| Spectroscopic observations of sn 2012fr: A luminous, normal type ia supernova with early high-velocity features and a late velocity plateau MJ Childress, RA Scalzo, SA Sim, BE Tucker, F Yuan, BP Schmidt, ... The Astrophysical Journal 770 (1), 29, 2013 | 98 | 2013 |
| Machine learning for numerical weather and climate modelling: a review CO de Burgh-Day, T Leeuwenburg Geoscientific Model Development 16 (22), 6433-6477, 2023 | 52 | 2023 |
| ACCESS-S2: the upgraded Bureau of Meteorology multi-week to seasonal prediction system R Wedd, O Alves, C de Burgh-Day, C Down, M Griffiths, HH Hendon, ... Journal of Southern Hemisphere Earth Systems Science 72 (3), 218-242, 2022 | 48 | 2022 |
| Forecasting the extreme rainfall, low temperatures, and strong winds associated with the northern Queensland floods of February 2019 T Cowan, MC Wheeler, O Alves, S Narsey, C de Burgh-Day, M Griffiths, ... Weather and Climate Extremes 26, 100232, 2019 | 48 | 2019 |
| Why Australia was not wet during spring 2020 despite La Niña EP Lim, D Hudson, MC Wheeler, AG Marshall, A King, H Zhu, HH Hendon, ... Scientific reports 11 (1), 18423, 2021 | 32 | 2021 |
| Direct shear mapping–a new weak lensing tool CO de Burgh-Day, EN Taylor, RL Webster, AM Hopkins Monthly Notices of the Royal Astronomical Society 451 (2), 2161-2173, 2015 | 23 | 2015 |
| Forecasting extreme marine heat events in key aquaculture regions around New Zealand CO de Burgh-Day, CM Spillman, G Smith, CL Stevens Journal of Southern Hemisphere Earth Systems Science 72 (1), 58-72, 2022 | 22 | 2022 |
| Subseasonal drivers of extreme fire weather in Australia and its prediction in ACCESS-S1 during spring and summer AG Marshall, PA Gregory, CO de Burgh-Day, M Griffiths Climate Dynamics 58 (1), 523-553, 2022 | 18 | 2022 |
| Machine learning for numerical weather and climate modelling: a review, Geosci. Model Dev., 16, 6433–6477 CO de Burgh-Day, T Leeuwenburg | 10 | 2023 |
| Tropical forcing of Australian extreme low minimum temperatures in September 2019 EP Lim, HH Hendon, L Shi, C de Burgh-Day, D Hudson, A King, B Trewin, ... Climate Dynamics 56 (11), 3625-3641, 2021 | 10 | 2021 |
| Multi-week prediction of livestock chill conditions associated with the northwest Queensland floods of February 2019 T Cowan, MC Wheeler, C de Burgh-Day, H Nguyen, D Cobon Scientific Reports 12 (1), 5907, 2022 | 9 | 2022 |
| Predicting seasonal ocean variability around New Zealand using a coupled ocean-atmosphere model CO de Burgh-Day, CM Spillman, C Stevens, O Alves, G Rickard New Zealand Journal of Marine and Freshwater Research 53 (2), 201-221, 2019 | 9 | 2019 |
| An adaptable framework for development and real time production of experimental sub-seasonal to seasonal forecast products C De Burgh-Day, O Alves, M Griffiths, D Hudson, H Yan, G Young Australian Bureau of Meteorology, 2020 | 8 | 2020 |
| Machine learning for numerical weather and climate modelling: a review, Geoscientific Model Development, 16, 6433–6477 CO de Burgh-Day, T Leeuwenburg | 6 | 2023 |
| Forecasting northern Australian summer rainfall bursts using a seasonal prediction system T Cowan, MC Wheeler, S Sharmila, S Narsey, C de Burgh-Day Weather and Forecasting 37 (1), 23-44, 2022 | 6 | 2022 |
| Direct Shear Mapping: Prospects for Weak Lensing Studies of Individual Galaxy–Galaxy Lensing Systems CO de Burgh-Day, EN Taylor, RL Webster, AM Hopkins Publications of the Astronomical Society of Australia 32, e040, 2015 | 6 | 2015 |
| The Northern Australia Climate Program: Overview and Selected Highlights SL Lavender, T Cowan, M Hawcroft, MC Wheeler, C Jarvis, D Cobon, ... Bulletin of the American Meteorological Society 103 (11), E2492-E2505, 2022 | 1 | 2022 |
| Skill of ACCESS-S2 in predicting rainfall bursts over Australia T Cowan, MC Wheeler, D Hudson, C de Burgh-Day, M Griffiths, G Young Bureau of Meteorology, 2022 | 1 | 2022 |
| A hybrid parametrisation for precipitation probability of exceedance data C de Burgh-Day, F Dillon Bureau of Meteorology Research Report, 2021 | 1 | 2021 |
| Artificial intelligence and machine learning: revolutionizing weather forecasting F Pappenberger, N Wedi, M Chantry, C Lessig, S Lang, P Deuben, ... World Meteorological Organization, 2024 | | 2024 |