| Skilful precipitation nowcasting using deep generative models of radar S Ravuri, K Lenc, M Willson, D Kangin, R Lam, P Mirowski, M Fitzsimons, ... Nature 597 (7878), 672-677, 2021 | 679 | 2021 |
| A review of radar-based nowcasting of precipitation and applicable machine learning techniques R Prudden, S Adams, D Kangin, N Robinson, S Ravuri, S Mohamed, ... arXiv preprint arXiv:2005.04988, 2020 | 55 | 2020 |
| Quantifying causal pathways of teleconnections M Kretschmer, SV Adams, A Arribas, R Prudden, N Robinson, E Saggioro, ... Bulletin of the American Meteorological Society, 1-34, 2021 | 50 | 2021 |
| A review of radar-based nowcasting of precipitation and applicable machine learning techniques, arXiv R Prudden, S Adams, D Kangin, N Robinson, S Ravuri, S Mohamed, ... arXiv preprint arXiv:2005.04988 11, 2020 | 12 | 2020 |
| A review of radar-based nowcasting of precipitation and applicable machine learning techniques. arXiv 2020 R Prudden, S Adams, D Kangin, N Robinson, S Ravuri, S Mohamed, ... arXiv preprint arXiv:2005.04988, 0 | 11 | |
| The role of digital technologies in responding to the grand challenges of the natural environment: the Windermere Accord GS Blair, R Bassett, L Bastin, L Beevers, MI Borrajo, M Brown, SL Dance, ... Patterns 2 (1), 2021 | 8 | 2021 |
| Quantifying causal pathways of teleconnections, B. Am. Meteorol. Soc., 102, E2247–E2263 M Kretschmer, SV Adams, A Arribas, R Prudden, N Robinson, E Saggioro, ... | 5 | 2021 |
| Machine Learning for Nonorographic Gravity Waves in a Climate Model SC Hardiman, AA Scaife, A van Niekerk, R Prudden, A Owen, SV Adams, ... Artificial intelligence for the Earth systems 2 (4), e220081, 2023 | 4 | 2023 |
| Improved infilling of missing metadata from expendable bathythermographs (XBTs) Using multiple machine learning methods S Haddad, RE Killick, MD Palmer, MJ Webb, R Prudden, F Capponi, ... Journal of Atmospheric and Oceanic Technology 39 (9), 1367-1385, 2022 | 2 | 2022 |
| How We Used Cloud Services to Develop a 4D Browser Visualization of Environmental Data at the Met Office Informatics Lab N Robinson, R Hogben, R Prudden, T Powell, J Tomlinson, R Middleham, ... Cloud Computing in Ocean and Atmospheric Sciences, 89-106, 2016 | 2 | 2016 |
| Stochastic downscaling to chaotic weather regimes using spatially conditioned gaussian random fields with adaptive covariance R Prudden, N Robinson, P Challenor, R Everson Weather and Forecasting 36 (6), 2233-2258, 2021 | 1 | 2021 |
| A New Approach to Streaming Data from the Cloud NH Robinson, R Prudden, A Arribas Bulletin of the American Meteorological Society 98 (11), 2280-2283, 2017 | 1 | 2017 |
| A practical approach to spatiotemporal data compression NH Robinson, R Prudden, A Arribas arXiv preprint arXiv:1604.03688, 2016 | 1 | 2016 |
| Machine learning for gravity wave forcing in the Met Office climate model S Hardiman, AA Scaife, R Prudden, A van Niekerk, A Owen, S Adams, ... Fall Meeting 2022, 2022 | | 2022 |
| Quantifying Teleconnection pathways leading to Low Rainfall anomalies during Boreal Summer in Indonesian Borneo T Lam, M Kretschmer, S Adams, A Arribas, R Prudden, E Saggioro, ... EGU General Assembly Conference Abstracts, EGU21-13694, 2021 | | 2021 |
| Connecting Machine Learning with Atmospheric Science (Invited Presentation) R Prudden 99th American Meteorological Society Annual Meeting, 2019 | | 2019 |
| Spatially non-stationary downscaling of cloud coverage. R Prudden Geophysical Research Abstracts 21, 2019 | | 2019 |
| Jade: using on-demand cloud analysis to give scientists back their flow N Robinson, R Prudden EGU General Assembly Conference Abstracts, 9208, 2018 | | 2018 |
| Downscaling precipitation data over the UK R Prudden EGU General Assembly Conference Abstracts, 9189, 2018 | | 2018 |
| Opportunities for Machine Learning in Weather Forecasting R Prudden, N Robinson, A Arribas, C Ewen 98th American Meteorological Society Annual Meeting, 2018 | | 2018 |
