Dean S. Oliver
Atmospheric Science · 2007–2017
A researcher in the Science corpus: 11 papers; trusts 40 researchers and is trusted by 56 researchers here, as inferred from the citation graph.
Subfields
- Atmospheric Science — 52 citations into it; #320 of 4,804 by trust here, #1,374 by citations
- Global and Planetary Change — 48 citations into it; #2,332 of 10,225 by trust here, #6,088 by citations
- Environmental Engineering — 20 citations into it; #859 of 5,479 by trust here, #2,232 by citations
- Ocean Engineering — 14 citations into it; #25 of 3,193 by trust here, #86 by citations
- Oceanography — 13 citations into it; #1,345 of 4,047 by trust here, #3,174 by citations
- Mechanical Engineering — 7 citations into it; #182 of 8,300 by trust here, #2,110 by citations
- Numerical Methods & Scientific Computing — 6 citations into it
- Artificial Intelligence — 5 citations into it
Whom Dean S. Oliver trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Jeffrey L. Anderson
- Geir Evensen
- Yan Chen
- P. Houtekamer
- Pavel Sakov
- Yaqing Gu
- H. Mitchell
- Alexandre Anozé Emerick
- Laurent Bertino
- Thomas M. Hamill
Who trusts Dean S. Oliver overall
- Yan Chen
- Louis J. Durlofsky
- Alexandre Anozé Emerick
- Jan Dirk Jansen
- Ahmed H. Elsheikh
- Albert C. Reynolds
- Wenyue Sun
- M. Iglesias
- Kody J. H. Law
- Sy T. Do
Papers from Dean S. Oliver's perspective
Ordered by how strongly the researchers Dean S. Oliver trusts endorse each paper. Same corpus, a different order for every researcher.
- An Ensemble Adjustment Kalman Filter for Data Assimilation
- The Ensemble Kalman Filter: theoretical formulation and practical implementation
- A Sequential Ensemble Kalman Filter for Atmospheric Data Assimilation
- Distance-dependent filtering of background error covariance estimates in an ensemble Kalman filter
- The potential of the ensemble Kalman filter for NWP—a comparison with 4D‐Var
- Ensemble Data Assimilation without Perturbed Observations
- Implications of Stochastic and Deterministic Filters as Ensemble-Based Data Assimilation Methods in Varying Regimes of Error Growth
- Hydrologic Data Assimilation with the Ensemble Kalman Filter
- Deterministic nonperiodic flow
- Ensemble size, balance, and model-error representation in an ensemble Kalman filter
Their papers
- Localization and regularization for iterative ensemble smoothers (2017) — 68 citations
- Levenberg–Marquardt forms of the iterative ensemble smoother for efficient history matching and uncertainty quantification (2013) — 342 citations
- An Iterative EnKF for Strongly Nonlinear Systems (2012) — 180 citations
- Recent progress on reservoir history matching: a review (2011) — 736 citations
- Ensemble Randomized Maximum Likelihood Method as an Iterative Ensemble Smoother (2011) — 349 citations
- Cross-covariances and localization for EnKF in multiphase flow data assimilation (2010) — 157 citations
- Improving the Ensemble Estimate of the Kalman Gain by Bootstrap Sampling (2010) — 61 citations
- Efficient Ensemble-Based Closed-Loop Production Optimization (2009) — 344 citations
- Data assimilation for nonlinear problems by ensemble Kalman filter with reparameterization (2009) — 72 citations
- Inverse Theory for Petroleum Reservoir Characterization and History Matching (2008) — 737 citations
- An Iterative Ensemble Kalman Filter for Multiphase Fluid Flow Data Assimilation (2007) — 302 citations