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Multiplicative non‐gaussian model error estimation in data assimilation

Pathiraja, S. ORCID: and Van Leeuwen, P. J. ORCID: (2022) Multiplicative non‐gaussian model error estimation in data assimilation. Journal of Advances in Modeling Earth Systems, 14 (4). e2021MS002564. ISSN 1942-2466

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To link to this item DOI: 10.1029/2021ms002564


Abstract: Model uncertainty quantification is an essential component of effective data assimilation. Model errors associated with sub‐grid scale processes are often represented through stochastic parameterizations of the unresolved process. Many existing Stochastic Parameterization schemes are only applicable when knowledge of the true sub‐grid scale process or full observations of the coarse scale process are available, which is typically not the case in real applications. We present a methodology for estimating the statistics of sub‐grid scale processes for the more realistic case that only partial observations of the coarse scale process are available. Model error realizations are estimated over a training period by minimizing their conditional sum of squared deviations given some informative covariates (e.g., state of the system), constrained by available observations and assuming that the observation errors are smaller than the model errors. From these realizations a conditional probability distribution of additive model errors given these covariates is obtained, allowing for complex non‐Gaussian error structures. Random draws from this density are then used in actual ensemble data assimilation experiments. We demonstrate the efficacy of the approach through numerical experiments with the multi‐scale Lorenz 96 system using both small and large time scale separations between slow (coarse scale) and fast (fine scale) variables. The resulting error estimates and forecasts obtained with this new method are superior to those from two existing methods.

Item Type:Article
Divisions:Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
ID Code:104706
Uncontrolled Keywords:ATMOSPHERIC COMPOSITION AND STRUCTURE, Evolution of the atmosphere, Biosphere/atmosphere interactions, BIOGEOSCIENCES, EXPLORATION GEOPHYSICS, Gravity methods, GEODESY AND GRAVITY, Transient deformation, Tectonic deformation, Time variable gravity, Gravity anomalies and Earth structure, Satellite geodesy: results, Seismic cycle related deformations, GLOBAL CHANGE, Atmosphere, HYDROLOGY, Estimation and forecasting, Uncertainty assessment, INFORMATICS, Data assimilation, integration and fusion, Forecasting, Numerical algorithms, Uncertainty, IONOSPHERE, MAGNETOSPHERIC PHYSICS, MATHEMATICAL GEOPHYSICS, Prediction, Probabilistic forecasting, Uncertainty quantification, OCEANOGRAPHY: GENERAL, Ocean predictability and prediction, NATURAL HAZARDS, Monitoring, forecasting, prediction, POLICY SCIENCES, RADIO SCIENCE, Interferometry, Ionospheric physics, SEISMOLOGY, Continental crust, Earthquake dynamics, Earthquake source observations, Earthquake interaction, forecasting, and prediction, Seismicity and tectonics, Subduction zones, SPACE WEATHER, Policy, TECTONOPHYSICS, Evolution of the Earth, Research Article, model uncertainty, non‐Gaussian, data‐driven, uncertainty quantification, Lorenz 96, sub‐grid scale
Additional Information:** Article version: VoR ** From Wiley via Jisc Publications Router ** Licence for VoR version of this article: ** Journal IDs: issn 1942-2466 ** Article IDs: publisher-id: jame21551; society-id: 2021ms002564 ** History: published 14-04-2022; published 04-2022; accepted 30-12-2021; rev-recd 21-12-2021; submitted 10-04-2021
Publisher:American Geophysical Union


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