Uncertainty Management in Remote Sensing of Climate Data Summary of a Workshop (2009) / Chapter Skim
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Appendix B: Summaries of Workshop Presentations
Appendix B: Summaries of Workshop Presentations
Pages 32-47
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From page 32... ...
In order to assess the predictions obtained using models that integrate such datasets, tools must be developed that quantify the full uncertainty associated with such models, rather than simply evaluating the sensitivity of model predictions to a set of model parameters. In many cases, the uncertainty associated with the conceptual framework of the models and the specific parameterizations included in the models, outweigh the uncertainty caused by incomplete knowledge of individual parameters.
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From page 33... ...
Developing statistical tools that explicitly account for spatial or temporal autocorrelation avoids such errors. In addition, using models that quantify and account for spatial and/or temporal correlation can decrease the uncertainty associated with model predictions because the spatial or temporal information footprint of available data can be assessed and used to inform the model.
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From page 34... ...
This could be achieved by developing statistical models that emulate many of the underlying physical processes, and explicitly account for the uncertainty associated with all model components. Remote Sensing of Surface Winds Ralph Milliff, Northwest Research Associates Bayesian hierarchical modeling (BHM)
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Mistral events, where cold dry air blows off the European continent in late winter, generate large wind stress curl, the uncertainty of which can be characterized. The forecast model generates physically realistic spreads in the forecast initial condition, and the spread is focused on uncertain scales of the general circulation in the Mediterranean.
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From page 36... ...
It is important for statisticians to appreciate the complexity of the dataset to gain an understanding of the issue prior to analysis. The temporal and spatial discrepancies between the rain gauge data and the satellite measurements lead to several questions about the validation process that must be considered: what size area should be used in averaging satellite data, what is the optimal time period over which the rain gauge data should be averaged, and if a satellite passes several times in one month, how long before and after the pass should the rain gauge data be averaged?
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From page 37... ...
Cloud size varies greatly, and this complicates the measurement of cloud fraction. A welldesigned detection threshold that produces a good estimate of cloud fraction despite the problems associated with size and area measurement has been demonstrated by the International Satellite Cloud Climatology Project (ISCCP)
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From page 38... ...
This talk compares performance of the ELCMC against two other machine learning techniques, the quadratic discriminant analysis (ELCMC-QDA) and the ELCMC support vector machine.
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This talk demonstrates how to compare and validate microphysical cloud properties using multiple instruments including satellite measurements, groundbased remote sensing measurements, and aircraft measurements. Cloud properties play an important role in the climate system.
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From page 40... ...
Algorithms incorporate assumptions about physical parameters, which contribute to the uncertainties in the algorithms in subtle ways. An example of comparing ground-based and satellite data is seen in examining Moderate Resolution Imaging Spectroradiometer (MODIS)
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From page 41... ...
The satellite measures the aerosol optical depth (AOD) , which are measures of the column-integrated extinction, the amount of light that is either scattered or absorbed as it passes through the aerosol layer giving an indication of the amount of aerosol.
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From page 42... ...
Spatio-temporal statistics incorporates space and time into statistical models of all aspects of the uncertainty. There are a multitude of statistical techniques that can be applied to climate datasets, and hierarchical statistical modeling is one such.
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From page 43... ...
Aerosol and Cloud Representation in Global Models Joyce Penner, University of Michigan Global climate modelers continually work to improve climate models by analyzing observational data to gain better insights into the physics of atmospheric processes. The challenges associated with comparing climate models and data are similar to validation studies that compare satellite retrievals with ground-based measurements.
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From page 44... ...
Validation of the chemical composition of the aerosols in the models cannot come from comparisons with satellite data or ground-based remote sensing data like that of AERONET, because the measured aerosol optical depth (AOD) is a composite of the effects of the different aerosol types we attempt to model.
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From page 45... ...
A Bayesian perspective to retrospective data assimilation, combining information to create datasets for use in climate model initial conditions, is explored in this talk. For example, a wind dataset that can be replicated over time and space has uncertainty associated with the satellite observations and these uncertainties influence the weighted combination of the prior mean and the mean data as well as the outcome of the posterior data distributions.
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From page 46... ...
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, National Center for Atmospheric Research A few significant obstacles that are most commonly felt by statisticians and geoscientists who deal with processing, validating, and analyzing remotely sensed climate data include resistance to new ideas by members of the community, lack of funding for analysis following a satellite launch, and the need for more work on combining models with observations through data assimilation. This talk addresses these barriers from case studies in the community.
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From page 47... ...
Using data assimilation in this way creates an opportunity to use multiple instruments and parameters from remotely sensed data to improve upon model physics and dynamics. It essentially blurs the line between climate and weather models, which can be a beneficial way to improve both.
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