| 100% clean and renewable wind, water, and sunlight all-sector energy roadmaps for 139 countries of the world MZ Jacobson, MA Delucchi, ZAF Bauer, SC Goodman, WE Chapman, ... Joule 1 (1), 108-121, 2017 | 1149 | 2017 |
| The effect of nitrogen and phosphorus deficiency on flavonol accumulation in plant tissues AJ Stewart, W Chapman, GI Jenkins, I Graham, T Martin, A Crozier Plant, Cell & Environment 24 (11), 1189-1197, 2001 | 393 | 2001 |
| Training machine learning models on climate model output yields skillful interpretable seasonal precipitation forecasts PB Gibson, WE Chapman, A Altinok, L Delle Monache, MJ DeFlorio, ... Communications Earth & Environment 2 (1), 159, 2021 | 96 | 2021 |
| Improving atmospheric river forecasts with machine learning WE Chapman, AC Subramanian, L Delle Monache, SP Xie, FM Ralph Geophysical Research Letters 46 (17-18), 10627-10635, 2019 | 88 | 2019 |
| Towards implementing artificial intelligence post-processing in weather and climate: Proposed actions from the Oxford 2019 workshop SE Haupt, W Chapman, SV Adams, C Kirkwood, JS Hosking, ... Philosophical Transactions of the Royal Society A 379 (2194), 20200091, 2021 | 70 | 2021 |
| 100% clean and renewable wind, water, and sunlight all-sector energy roadmaps for 139 countries of the world. Joule 2017; 1: 108–21 MZ Jacobson, MA Delucchi, ZAF Bauer, SC Goodman, WE Chapman, ... | 68 | |
| ClimateNet: An expert-labelled open dataset and Deep Learning architecture for enabling high-precision analyses of extreme weather Prabhat, K Kashinath, M Mudigonda, S Kim, L Kapp-Schwoerer, ... Geoscientific Model Development Discussions 2020, 1-28, 2020 | 65 | 2020 |
| Probabilistic predictions from deterministic atmospheric river forecasts with deep learning WE Chapman, L Delle Monache, S Alessandrini, AC Subramanian, ... Monthly Weather Review 150 (1), 215-234, 2022 | 47 | 2022 |
| 100% Clean and renewable wind, water, and sunlight (WWS) all-sector energy roadmaps for 139 countries of the world By 2050 MZ Jacobson, MA Delucchi, ZAF Bauer, C Savannah, WE Chapman, ... url: http://web. stanford. edu/group/efmh/jacobson/Articles/I/WWS-50-USState …, 2015 | 29* | 2015 |
| Monthly modulations of ENSO teleconnections: Implications for potential predictability in North America WE Chapman, AC Subramanian, SP Xie, MD Sierks, FM Ralph, Y Kamae Journal of Climate 34 (14), 5899-5921, 2021 | 24 | 2021 |
| Deep learning forecast uncertainty for precipitation over the Western United States W Hu, M Ghazvinian, WE Chapman, A Sengupta, FM Ralph, ... Monthly Weather Review 151 (6), 1367-1385, 2023 | 14 | 2023 |
| Post-processing rainfall in a high-resolution simulation of the 1994 Piedmont flood S Meech, S Alessandrini, W Chapman, L Delle Monache Bulletin of Atmospheric Science and Technology, 1-13, 2021 | 10 | 2021 |
| Direct and indirect effects—An information theoretic perspective G Schamberg, W Chapman, SP Xie, TP Coleman Entropy 22 (8), 854, 2020 | 7 | 2020 |
| Increase in MJO predictability under global warming D Du, AC Subramanian, W Han, WE Chapman, JB Weiss, E Bradley Nature Climate Change 14 (1), 68-74, 2024 | 6 | 2024 |
| Using deep learning for an analysis of atmospheric rivers in a high‐resolution large ensemble climate data set TB Higgins, AC Subramanian, A Graubner, L Kapp‐Schwoerer, ... Journal of Advances in Modeling Earth Systems 15 (4), e2022MS003495, 2023 | 6 | 2023 |
| Improving precipitation forecasts with convolutional neural networks A Badrinath, L Delle Monache, N Hayatbini, W Chapman, F Cannon, ... Weather and Forecasting 38 (2), 291-306, 2023 | 6 | 2023 |
| Training the next generation of researchers in the science and application of atmospheric rivers AM Wilson, W Chapman, A Payne, AM Ramos, C Boehm, D Campos, ... Bulletin of the American Meteorological Society 101 (6), E738-E743, 2020 | 5 | 2020 |
| Supermodeling: improving predictions with an ensemble of interacting models F Schevenhoven, N Keenlyside, F Counillon, A Carrassi, WE Chapman, ... Bulletin of the American Meteorological Society 104 (9), E1670-E1686, 2023 | 4 | 2023 |
| Enhancing Regional Climate Downscaling through Advances in Machine Learning N Rampal, S Hobeichi, PB Gibson, J Baņo-Medina, G Abramowitz, ... Artificial Intelligence for the Earth Systems 3 (2), 230066, 2024 | 3 | 2024 |
| Alphabetical: C MZ Jacobson, MA Delucchi, ZAF Bauer, SC Goodman, WE Chapman, ... Bozonnat, L. Chobadi, JR Erwin, SN Fobi, OK Goldstrom, SH Harrison, TM …, 2015 | 3 | 2015 |
Luca Delle MonacheCW3E/Scripps/UCSDVerified email at ucsd.edu
