Correcting surface winds by assimilating high-frequency radar surface currents in the German Bight
Alexander BarthAïda Alvera AzcarateJean-Marie BeckersJoanna StanevaEmil V. StanevJohannes Schulz‐Stellenfleth
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HF radar has become an increasingly important tool for mapping surface currents in the coastal ocean. However, the limited range, due to much higher propagation loss and smaller wave heights (relative to the saltwater ocean), has discouraged HF radar use over fresh water, Nevertheless, the potential usefulness of HF radar in measuring circulation patterns in freshwater lakes has stimulated pilot experiments to explore HF radar capabilities over fresh water. The Episodic Events Great Lakes Experiment (EEGLE), which studied the impact of intermittent strong wind events on the resuspension of pollutants from lake-bottom sediments, provided an excellent venue for a pilot experiment. A Multifrequency Coastal HF Radar (MCR) was deployed for 10 days at two sites on the shore of Lake Michigan near St. Joseph, MI. Similarly, a single-frequency CODAR SeaSonde instrument was deployed on the California shore of Lake Tahoe. These two experiments showed that when sufficiently strong surface winds (2 about 7 m/s) exist for an hour or more, a single HE radar can be effective in measuring the radial component of surface currents out to ranges of 10-15 km. We also show the effectiveness of using HF radar in concert with acoustic Doppler current profilers (ADCPs) for measuring a radial component of the current profile to depths as shallow as 50 cm and thus potentially extending the vertical coverage of an ADCP array.
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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.
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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.
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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.
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