Spatiotemporal Hierarchical Bayesian Modeling Tropical Ocean Surface Winds

By Christopher K. Wikle, R. F. Milli, D. Nychka, M. Berliner. 2001

359 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/0adcb3155b4174cb5df8170680c3d7b339d45306

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Citations in the corpus

Recent developments on the construction of spatio-temporal covariance models (2008, background): “See, for instance, Handcock and Wallis (1994), Onof et al. (1996), Christakos and Hristopulos (1998), Haas (1998), Meiring et al. (1998), Northrop (1998), Wikle et al. (1998), Cressie and Huang (1999), Wikle and Cressie (1999), Christakos (2000), Knorr-Held (2000), Wheater et al. (2000), Brown et al”
Powering Up With Space-Time Wind Forecasting (2010, methodology): “Either model-free bootstrapping techniques (Alonso, Peña, and Romo 2006) or using a fully Bayesian analysis (Wikle et al. 2001) could be interesting approaches to obtain such intervals.”
Hierarchical statistical modeling of big spatial datasets using the exponential family of distributions (2013, methodology): “We could usethe same data model and a process model where the reduced-dimensional basis function coefficients evolve over time (e.g., Wikle et al., 2001; Cressie et al., 2010).”
Hierarchical Bayesian Models for Predicting The Spread of Ecological Processes (2003, methodology): “For complicated spatio-temporal applications of these methods, see Wikle et al. (1998), Berliner et al. (2000), and Wikle et al. (2001).”
Multivariate Bayesian analysis of atmosphere–ocean general circulation models (2007, background): “The physical processes driving the climatological responses are extremely complex and it would be virtually impossible to pursue a physical prior modeling approach as illustrated for example in Wikle et al. (2001).”
Non-homogeneous hidden Markov-switching models for wind time series (2015, background): “VAR models have been proposed for wind fields in a space-time context in [2, 25, 13].”
Curse-of-dimensionality revisited: Collapse of the particle filter in very large scale systems (2008, background): “[16, 28]), but require computationally heavy, chain-based sampling and thus do not extend in any obvious manner to real-time applications.”
A MULTIVARIATE SEMIPARAMETRIC BAYESIAN SPATIAL MODELING FRAMEWORK FOR HURRICANE SURFACE WIND FIELDS (2007, background): “This is the approach of Wikle et al. (2001) for oceanic surface winds.”
Basis-Function Models in Spatial Statistics (2021, methodology): “Basis functions have often been used in dynamic spatio-temporal models, such as in Wikle & Cressie (1999), Wikle et al. (2001), Stroud et al. (2001), Sahu & Mardia (2005), Sansó et al. (2008), Dewar et al. (2009), Cressie et al. (2010), Freestone et al. (2011), Cressie & Wikle (2011), Katzfuss &…”
Why environmental scientists are becoming Bayesians (2004, methodology): “…latent variables), pðparametersjmodel,dataÞ/ pðdatajprocess,data parametersÞ pðprocessjprocess parametersÞ pðall parametersÞ This decomposition comes from Berliner (1996), and has been adopted in a number of recent applications (Wikle et al. 2001; Clark et al. 2003, 2004; Clark 2003; Wikle 2003a).”
Space‐time modelling of trends in temperature series (2011, methodology): “Previously, Berliner et al. (1999) and Wikle et al. (2001) used hierarchical Bayesian space-time models with wavelet bases in their definition.”
Quantifying and reducing model-form uncertainties in Reynolds-averaged Navier-Stokes simulations: A data-driven, physics-informed Bayesian approach (2015, background): “[20, 21] in the context of geophysical fluid dynamics, where uncertainties were introduced to the discretized coefficients of the governing geostrophic equations.”
Combining Field Data and Computer Simulations for Calibration and Prediction (2005, methodology): “Another promising approach is to replace the simulator altogether with a statistical model that incorporates key features of the physical process as in Wikle et al.(2001) .”