{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T22:58:50Z","timestamp":1769727530616,"version":"3.49.0"},"reference-count":48,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2024,6,28]],"date-time":"2024-06-28T00:00:00Z","timestamp":1719532800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000001","name":"US National Science Foundation","doi-asserted-by":"publisher","award":["2152814"],"award-info":[{"award-number":["2152814"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Ensemble Kalman filters are an efficient class of algorithms for large-scale ensemble data assimilation, but their performance is limited by their underlying Gaussian approximation. A two-step framework for ensemble data assimilation allows this approximation to be relaxed: The first step updates the ensemble in observation space, while the second step regresses the observation state update back to the state variables. This paper develops a new quantile-conserving ensemble filter based on kernel-density estimation and quadrature for the scalar first step of the two-step framework. It is shown to perform well in idealized non-Gaussian problems, as well as in an idealized model of assimilating observations of sea-ice concentration.<\/jats:p>","DOI":"10.3390\/rs16132377","type":"journal-article","created":{"date-parts":[[2024,6,28]],"date-time":"2024-06-28T08:31:36Z","timestamp":1719563496000},"page":"2377","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["A Quantile-Conserving Ensemble Filter Based on Kernel-Density Estimation"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4678-7203","authenticated-orcid":false,"given":"Ian","family":"Grooms","sequence":"first","affiliation":[{"name":"Department of Applied Mathematics, University of Colorado, Boulder, CO 80309, USA"}]},{"given":"Christopher","family":"Riedel","sequence":"additional","affiliation":[{"name":"University Corporation for Atmospheric Research, Boulder, CO 80309, USA"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"10143","DOI":"10.1029\/94JC00572","article-title":"Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics","volume":"99","author":"Evensen","year":"1994","journal-title":"J. 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