| Correcting weather and climate models by machine learning nudged historical simulations O Watt‐Meyer, ND Brenowitz, SK Clark, B Henn, A Kwa, J McGibbon, ... Geophysical Research Letters 48 (15), e2021GL092555, 2021 | 71 | 2021 |
| Cloud System Evolution in the Trades (CSET): Following the evolution of boundary layer cloud systems with the NSF–NCAR GV B Albrecht, V Ghate, J Mohrmann, R Wood, P Zuidema, C Bretherton, ... Bulletin of the American Meteorological Society 100 (1), 93-121, 2019 | 67 | 2019 |
| Correcting coarse‐grid weather and climate models by machine learning from global storm‐resolving simulations CS Bretherton, B Henn, A Kwa, ND Brenowitz, O Watt‐Meyer, J McGibbon, ... Journal of Advances in Modeling Earth Systems 14 (2), e2021MS002794, 2022 | 50 | 2022 |
| Skill of ship‐following large‐eddy simulations in reproducing MAGIC observations across the northeast P acific stratocumulus to cumulus transition region J McGibbon, CS Bretherton Journal of Advances in Modeling Earth Systems 9 (2), 810-831, 2017 | 33 | 2017 |
| Machine learning climate model dynamics: Offline versus online performance ND Brenowitz, B Henn, J McGibbon, SK Clark, A Kwa, WA Perkins, ... arXiv preprint arXiv:2011.03081, 2020 | 31 | 2020 |
| Correcting a 200 km resolution climate model in multiple climates by machine learning from 25 km resolution simulations SK Clark, ND Brenowitz, B Henn, A Kwa, J McGibbon, WA Perkins, ... Journal of Advances in Modeling Earth Systems 14 (9), e2022MS003219, 2022 | 21 | 2022 |
| Lagrangian evolution of the Northeast Pacific marine boundary layer structure and cloud during CSET J Mohrmann, CS Bretherton, IL McCoy, J McGibbon, R Wood, V Ghate, ... Monthly weather review 147 (12), 4681-4700, 2019 | 20 | 2019 |
| sympl (v. 0.4. 0) and climt (v. 0.15. 3)–towards a flexible framework for building model hierarchies in Python JM Monteiro, J McGibbon, R Caballero Geoscientific Model Development 11 (9), 3781-3794, 2018 | 17 | 2018 |
| Single‐column emulation of reanalysis of the northeast Pacific marine boundary layer J McGibbon, CS Bretherton Geophysical Research Letters 46 (16), 10053-10060, 2019 | 14 | 2019 |
| Drivers of seasonal variability in marine boundary layer aerosol number concentration investigated using a steady state approach J Mohrmann, R Wood, J McGibbon, R Eastman, E Luke Journal of Geophysical Research: Atmospheres 123 (2), 1097-1112, 2018 | 12 | 2018 |
| Productive performance engineering for weather and climate modeling with python T Ben-Nun, L Groner, F Deconinck, T Wicky, E Davis, J Dahm, OD Elbert, ... SC22: International Conference for High Performance Computing, Networking …, 2022 | 11 | 2022 |
| fv3gfs-wrapper: a Python wrapper of the FV3GFS atmospheric model J McGibbon, ND Brenowitz, M Cheeseman, SK Clark, J Dahm, E Davis, ... Geoscientific Model Development Discussions 2021, 1-14, 2021 | 11 | 2021 |
| sympl (v. 0.4. 0) and climt (v. 0.15. 3)–towards a flexible framework for building model hierarchies in Python, Geosci. Model Dev., 11, 3781–3794 JM Monteiro, J McGibbon, R Caballero | 11 | 2018 |
| Assessment of precipitating marine stratocumulus clouds in the E3SMv1 atmosphere model: A case study from the ARM MAGIC field campaign X Zheng, SA Klein, VP Ghate, S Santos, J McGibbon, P Caldwell, ... Monthly Weather Review 148 (8), 3341-3359, 2020 | 8 | 2020 |
| sympl (v. 0.4. 0) and climt (v. 0.15. 3)–towards a flexible framework for building model hierarchies in Python, Geosci. Model Dev., 11, 3781–3794, 10.5194 JM Monteiro, J McGibbon, R Caballero gmd-11-3781-2018, 2018 | 8 | 2018 |
| ACE: A fast, skillful learned global atmospheric model for climate prediction O Watt-Meyer, G Dresdner, J McGibbon, SK Clark, B Henn, J Duncan, ... arXiv preprint arXiv:2310.02074, 2023 | 7 | 2023 |
| Machine‐learned climate model corrections from a global storm‐resolving model: Performance across the annual cycle A Kwa, SK Clark, B Henn, ND Brenowitz, J McGibbon, O Watt‐Meyer, ... Journal of Advances in Modeling Earth Systems 15 (5), e2022MS003400, 2023 | 7 | 2023 |
| Improving the predictions of ML-corrected climate models with novelty detection C Sanford, A Kwa, O Watt-Meyer, S Clark, N Brenowitz, J McGibbon, ... arXiv preprint arXiv:2211.13354, 2022 | 4 | 2022 |
| Pace v0. 1: A python-based performance-portable implementation of the FV3 dynamical core J Dahm, E Davis, F Deconinck, O Elbert, R George, J McGibbon, T Wicky, ... EGUsphere 2022, 1-24, 2022 | 4 | 2022 |
| Correcting a coarse-grid climate model in multiple climates by machine learning from global 25-km resolution simulations SK Clark, ND Brenowitz, B Henn, A Kwa, J McGibbon, WA Perkins, ... Authorea Preprints, 2022 | 3 | 2022 |
