Ensemble Data Assimilation in Volcanology
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Volcanology
Volcanoes erupt and are constructed of materials that were subjected to complex physical and chemical processes during ascent and emplacement on the earth's surface. To understand these processes is the most fundamental reason for studying volcanoes. We take a broad approach in this review, which concentrates on American efforts in volcanology during 1979–82, and emphasize those investigations concerned with processes of magma rise, eruption, and emplacement on the surface. Volcanology draws on many diverse disciplines, including petrology, geochemistry, geophysics, geodetics, mechanics, and classical geology.
Volcanology
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Data assimilation applications with large-scale numerical models
exhibit extreme requirements on computational resources. Good
scalability of the assimilation system is necessary to make these
applications feasible. Sequential data assimilation methods based on
ensemble forecasts, like ensemble-based Kalman filters, provide such
good scalability, because the forecast of each ensemble member can be
performed independently. However, this parallelism has to be combined
with the parallelization of both the numerical model and the data
assimilation algorithm. In order to simplify the implementation of
scalable data assimilation systems based on existing numerical models,
the Parallel Data Assimilation Framework PDAF (http://pdaf.awi.de) has
been developed. PDAF provides support for implementing a data
assimilation system with parallel ensemble forecasts and parallel
numerical models. Further, it includes several optimized parallel
filter algorithms, like the Ensemble Transform Kalman Filter.
We will discuss the philosophy behind PDAF as well as features and
scalability of data assimilation systems based on PDAF on the example
of data assimilation with the finite element ocean model FEOM.
Ensemble forecasting
Ensemble Learning
Assimilation (phonology)
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Data assimilation applications with large-scale numerical models
exhibit extreme requirements on computational resources. Good
scalability of the assimilation system is necessary to make these
applications feasible. Sequential data assimilation methods based on
ensemble forecasts, like ensemble-based Kalman filters, provide such
good scalability, because the forecast of each ensemble member can be
performed independently. However, this parallelism has to be combined
with the parallelization of both the numerical model and the data
assimilation algorithm. In order to simplify the implementation of
scalable data assimilation systems based on existing numerical models,
the Parallel Data Assimilation Framework PDAF (http://pdaf.awi.de) has
been developed. PDAF provides support for implementing a data
assimilation system with parallel ensemble forecasts and parallel
numerical models. Further, it includes several optimized parallel
filter algorithms, like the Ensemble Transform Kalman Filter.
We will discuss the philosophy behind PDAF as well as features and
scalability of data assimilation systems based on PDAF on the example
of data assimilation with the finite element ocean model FEOM.
Assimilation (phonology)
Ensemble forecasting
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Accretionary wedge
Anticline
Forearc
Mass wasting
Thrust fault
Seafloor Spreading
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Data assimilation applications with high-dimensional numerical modelsshow extreme requirements on computational resources. Thus, goodscalability of the assimilation system is necessary to make theseapplications feasible. Sequential data assimilation methods based onensemble forecasts, like ensemble-based Kalman filters, provide suchgood scalability, because the forecast of each ensemble member can beperformed independently. However, this parallelism has to be combinedwith the parallelization of both the numerical model and the data assimilation algorithm. In order to simplify the implementation ofscalable data assimilation systems based on existing numerical models,the Parallel Data Assimilation Framework PDAF has been developed. Itprovides support for parallel ensemble forecasts and parallelnumerical models. Further, it includes several optimized parallel filteralgorithms, like the ensemble transform Kalman filter. We will discussthe features and scalability of data assimilation systems based onPDAF on the example of data assimilation with the finite element oceanmodel FEOM.
Assimilation (phonology)
Cite
Citations (0)