurring progress in understanding uncertainties associated with particular geophysical processes and with some particular data-collection challenges (e.g., mea- suring aerosols or cloud cover). These follow-on workshops could probe more deeply into particular models, research, and challenges. Examples might include the advancement of statistical techniques to address spatial and temporal autocorrelation in large datasets and methods to incorpo- rate more physical knowledge and physical modeling into the statistical models that will help improve calibration and validation studies. With the rich collection of remotely sensed data, the workshop par- ticipants discussed that considerable progress could be made by going beyond simple monthly and annual averages to describe the climate sys- tem, and that modern statistical methods had much to offer in the area of representing the physical processes that make up the climate system. There is a wealth of data to be processed, and analysis of this data requires both good physical models and modern statistical methods to fully under- stand the biases and residual errors. In the era of advanced earth science collection and processing techniques, the fusion of multiple datasets is a challenge in the remote sensing community and represents another rich area for collaboration, as does data assimilation. The goal for a statistical framework is to account for uncertainty not only in individual parameters but also in the entire modeling framework used to predict the processes of interest. An overall statistical framework for accounting for uncertainty in remotely sensed climate data and in climate models might also assist in the development of an integrated strategy for communicating uncertainties. Such progress would not only aid research in the earth science and statistics communities, but will result in more useful information for the climate policy community.

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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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