Kody J. H. Law

Atmospheric Science · 2012–2015

A researcher in the Science corpus: 5 papers; trusts 50 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Atmospheric Science — 23 citations into it; #1,185 of 4,804 by trust here, #3,694 by citations
• Global and Planetary Change — 21 citations into it; #8,810 of 10,225 by trust here, #9,714 by citations
• Statistics and Probability — 20 citations into it; #11,141 of 12,932 by trust here, #9,233 by citations
• Artificial Intelligence — 18 citations into it; #23,935 of 35,345 by trust here, #28,481 by citations
• Ocean Engineering — 14 citations into it; #1,135 of 3,193 by trust here, #2,433 by citations
• Mechanical Engineering — 12 citations into it; #7,040 of 8,300 by trust here, #8,205 by citations
• Probabilistic & Bayesian Methods — 12 citations into it
• Environmental Engineering — 11 citations into it; #4,772 of 5,479 by trust here, #5,399 by citations

Whom Kody J. H. Law trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Andrew M. Stuart
2. Geir Evensen
3. Jeffrey L. Anderson
4. Dean S. Oliver
5. Simon L. Cotter
6. Masoumeh Dashti
7. Eugenia Kalnay
8. Dimitris Menemenlis
9. J. Whitaker
10. F. Liang

Who trusts Kody J. H. Law overall

1. Andrew M. Stuart
2. Tapio Schneider
3. Kaushik Bhattacharya
4. Heng Xiao
5. Shiwei Lan
6. Masoumeh Dashti
7. Karthik Duraisamy
8. Bamdad Hosseini
9. Konstantinos C. Zygalakis

Papers from Kody J. H. Law's perspective

Ordered by how strongly the researchers Kody J. H. Law trusts endorse each paper. Same corpus, a different order for every researcher.

1. An Ensemble Adjustment Kalman Filter for Data Assimilation
2. The Ensemble Kalman Filter: theoretical formulation and practical implementation
3. Deterministic nonperiodic flow
4. A Sequential Ensemble Kalman Filter for Atmospheric Data Assimilation
5. Ensemble Data Assimilation without Perturbed Observations
6. Atmospheric Modeling, Data Assimilation and Predictability
7. Inverse Modeling of the Ocean and Atmosphere
8. Monte Carlo Sampling Methods Using Markov Chains and Their Applications
9. Monte Carlo Statistical Methods
10. Markov Chains and Stochastic Stability

Their papers

• MAP estimators and their consistency in Bayesian nonparametric inverse problems (2013) — 181 citations
• Data Assimilation: A Mathematical Introduction (2015) — 319 citations
• Well-posedness and accuracy of the ensemble Kalman filter in discrete and continuous time (2013) — 142 citations
• Evaluation of Gaussian approximations for data assimilation in reservoir models (2012) — 78 citations
• Ensemble Kalman methods for inverse problems (2012) — 460 citations