Inverse Modeling of the Ocean and Atmosphere

By Dimitris Menemenlis. 2002

337 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/4ac296a40bce52766fcf8a0bdd3d52c279af3951

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In Oceanography

Citations in the corpus

Non-commercial Research and Educational Use including without Limitation Use in Instruction at Your Institution, Sending It to Specific Colleagues That You Know, and Providing a Copy to Your Institution's Administrator. All Other Uses, Reproduction and Distribution, including without Limitation Comm (2005, neutral): “We mention selective deuteration in conjunction with heteronuclear decoupling, multiple quantum NMR (MQ–NMR) techniques , computer experiments such as molecular dynamics or Monte Carlo type simulations to estimate the degree of orientational order, and the use of 'magic mixtures' in combination…”
Collective Motion, Sensor Networks, and Ocean Sampling (2007, neutral): “As seen in Figure 3, the Slocum gliders traveled around approximately trapezoidal racetracks closer to shore, other than when used for coordination experiments as described next.”
Data assimilation: Mathematical and statistical perspectives (2008, background): “Geophysical applications [1], the atmospheric sciences [2] and oceanography [3] provide important application areas of this type.”
The Ensemble Kalman Filter: theoretical formulation and practical implementation (2003, methodology): “Note that, due to the use of white noise this curve will have discontinuous time derivatives at the measurement locations, a property also found in the representer solutions by Bennett (1992, 2002) when white model errors are used.”
Estimation of Spatially Distributed Processes Using Mobile Spatially Distributed Sensor Network (2009, background): “, salinity or temperature) satisfies a particular PDE [9, 54, 78, 79].”
Sampling the posterior: An approach to non-Gaussian data assimilation (2007, methodology): “The second observation is that, in the language of signal processing, the Bayesian method proposed here is performing smoothing, not filtering.”
A coupled data assimilation system for climate reanalysis (2016, methodology): “, 2012a) with its 3D-Var FGAT configuration (Bennett, 2002), which does not required tangent linear and adjoint models.”
Bayesian inverse problems for functions and applications to fluid mechanics (2009, background): “The book [5] provides a very clear discussion of variational methods and, of particular relevance to the development in this paper, develops a view of the subject in function space.”
The Regional Ocean Modeling System (ROMS) 4-dimensional variational data assimilation systems Part I - System overview and formulation (2011, methodology): “Excellent reviews and seminal texts on data assimilation include Bengtsson et al. (1981), Tarantola (1987), Daley (1991), Ghil and Malanotte-Rizzoli (1991), Bennett (1992, 2002) and Wunsch (1996, 2006).”
Data Assimilation: A Mathematical Introduction (2015, background): “The book [66] describes data assimilation from the viewpoint of the atmospheric sciences and weather prediction, whilst the book [10]”
The ensemble Kalman filter for combined state and parameter estimation (2009, methodology): “In [26] and [27] this problem is formulated using a variational cost function that is minimized using the representer method [28], [29].”
Data Assimilation for Numerical Weather Prediction: A Review (2009, background): “The advent of optimal control theory of partial differential equations is attributed to Bellman starting in the late 1950s Bellman (1957) (the Hamilton-Jacobi-Bellman equation) and to Pontryagin et al. (1962) (Pontryagin’s minimum principle).”
MCMC Methods for Functions: ModifyingOld Algorithms to Make Them Faster (2012, background): “In weather forecasting and oceanography it is frequently of interest to determine the initial condition u for a PDE dynamical system modelling a fluid, given observations [3, 26].”
Inverse problems: A Bayesian perspective (2010, methodology): “more generally, the variational method in the applied literature (Bennett 2002, Evensen 2006). The book by Engl et al. (1996) discusses regularization techniques in the Hilbert space setting and the Banach space setting is discussed in, for example, the recent papers of Kaltenbacher, Schöpfer and Sc”
Source Inversion for Contaminant Plume Dispersion in Urban Environments Using Building-Resolving Simulations (2005, background): “General dispersion related inverse problems, however, often include non-linear processes (e.g., dispersion of chemically reacting substances) or are characterized by non-Gaussian probability distributions (Bennett, 2002).”
MAP estimators and their consistency in Bayesian nonparametric inverse problems (2013, methodology): “In our specific application to fluid mechanics, our analysis demonstrates that widely used variational methods [2] may be interpreted as MAP estimators for an appropriate Bayesian inverse problem and, in particular, that this interpretation, which is understood in the atmospheric sciences community ”
A Reanalysis of Ocean Climate Using Simple Ocean Data Assimilation (SODA) (2008, background): “In recent years there has been a proliferation of data assimilation algorithms that have been applied to problems in physical oceanography.”