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The Ensemble Kalman Filter
An Iterative Ensemble Kalman Filter for Data Assimilation - OnePetro
Close this notification. Journal of Physics: Conference Series. Download Article PDF. Ensemble data assimilation provides a consistent ensemble of initial states, whose distribution is determined dynamically by data and by previous model states, such that it fits the current weather situation.
At Deutscher Wetterdienst an ensemble data assimilation system for its global model ICON is being used operationally since January The assimilation method for the deterministic state is EnVAR, where the correlation matrix is dynamically generated from the current ensemble.
In contrast to the global model, the resolution of all model states is the same to properly model convection processes. The data assimilation of the deterministic state is carried out by using the so-called Kalman matrix of the ensemble. For its data assimilation and for its forecasts a regional model needs boundary conditions.
Indeed, global and regional ensemble data assimilation and the global and regional ensemble prediction system build one integrated system. Graphics: Boundary conditions for the regional model are generated based on the global data assimilation and prediction system.
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Content Main Menu Search. Ensemble-Data Assimilation.
To distribute the information from one measurement location to other locations we need knowledge about the connection between the variables. From a physical and statistical viewpoint this is about the correlation between different spatial points, between different meteorological variables and the observed quantities. It is possible to derive such correlations by statistical methods applied to some period of time for example one month.
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But in reality they are dynamic, i. To optimally evaluate observations, the knowledge about the current atmospheric correlation is necessary.
Ensemble data assimilation allows the evaluation of dynamical correlations within each analysis step of the data assimilation cycle. To the top Ensemble Data Assimilation for Ensemble Prediction Systems The estimation of the uncertainty or risk of some particular quantity and phenomenon is important not only for the state determination, but in particular for the prediction.
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- Data assimilation with the Weighted Ensemble Kalman Filter.
This workshop does not publish full papers, so submission of full paper is not required. To facilitate the workshop organization, we encourage our participants to submit abstracts with full information of all authors e. About one page must be e-mailed to Xiaodong Luo xluo norceresearch. Deadline for registration: April 12th, Accommodation: If you want to stay at the conference hotel; Park Hotel Vossevangen prior Sunday June 2nd to Monday June 3rd or during the conference please contact the hotel directly kurs parkvoss.