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Surface and Interior Dynamics of Arctic Seas Using Surface Quasi-Geostrophic Approach., , , , , , , and . Remote. Sens., 15 (7): 1722 (April 2023)Super-resolution data assimilation., , , and . CoRR, (2021)An Evaluation of the EnKF vs. EnOI and the Assimilation of SMAP, SMOS and ESA CCI Soil Moisture Data over the Contiguous US., , , , , and . Remote. Sens., 11 (5): 478 (2019)A Kernel Extension of the Ensemble Transform Kalman Filter., , , and . ICCS (4), volume 14076 of Lecture Notes in Computer Science, page 438-452. Springer, (2023)Bayesian inference of dynamics from partial and noisy observations using data assimilation and machine learning., , , and . CoRR, (2020)The circlet transform: A robust tool for detecting features with circular shapes., , , , , and . Comput. Geosci., 37 (3): 331-342 (2011)Arctic Sea Level Budget Assessment during the GRACE/Argo Time Period., , , , , , , , , and 8 other author(s). Remote. Sens., 12 (17): 2837 (2020)GEROS-ISS: GNSS REflectometry, Radio Occultation, and Scatterometry Onboard the International Space Station., , , , , , , , , and 21 other author(s). IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 9 (10): 4552-4581 (2016)Combining data assimilation and machine learning to emulate a dynamical model from sparse and noisy observations: A case study with the Lorenz 96 model., , , and . J. Comput. Sci., (2020)Innovative sea surface monitoring with GNSS-REflectometry aboard ISS: Overview and recent results from GEROS-ISS., , , , , , , , , and 15 other author(s). IGARSS, page 5611-5612. IEEE, (2016)