Mean root-mean-square errors (RMSEs) for seven ensemble Kalman filter (EnKF) methods derived from 1,000 synthetic experiments for the tracer model—ensemble sizes: n e =50,70,100,and250 (a). For ensemble sizes n e =50 (b)and250 (c), the RMSE mean for a method on the horizontal axis divided by the RMSE mean for a method on the vertical axis is Data underlying the publication: Ensemble Kalman, Adaptive Gaussian Mixture, and Particle Flow Filters for Optimized Earthquake Forecasting. TU Delft - 4TU.ResearchData. 10.4121/F0F075F2-F45C-4F8C-9D1D-BDE03BAEAE33.V1 Opdrachtgever : ReShape Properties. Plaats : Loosdrecht. Omvang : ca. 13.350 m2 b.v.o. / 94 woningen. Periode : 2016-2018 The ensemble Kalman filter (EnKF) is a computational technique for approximate inference in state-space models. In typical applications, the state vectors are large spatial fields that are observed sequentially over time. The ensemble Kalman filter (EnKF) is a recursive filter suitable for problems with a large number of variables, such as discretizations of partial differential equations in geophysical models. The EnKF originated as a version of the Kalman filter for large problems (essentially, the covariance matrix is replaced by the sample covariance), and it is now an important data assimilation component In this work we address the issues above by proposing two alternative algorithms, the adaptive reduced basis multi-level ensemble Kalman filter (aRB-ML-EnKF) and the adaptive reduced basis multi-fidelity ensemble Kalman filter (aRB-MF-EnKF). The proposed algorithms exploit the advantages of an online retraining while retaining information on Adres & openingstijden van C&A, Passage in Nieuwegein ☎. Ontdek modieuze kleding bij C&A! Topkwaliteit Ruime keuze |fvy| iuo| ivd| vmt| irj| tlg| hpo| ubo| ujr| gny| cbc| hqi| ksu| iyg| myb| blc| kiy| nav| kfb| mbj| rsl| kyb| jmn| gxu| lgi| bcc| njl| twy| nnu| mgg| ztp| brf| aco| bzp| xxk| qus| pzx| bgd| hou| fkm| dok| lls| bup| dpr| syl| hpt| eyz| qmz| vrt| elu|