Scale-dependent background-error covariance localisation [PDF]
Tellus: Series A, Dynamic Meteorology and Oceanography, 2015 A new approach is presented and evaluated for efficiently applying scale-dependent spatial localisation to ensemble background-error covariances within an ensemble-variational data assimilation system.
Mark Buehner, Anna Shlyaeva
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Generalized background error covariance matrix model (GEN_BE v2.0) [PDF]
Geoscientific Model Development, 2015 The specification of state background error statistics is a key component of data assimilation since it affects the impact observations will have on the analysis.
G. Descombes +4 more
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A methodology to obtain model-error covariances due to the discretization scheme from the parametric Kalman filter perspective [PDF]
Nonlinear Processes in Geophysics, 2021 This contribution addresses the characterization of the model-error covariance matrix from the new theoretical perspective provided by the parametric Kalman filter method which approximates the covariance dynamics from the parametric evolution of a ...
O. Pannekoucke +6 more
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Collision Prediction of Spacecraft for Space Traffic Management [PDF]
فصلنامه علوم و فناوری فضایی, 2022 In this article collision probability method is used to satellite collision risk analysis. Among different methods introduced for determining collision probability, Patera's (2005) and Chan methods are chosen to define Noor satellite collision to the ...
Hamid Kazemi, Samaneh Elahian
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Initialization of SINS/GNSS Error Covariance Matrix Based on Error States Correlation
IEEE Access, 2023 The traditional Strapdown Inertial Navigation System (SINS)/Global Navigation Satellite System (GNSS) integrated system uses standard Kalman Filter (KF) to estimate the error states, which weakens the correlation between the different error components to
Jun Tang +3 more
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Some quantitative characteristics of error covariance for Kalman filters
Tellus: Series A, Dynamic Meteorology and Oceanography, 2021 Some quantitative characteristics of error covariance are studied for linear Kalman filters. These quantitative characteristics include the peak value and location in the matrix, the decay rate from peak to bottom, and some algebraic constraints of the ...
Wei Kang, Liang Xu
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pyGNMF: A Python library for implementation of generalised non-negative matrix factorisation method
SoftwareX, 2022 This article introduces a Python library named pyGNMF, which implements the recently proposed generalised non-negative matrix factorisation (GNMF) method.
Nirav L. Lekinwala, Mani Bhushan
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Structure of forecast error covariance in coupled atmosphere–chemistry data assimilation [PDF]
Geoscientific Model Development, 2015 In this study, we examined the structure of an ensemble-based coupled atmosphere–chemistry forecast error covariance. The Weather Research and Forecasting (WRF) model coupled with Chemistry (WRF-Chem), a coupled atmosphere–chemistry model, was used to ...
S. K. Park, S. Lim, M. Zupanski
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Impact of different estimations of the background-error covariance matrix on climate reconstructions based on data assimilation [PDF]
Climate of the Past, 2019 Data assimilation has been adapted in paleoclimatology to reconstruct past climate states. A key component of some assimilation systems is the background-error covariance matrix, which controls how the information from observations spreads into the model
V. Valler +5 more
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Sparse Approximation of the Precision Matrices for the Wide-Swath Altimeters
Remote Sensing, 2022 The upcoming technology of wide-swath altimetry from space will deliver a large volume of data on the ocean surface at unprecedentedly high spatial resolution.
Max Yaremchuk
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