Search Results - "Lawless, Amos S."

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  1. 1

    Treating Sample Covariances for Use in Strongly Coupled Atmosphere‐Ocean Data Assimilation by Smith, Polly J., Lawless, Amos S., Nichols, Nancy K.

    Published in Geophysical research letters (16-01-2018)
    “…Strongly coupled data assimilation requires cross‐domain forecast error covariances; information from ensembles can be used, but limited sampling means that…”
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  2. 2

    Exploring strategies for coupled 4D-Var data assimilation using an idealised atmosphere-ocean model by Smith, Polly J., Fowler, Alison M., Lawless, Amos S.

    “…Operational forecasting centres are currently developing data assimilation systems for coupled atmosphere-ocean models. Strongly coupled assimilation, in which…”
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  3. 3

    Improving the condition number of estimated covariance matrices by Tabeart, Jemima M., Dance, Sarah L., Lawless, Amos S., Nichols, Nancy K., Waller, Joanne A.

    “…High dimensional error covariance matrices and their inverses are used to weight the contribution of observation and background information in data…”
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  4. 4
  5. 5

    Quantifying the latitudinal representivity of in situ solar wind observations by Owens, Mathew J., Lang, Matthew, Riley, Pete, Lockwood, Mike, Lawless, Amos S.

    “…Advanced space-weather forecasting relies on the ability to accurately predict near-Earth solar wind conditions. For this purpose, physics-based, global…”
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  6. 6

    Estimating correlated observation error statistics using an ensemble transform Kalman filter by Waller, Joanne A., Dance, Sarah L., Lawless, Amos S., Nichols, Nancy K.

    “…For certain observing types, such as those that are remotely sensed, the observation errors are correlated and these correlations are state- and…”
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  7. 7

    Parameter estimation for a morphochemical reaction-diffusion model of electrochemical pattern formation by Sgura, Ivonne, Lawless, Amos S., Bozzini, Benedetto

    “…The process of electrodeposition can be described in terms of a reaction-diffusion partial differential equation (PDE) system that models the dynamics of the…”
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  8. 8

    Assessment of short‐range forecast error atmosphere–ocean cross‐correlations from the Met Office coupled numerical weather prediction system by Wright, Azin, Lawless, Amos S., Nichols, Nancy K., Lea, Daniel J., Martin, Matthew J.

    “…Operational data assimilation systems for coupled atmosphere–ocean prediction are usually “weakly coupled”, in which there is no explicit interaction between…”
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  9. 9

    Conditioning of hybrid variational data assimilation by Shataer, Shaerdan, Lawless, Amos S., Nichols, Nancy K.

    Published in Numerical linear algebra with applications (01-03-2024)
    “…In variational assimilation, the most probable state of a dynamical system under Gaussian assumptions for the prior and likelihood can be found by solving a…”
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  10. 10

    The effective use of anchor observations in variational bias correction in the presence of model bias by Francis, Devon J., Fowler, Alison M., Lawless, Amos S., Eyre, John, Migliorini, Stefano

    “…In numerical weather prediction, satellite radiance observations have a significant impact on forecast skill. However, radiance observations must usually be…”
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  11. 11

    Convergent least-squares optimization methods for variational data assimilation by Cartis, Coralia, Kaouri, Maha H., Lawless, Amos S., Nichols, Nancy K.

    Published in Optimization (01-11-2024)
    “…Data assimilation combines prior (or background) information with observations to estimate the initial state of a dynamical system over a given time-window. A…”
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  12. 12

    The role of cross‐domain error correlations in strongly coupled 4D‐Var atmosphere–ocean data assimilation by Smith, Polly J., Lawless, Amos S., Nichols, Nancy K.

    “…Strongly coupled atmosphere–ocean data assimilation offers the ability to improve information exchange across the modelled air‐sea interface by enabling…”
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  13. 13

    On time‐parallel preconditioning for the state formulation of incremental weak constraint 4D‐Var by Daužickaitė, Ieva, Lawless, Amos S., Scott, Jennifer A., Leeuwen, Peter Jan

    “…Using a high degree of parallelism is essential for the efficient performance of data assimilation. The state formulation of the incremental weak constraint…”
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  14. 14

    Randomised preconditioning for the forcing formulation of weak‐constraint 4D‐Var by Daužickaitė, Ieva, Lawless, Amos S., Scott, Jennifer A., Leeuwen, Peter Jan

    “…There is growing awareness that errors in the model equations cannot be ignored in data assimilation methods such as four‐dimensional variational assimilation…”
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  15. 15

    The impact of hybrid oceanic data assimilation in a coupled model: A case study of a tropical cyclone by Leung, Tsz Yan, Lawless, Amos S., Nichols, Nancy K., Lea, Daniel J., Martin, Matthew J.

    “…Tropical cyclones tend to result in distinctive spatial and temporal characteristics in the upper ocean, which suggests that traditional, parametrisation‐based…”
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  16. 16

    The impact of using reconditioned correlated observation‐error covariance matrices in the Met Office 1D‐Var system by Tabeart, Jemima M., Dance, Sarah L., Lawless, Amos S., Migliorini, Stefano, Nichols, Nancy K., Smith, Fiona, Waller, Joanne A.

    “…Recent developments in numerical weather prediction have led to the use of correlated observation‐error covariance (OEC) information in data assimilation and…”
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  17. 17

    New bounds on the condition number of the Hessian of the preconditioned variational data assimilation problem by Tabeart, Jemima M., Dance, Sarah L., Lawless, Amos S., Nichols, Nancy K., Waller, Joanne A.

    Published in Numerical linear algebra with applications (01-01-2022)
    “…Data assimilation algorithms combine prior and observational information, weighted by their respective uncertainties, to obtain the most likely posterior of a…”
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  18. 18

    The conditioning of least‐squares problems in variational data assimilation by Tabeart, Jemima M., Dance, Sarah L., Haben, Stephen A., Lawless, Amos S., Nichols, Nancy K., Waller, Joanne A.

    Published in Numerical linear algebra with applications (01-10-2018)
    “…Summary In variational data assimilation a least‐squares objective function is minimised to obtain the most likely state of a dynamical system. This objective…”
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  19. 19

    Spectral estimates for saddle point matrices arising in weak constraint four‐dimensional variational data assimilation by Daužickaitė, Ieva, Lawless, Amos S., Scott, Jennifer A., Leeuwen, Peter Jan

    Published in Numerical linear algebra with applications (01-10-2020)
    “…Summary We consider the large sparse symmetric linear systems of equations that arise in the solution of weak constraint four‐dimensional variational data…”
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  20. 20

    Weak constraints in four-dimensional variational data assimilation by Laura R. Watkinson, Amos S. Lawless, Nancy K. Nichols, Ian Roulstone

    “…The formulation of four-dimensional variational data assimilation allows the incorporation of constraints into the cost function which need only be weakly…”
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