| Convolutional conditional neural processes for local climate downscaling A Vaughan, W Tebbutt, JS Hosking, RE Turner Geoscientific Model Development 15 (1), 251-268, 2022 | 35* | 2022 |
| Autoregressive conditional neural processes WP Bruinsma, S Markou, J Requiema, AYK Foong, TR Andersson, ... arXiv preprint arXiv:2303.14468, 2023 | 24 | 2023 |
| Practical conditional neural processes via tractable dependent predictions S Markou, J Requeima, WP Bruinsma, A Vaughan, RE Turner arXiv preprint arXiv:2203.08775, 2022 | 24 | 2022 |
| Unsupervised change detection of extreme events using ML On-board V Růžička, A Vaughan, D De Martini, J Fulton, V Salvatelli, C Bridges, ... arXiv preprint arXiv:2111.02995, 2021 | 23 | 2021 |
| RaVÆn: unsupervised change detection of extreme events using ML on-board satellites V Růžička, A Vaughan, D De Martini, J Fulton, V Salvatelli, C Bridges, ... Scientific reports 12 (1), 16939, 2022 | 20 | 2022 |
| Real world and tropical cyclone world. Part II: Sensitivity of tropical cyclone formation to uniform and meridionally varying sea surface temperatures under aquaplanet conditions KJE Walsh, S Sharmila, M Thatcher, S Wales, S Utembe, A Vaughan Journal of Climate 33 (4), 1473-1486, 2020 | 19 | 2020 |
| Active learning with convolutional gaussian neural processes for environmental sensor placement TR Andersson, WP Bruinsma, S Markou, DC Jones, JS Hosking, ... arXiv e-prints, arXiv: 2211.10381, 2022 | 12* | 2022 |
| The stationary banding complex and secondary eyewall formation in tropical cyclones A Vaughan, KJE Walsh, JD Kepert Journal of Geophysical Research: Atmospheres 125 (6), e2019JD031515, 2020 | 10 | 2020 |
| Aurora: A foundation model of the atmosphere C Bodnar, WP Bruinsma, A Lucic, M Stanley, J Brandstetter, P Garvan, ... arXiv preprint arXiv:2405.13063, 2024 | 9 | 2024 |
| Semantic segmentation of methane plumes with hyperspectral machine learning models V Růžička, G Mateo-Garcia, L Gómez-Chova, A Vaughan, L Guanter, ... Scientific Reports 13 (1), 19999, 2023 | 8* | 2023 |
| Multivariate climate downscaling with latent neural processes A Vaughan, M Herzog | 4 | 2024 |
| Sim2real for environmental neural processes J Scholz, TR Andersson, A Vaughan, J Requeima, RE Turner arXiv preprint arXiv:2310.19932, 2023 | 3 | 2023 |
| CH4Net: a deep learning model for monitoring methane super-emitters with Sentinel-2 imagery A Vaughan, G Mateo-García, L Gómez-Chova, V Růžička, L Guanter, ... Atmospheric Measurement Techniques 17 (9), 2583-2593, 2024 | 2 | 2024 |
| Aardvark Weather: end-to-end data-driven weather forecasting A Vaughan, S Markou, W Tebbutt, J Requeima, WP Bruinsma, ... arXiv preprint arXiv:2404.00411, 2024 | 2 | 2024 |
| AI for operational methane emitter monitoring from space A Vaughan, G Mateo-Garcia, I Irakulis-Loitxate, M Watine, ... arXiv preprint arXiv:2408.04745, 2024 | | 2024 |
| UNEP's Methane Alert and Response System (MARS): current status, new developments and case studies I Irakulis-Loitxate, C Randles, M Watine-Guiu, G Mateo-García, ... EGU24, 2024 | | 2024 |
Richard E TurnerProfessor, University of CambridgeVerified email at cam.ac.uk
