Data assimilation using a global Girsanov nudged particle filter

Data assimilation using a global Girsanov nudged particle filter | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Data assimilation using a global Girsanov nudged particle filter Maneesh Kumar Singh, Joshua Hope-Collins, Dan Crisan, Colin J. Cotter This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7207017/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract We present a particle filtering algorithm for stochastic models on infinite dimensional state space, making use of Girsanov perturbations to nudge the ensemble of particles into regions of higher likelihood. We argue that the optimal control problem needs to couple control variables for all of the particles to maintain an ensemble with good effective sample size (ESS). We provide an optimisation formulation that separates the problem into three stages, separating the nonlinearity in the ESS term in the functional with the nonlinearity due to the forward problem, and allowing independent parallel computation for each particle when calculations are performed over control variable space. The particle filter is applied to the stochastic Kuramoto-Sivashinsky equation, and compared with the temper-jitter particle filter approach. We observe that whilst the nudging filter is over spread compared to the temper-jitter filter, it responds to extreme events in the assimilated data more quickly and robustly. Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 16 Nov, 2025 Reviews received at journal 16 Nov, 2025 Reviews received at journal 31 Oct, 2025 Reviewers agreed at journal 04 Aug, 2025 Reviewers agreed at journal 04 Aug, 2025 Reviewers agreed at journal 02 Aug, 2025 Reviewers agreed at journal 01 Aug, 2025 Reviewers agreed at journal 29 Jul, 2025 Reviewers invited by journal 28 Jul, 2025 Editor assigned by journal 28 Jul, 2025 Submission checks completed at journal 28 Jul, 2025 First submitted to journal 24 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7207017","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":492253704,"identity":"91df7113-02bd-40fc-bb0d-60ba9d81381d","order_by":0,"name":"Maneesh Kumar Singh","email":"","orcid":"","institution":"Imperial College London","correspondingAuthor":false,"prefix":"","firstName":"Maneesh","middleName":"Kumar","lastName":"Singh","suffix":""},{"id":492253705,"identity":"5cf86c93-e6d4-4129-b5be-517c46c43aa1","order_by":1,"name":"Joshua Hope-Collins","email":"","orcid":"","institution":"Imperial College London","correspondingAuthor":false,"prefix":"","firstName":"Joshua","middleName":"","lastName":"Hope-Collins","suffix":""},{"id":492253708,"identity":"9e452cdf-7e90-4750-9c96-599bea7480e7","order_by":2,"name":"Dan Crisan","email":"","orcid":"","institution":"Imperial College London","correspondingAuthor":false,"prefix":"","firstName":"Dan","middleName":"","lastName":"Crisan","suffix":""},{"id":492253709,"identity":"43423de3-7fdb-4426-9288-75233b4e9633","order_by":3,"name":"Colin J. 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