| Assessing the potential of deep learning for emulating cloud superparameterization in climate models with real‐geography boundary conditions G Mooers, M Pritchard, T Beucler, J Ott, G Yacalis, P Baldi, P Gentine Journal of Advances in Modeling Earth Systems 13 (5), e2020MS002385, 2021 | 38 | 2021 |
| Neural general circulation models D Kochkov, J Yuval, I Langmore, P Norgaard, J Smith, G Mooers, J Lottes, ... arXiv preprint arXiv:2311.07222, 2023 | 15 | 2023 |
| Generative modeling of atmospheric convection G Mooers, J Tuyls, S Mandt, M Pritchard, TG Beucler Proceedings of the 10th international conference on climate informatics, 98-105, 2020 | 10 | 2020 |
| ClimSim: An open large-scale dataset for training high-resolution physics emulators in hybrid multi-scale climate simulators S Yu, WM Hannah, L Peng, MA Bhouri, R Gupta, J Lin, B Lütjens, JC Will, ... arXiv preprint arXiv:2306.08754, 2023 | 5 | 2023 |
| Temporal changes in the areal coverage of daily extreme precipitation in the Northeastern United States using high-resolution gridded data AT DeGaetano, G Mooers, T Favata Journal of Applied Meteorology and Climatology 59 (3), 551-565, 2020 | 5 | 2020 |
| Comparing storm resolving models and climates via unsupervised machine learning G Mooers, M Pritchard, T Beucler, P Srivastava, H Mangipudi, L Peng, ... Scientific Reports 13 (1), 22365, 2023 | 4 | 2023 |
| ClimSim: A large multi-scale dataset for hybrid physics-ML climate emulation S Yu, W Hannah, L Peng, J Lin, MA Bhouri, R Gupta, B Lütjens, JC Will, ... Advances in Neural Information Processing Systems 36, 2024 | 3 | 2024 |
| Analyzing High-Resolution Clouds and Convection using Multi-Channel VAEs H Mangipudi, G Mooers, M Pritchard, T Beucler, S Mandt arXiv preprint arXiv:2112.01221, 2021 | 1 | 2021 |
| Generative large eddy simulations with conditional variational autoencoders SK Mukkavilli, MS Pritchard, KG Pressel, G Mooers, PL Ma, S Mandt AGU Fall Meeting Abstracts 2020, A043-0009, 2020 | 1 | 2020 |
| Author Correction: Comparing storm resolving models and climates via unsupervised machine learning G Mooers, M Pritchard, T Beucler, P Srivastava, H Mangipudi, L Peng, ... Scientific Reports 14, 2024 | | 2024 |
| Understanding precipitation changes through unsupervised machine learning G Mooers, T Beucler, M Pritchard, S Mandt Environmental Data Science 3, e3, 2024 | | 2024 |
| Neural General Circulation Models for Weather and Climate S Hoyer, J Yuval, D Kochkov, I Langmore, P Norgaard, G Mooers, ... AGU23, 2023 | | 2023 |
| Improving the Modeling and Analysis of Tropical Convection and Precipitation Through Machine Learning Methods G Mooers University of California, Irvine, 2023 | | 2023 |
| Understanding Extreme Precipitation Changes through Unsupervised Machine Learning G Mooers, T Beucler, M Pritchard, S Mandt arXiv preprint arXiv:2211.01613, 2022 | | 2022 |
| The Water Cycle in a Warmer World through Process Understanding and Climate and Hydrological Modeling. Part III A Cherchi, PA Arias, A Jenney, EM Dougherty 102nd American Meteorological Society Annual Meeting, 2022 | | 2022 |
| Withdrawn: Using Secondary Neural Networks to Bias Correct Coupled Neural Network Convective Parameterizations J Lin, N Crawford, PL Ma, G Mooers, D Walling, M Pritchard 102nd American Meteorological Society Annual Meeting, 2022 | | 2022 |
| Unsupervised Organization of Turbulent Updraft Regimes and their Global Response to Warming G Mooers, M Pritchard, P Gentine, S Mandt, T Beucler AGU Fall Meeting Abstracts 2021, NG51A-08, 2021 | | 2021 |
| Toward Generative Superparameterized Updrafts with Variational Autoencoder Interpretability G Mooers, S Mandt, M Pritchard, T Beucler, J Tuyls, K Mukkavilli 101st American Meteorological Society Annual Meeting, 2021 | | 2021 |
| Assessing the potential of deep neural networks for emulating cloud superparameterization in climate models under real geography boundary conditions G Mooers, MS Pritchard, T Beucler, J Ott, G Yacalis, P Baldi, P Gentine AGU Fall Meeting Abstracts 2020, A068-0004, 2020 | | 2020 |
| Towards robust operational neural network parameterizations of convection in climate models—advances in stability, credibility and software MS Pritchard, T Beucler, G Mooers, J Ott, P Gentine, L Peng, P Baldi, ... AGU Fall Meeting Abstracts 2020, A071-02, 2020 | | 2020 |
