| Deep learning to represent subgrid processes in climate models S Rasp, MS Pritchard, P Gentine Proceedings of the national academy of sciences 115 (39), 9684-9689, 2018 | 819 | 2018 |
| Could machine learning break the convection parameterization deadlock? P Gentine, M Pritchard, S Rasp, G Reinaudi, G Yacalis Geophysical Research Letters 45 (11), 5742-5751, 2018 | 452 | 2018 |
| Enforcing analytic constraints in neural networks emulating physical systems T Beucler, M Pritchard, S Rasp, J Ott, P Baldi, P Gentine Physical Review Letters 126 (9), 098302, 2021 | 292 | 2021 |
| The ongoing need for high-resolution regional climate models: Process understanding and stakeholder information WJ Gutowski, PA Ullrich, A Hall, LR Leung, TA O’Brien, CM Patricola, ... Bulletin of the American Meteorological Society 101 (5), E664-E683, 2020 | 153 | 2020 |
| Zonally contrasting shifts of the tropical rain belt in response to climate change A Mamalakis, JT Randerson, JY Yu, MS Pritchard, G Magnusdottir, ... Nature climate change 11 (2), 143-151, 2021 | 141 | 2021 |
| A fortran‐keras deep learning bridge for scientific computing J Ott, M Pritchard, N Best, E Linstead, M Curcic, P Baldi Scientific Programming 2020 (1), 8888811, 2020 | 128 | 2020 |
| Interpreting and stabilizing machine-learning parametrizations of convection ND Brenowitz, T Beucler, M Pritchard, CS Bretherton Journal of the Atmospheric Sciences 77 (12), 4357-4375, 2020 | 118 | 2020 |
| Orogenic propagating precipitation systems over the United States in a global climate model with embedded explicit convection MS Pritchard, MW Moncrieff, RCJ Somerville Journal of the Atmospheric Sciences 68 (8), 1821-1840, 2011 | 118 | 2011 |
| Forest response to rising CO2 drives zonally asymmetric rainfall change over tropical land GJ Kooperman, Y Chen, FM Hoffman, CD Koven, K Lindsay, MS Pritchard, ... Nature Climate Change 8 (5), 434-440, 2018 | 111 | 2018 |
| Constraining the influence of natural variability to improve estimates of global aerosol indirect effects in a nudged version of the Community Atmosphere Model 5 GJ Kooperman, MS Pritchard, SJ Ghan, M Wang, RCJ Somerville, ... Journal of Geophysical Research: Atmospheres 117 (D23), 2012 | 104 | 2012 |
| Causal evidence that rotational moisture advection is critical to the superparameterized Madden–Julian oscillation MS Pritchard, CS Bretherton Journal of the Atmospheric Sciences 71 (2), 800-815, 2014 | 92 | 2014 |
| Achieving conservation of energy in neural network emulators for climate modeling T Beucler, S Rasp, M Pritchard, P Gentine arXiv preprint arXiv:1906.06622, 2019 | 84 | 2019 |
| Robust effects of cloud superparameterization on simulated daily rainfall intensity statistics across multiple versions of the Community Earth System Model G Kooperman, MS Pritchard, M Burt, MD Branson, DA Randall Journal of Advances in Modeling Earth Systems 8, 2016 | 81 | 2016 |
| Assessing the diurnal cycle of precipitation in a multi‐scale climate model MS Pritchard, RCJ Somerville Journal of Advances in Modeling Earth Systems 1 (4), 2009 | 78 | 2009 |
| Why Does Amazon Precipitation Decrease When Tropical Forests Respond to Increasing CO2? B Langenbrunner, MS Pritchard, GJ Kooperman, JT Randerson Earth's future 7 (4), 450-468, 2019 | 62 | 2019 |
| Vertical structure and physical processes of the Madden‐Julian oscillation: Linking hindcast fidelity to simulated diabatic heating and moistening NP Klingaman, SJ Woolnough, X Jiang, D Waliser, PK Xavier, J Petch, ... Journal of Geophysical Research: Atmospheres 120 (10), 4690-4717, 2015 | 58 | 2015 |
| Toward low‐cloud‐permitting cloud superparameterization with explicit boundary layer turbulence H Parishani, MS Pritchard, CS Bretherton, MC Wyant, M Khairoutdinov Journal of Advances in Modeling Earth Systems 9 (3), 1542-1571, 2017 | 56 | 2017 |
| Empirical orthogonal function analysis of the diurnal cycle of precipitation in a multi‐scale climate model MS Pritchard, RCJ Somerville Geophysical Research Letters 36 (5), 2009 | 52 | 2009 |
| The effect of plant physiological responses to rising CO2 on global streamflow MD Fowler, GJ Kooperman, JT Randerson, MS Pritchard Nature Climate Change 9 (11), 873-879, 2019 | 51 | 2019 |
| Climate-invariant machine learning T Beucler, P Gentine, J Yuval, A Gupta, L Peng, J Lin, S Yu, S Rasp, ... Science Advances 10 (6), eadj7250, 2024 | 50 | 2024 |
Pierre GentineProfessor @ Columbia University - Director NSF LEAP STCVerified email at columbia.edu
