Stochastic parameterization: Toward a new view of weather and climate models J Berner, U Achatz, L Batte, L Bengtsson, A De La Camara, ... Bulletin of the American Meteorological Society 98 (3), 565-588, 2017 | 404 | 2017 |
Strategies for model reduction: Comparing different optimal bases DT Crommelin, AJ Majda Journal of the Atmospheric Sciences 61 (17), 2206-2217, 2004 | 161 | 2004 |
Subgrid-scale parameterization with conditional Markov chains D Crommelin, E Vanden-Eijnden Journal of the Atmospheric Sciences 65 (8), 2661-2675, 2008 | 151 | 2008 |
Normal forms for reduced stochastic climate models AJ Majda, C Franzke, D Crommelin Proceedings of the National Academy of Sciences 106 (10), 3649-3653, 2009 | 134 | 2009 |
Stochastic parameterization of shallow cumulus convection estimated from high-resolution model data J Dorrestijn, DT Crommelin, AP Siebesma, HJJ Jonker Theoretical and Computational Fluid Dynamics, 1-16, 2012 | 96 | 2012 |
Distinct metastable atmospheric regimes despite nearly Gaussian statistics: A paradigm model AJ Majda, CL Franzke, A Fischer, DT Crommelin Proceedings of the National Academy of Sciences 103 (22), 8309-8314, 2006 | 94 | 2006 |
A hidden Markov model perspective on regimes and metastability in atmospheric flows C Franzke, D Crommelin, A Fischer, AJ Majda Journal of Climate 21 (8), 1740-1757, 2008 | 89 | 2008 |
Stochastic climate theory GA Gottwald, DT Crommelin, CLE Franzke arXiv preprint arXiv:1612.07474, 2016 | 87 | 2016 |
A mechanism for atmospheric regime behaviour DT Crommelin, JD Opsteegh, F Verhulst J. Atmos. Sci 61, 1406-1419, 2004 | 83 | 2004 |
Regime transitions and heteroclinic connections in a barotropic atmosphere DT Crommelin Journal of the atmospheric sciences 60 (2), 229-246, 2003 | 82 | 2003 |
Fitting timeseries by continuous-time Markov chains: A quadratic programming approach DT Crommelin, E Vanden-Eijnden Journal of Computational Physics 217 (2), 782-805, 2006 | 81 | 2006 |
The impact of uncertainty on predictions of the CovidSim epidemiological code W Edeling, H Arabnejad, R Sinclair, D Suleimenova, K Gopalakrishnan, ... Nature Computational Science 1 (2), 128-135, 2021 | 73 | 2021 |
Reconstruction of diffusions using spectral data from timeseries D Crommelin, E Vanden-Eijnden Communications in Mathematical Sciences 4 (3), 651-668, 2006 | 56 | 2006 |
Diffusion estimation from multiscale data by operator eigenpairs D Crommelin, E Vanden-Eijnden Multiscale Modeling & Simulation 9 (4), 1588-1623, 2011 | 52 | 2011 |
Homoclinic dynamics: a scenario for atmospheric ultralow-frequency variability DT Crommelin Journal of the Atmospheric Sciences 59 (9), 1533-1549, 2002 | 45 | 2002 |
Stochastic parameterization of convective area fractions with a multicloud model inferred from observational data J Dorrestijn, DT Crommelin, AP Siebesma, HJJ Jonker, C Jakob Journal of the Atmospheric Sciences 72 (2), 854-869, 2015 | 44 | 2015 |
Observed nondiffusive dynamics in large-scale atmospheric flow DT Crommelin Journal of the atmospheric sciences 61 (19), 2384-2396, 2004 | 44 | 2004 |
A data-driven multicloud model for stochastic parameterization of deep convection J Dorrestijn, DT Crommelin, JA Biello, SJ Böing | 41 | 2012 |
Stochastic convection parameterization with Markov chains in an intermediate-complexity GCM J Dorrestijn, DT Crommelin, AP Siebesma, HJJ Jonker, F Selten Journal of the Atmospheric Sciences 73 (3), 1367-1382, 2016 | 36 | 2016 |
Hidden Markov models for wind farm power output D Bhaumik, D Crommelin, S Kapodistria, B Zwart IEEE Transactions on Sustainable Energy 10 (2), 533-539, 2018 | 32 | 2018 |