Uncertainty Management in Remote Sensing of Climate Data Summary of a Workshop (2009) / Chapter Skim
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1 Introduction
1 Introduction
Pages 1-10
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In December 2008, three standing committees of the National Academies held a workshop to survey how statisticians, climate scientists, and remote sensing experts might address the challenges of uncertainty management in remote sensing of climate data. The emphasis of the workshop was on raising and discussing issues that could be studied more intently by individual researchers or teams of researchers, and on setting the stage for possible future collaborative activities.
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When a remote sensing instrument retrieves a measurement that is used to infer a geophysical value (e.g., atmospheric temperature) , uncertainties exist both in the measured values and in the statistical model used to validate the remotely sensed parameter.
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Challenge: Improving Challenge: physical Challenge: Validation of representations Extrapolating to remote sensing and future climate retrievals understanding predictions Role for Characterize Develop new Maximize value statistics: spatio-temporal statistical of limited data Clarify and mismatches, methods to and hardcharacterize retrieval make the most to-formalize sources of algorithm of new data assumptions uncertainty in differences; types to address about remote sensing address new science relationships data sparseness questions among past, or absence of present, ground truth and future Role for Develop formal Develop new Develop statistics: statistical error methods to formalisms Develop measures for exploit massive for combining statistical both bias and datasets in output from methods variance an inferential different models to quantify setting in light of and reduce available data uncertainty Role for Overcome Pose problems Combine statistics: mismatches as formal physical Provide an by statistical questions and statistical overarching modeling of of statistical models framework relationships inference between observed and unobserved quantities. SOURCE: Table courtesy of Amy Braverman, JPL.
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Uncertainty quantification, in the broadest sense, is to account for not only uncertainty in individual parameters within the models that are used, but also to account for the uncertainty inherent in the actual models themselves, which are only approximate representations of physical processes. Workshop participants emphasized that improving physical process representation is critical for both improving climate models and for better characterizing their uncertainties.
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A good statistical model is built in a way that captures some of the physical processes that control elements of the climate system, or 1-1 alternative hypotheses about those processes. The classical method, described at the workshop, for characterizing uncertainty in earth science modeling is through sensitivity analysis.
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WHY WORRY ABOUT STATISTICAL STRUCTURE: AN EXAMPLE FROM MODELING SNOW DEPTH Anna Michalak at the University of Michigan described how the statistical properties in remote sensing datasets offer both a challenge and an opportunity. For example, understanding and accounting for statistical dependence, including spatial and temporal correlations, can improve the utility of observational datasets.
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, the spatial correlation is accounted for in the estimation process. In the example shown in the figure, the classical approach incorrectly rejects the null hypothesis that there is no trend between snow depth and elevation, whereas the approach based on spatial statistics correctly does not reject this hypothesis at the 95 percent confidence level.
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The red line represents the biased estimate of average snow depth obtained from a simple average of the available observations. The green line represents the unbiased estimate obtained by assigning weights to the observations based on an understanding of the scales of spatial variability of the snow depth in the valley.
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Top: illustrates one case of the generated data, and the estimated slope between snow depth and elevation, using simple linear regression (red line) , and an approach that accounts for the spatial correlation of the data (green line)
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Overall, this example illustrates that statistical approaches that ignore spatial and/or temporal correlation inherent in environmental data carry with them an increased risk of erroneously concluding that significant relationships exist between physical phenomena (snow depth and elevation, in this case) , and, more generally, yield biased estimates due to their assumption of independent observations.
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