| Stochastic parameterization: Toward a new view of weather and climate models J Berner, U Achatz, L Batte, L Bengtsson, A Cámara, HM Christensen, ... Bulletin of the American Meteorological Society 98 (3), 565-588, 2017 | 414 | 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 | 153 | 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 | 97 | 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 |
| Stochastic climate theory G Gottwald, DT Crommelin, CLE Franzke Nonlinear and stochastic climate dynamics, 209-240, 2017 | 91 | 2017 |
| 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 |
| A mechanism for atmospheric regime behaviour DT Crommelin, JD Opsteegh, F Verhulst J. Atmos. Sci 61, 1406-1419, 2004 | 85 | 2004 |
| Regime transitions and heteroclinic connections in a barotropic atmosphere DT Crommelin Journal of the atmospheric sciences 60 (2), 229-246, 2003 | 83 | 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 | 80 | 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 | 78 | 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 |
| 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 | 45 | 2015 |
| Observed nondiffusive dynamics in large-scale atmospheric flow DT Crommelin Journal of the atmospheric sciences 61 (19), 2384-2396, 2004 | 45 | 2004 |
| Homoclinic dynamics: a scenario for atmospheric ultralow-frequency variability DT Crommelin Journal of the Atmospheric Sciences 59 (9), 1533-1549, 2002 | 45 | 2002 |
| 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 | 37 | 2016 |
| Building confidence in simulation: applications of EasyVVUQ DW Wright, RA Richardson, W Edeling, J Lakhlili, RC Sinclair, ... Advanced Theory and Simulations 3 (8), 1900246, 2020 | 33 | 2020 |