NASA Technical Reports Server (NTRS) 20100036784: Sources of Uncert... | |
by NASA Technical Reports Server (NTRS) | |
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In the domain of predicting land surface fluxes, models | |
are used to bring data from large observation networks | |
and satellite remote sensing together to make predictions | |
about present and future states of the Earth. | |
Characterizing the uncertainty about such predictions is | |
a complex process and one that is not yet fully | |
understood. Uncertainty exists about initialization, | |
measurement and interpolation of input variables; model | |
parameters; model structure; and mixed spatial and | |
temporal supports. Multiple models or structures often | |
exist to describe the same processes. Uncertainty about | |
structure is currently addressed by running an ensemble | |
of different models and examining the distribution of | |
model outputs. To illustrate structural uncertainty, a | |
multi-model ensemble experiment we have been conducting | |
using the Terrestrial Observation and Prediction System | |
(TOPS) will be discussed. TOPS uses public versions of | |
process-based ecosystem models that use satellite-derived | |
inputs along with surface climate data and land surface | |
characterization to produce predictions of ecosystem | |
fluxes including gross and net primary production and net | |
ecosystem exchange. Using the TOPS framework, we have | |
explored the uncertainty arising from the application of | |
models with different assumptions, structures, | |
parameters, and variable definitions. With a small number | |
of models, this only begins to capture the range of | |
possible spatial fields of ecosystem fluxes. Few attempts | |
have been made to systematically address the components | |
of uncertainty in such a framework. We discuss the | |
characterization of uncertainty for this approach | |
including both quantifiable and poorly known aspects. | |
Date Published: 2016-11-05 16:28:14 | |
Identifier: NASA_NTRS_Archive_20100036784 | |
Item Size: 4683368 | |
Language: english | |
Media Type: texts | |
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