Catherine de Burgh-Day

Subseasonal forecasts of the MJO and convectively coupled equatorial waves in ACCESS‐S2 and GraphCast: A multi‐year analysis

Beata Latos, Hanh Nguyen, Matthew C. Wheeler, Chen Li et autres

Abstract The Madden–Julian Oscillation (MJO) and convectively coupled equatorial waves are fundamental drivers of tropical precipitation variability at subseasonal‐to‐seasonal (S2S) time‐scales, yet their accurate prediction remains challenging for S2S forecasting systems. This study evaluates the predictive skill of two contrasting approaches—the dynamical …

pl, au, sg (code pays fourni par la source)

PyEarthTools: Machine learning for Earth system science

Tennessee Leeuwenburg, Harrison Cook, Maxime Rio, Sanaa Hobeichi et autres

PyEarthTools: is a Python framework that supports the develoment of machine learning models, big and small, for Earth system science is suitable for students and newcomers, as well as for domain specialists and scientists runs effectively on HPC (supercomputers), cloud, workstations and …

au, nz, gb (code pays fourni par la source)

PyEarthTools: Machine learning for Earth system science

Tennessee Leeuwenburg, Harrison Cook, Maxime Rio, Sanaa Hobeichi et autres

PyEarthTools: is a Python framework that supports the develoment of machine learning models, big and small, for Earth system science is suitable for students and newcomers, as well as for domain specialists and scientists runs effectively on HPC (supercomputers), cloud, workstations and …

au, nz, gb (code pays fourni par la source)

Applying a Standardized Benchmarking Framework to Evaluate AI Methods for Precipitation Downscaling over Australia

Sanaa Hobeichi, Declan Curran, Matthias Bittner, Rachael N. Isphording et autres

Abstract Downscaling techniques are essential for refining coarse-resolution climate projections to scales relevant for local and regional impact assessments, with artificial intelligence (AI) emerging as a promising approach for this task. However, a standardized benchmarking framework for evaluating these AI-based downscaling methods …

au, at (code pays fourni par la source)

Climate impacts of the El Niño–Southern Oscillation on Australia

Andréa S. Taschetto, Shayne McGregor, Dietmar Dommenget, Zoe E. Gillett et autres

au, gr, cn, us (code pays fourni par la source)

Statistical Postprocessing Yields Accurate Probabilistic Forecasts from Artificial Intelligence Weather Models

Belinda Trotta, Robert D. Johnson, Catherine de Burgh-Day, Debra Hudson et autres

Abstract Artificial intelligence (AI) weather models are now reaching operational-grade performance for some variables, but like traditional numerical weather prediction (NWP) models, they exhibit systematic biases and reliability issues. We test the application of the Bureau of Meteorology’s existing statistical postprocessing system, …

au (code pays fourni par la source)

Statistical post-processing yields accurate probabilistic forecasts from Artificial Intelligence weather models

Belinda Trotta, Robert D. Johnson, Catherine de Burgh-Day, Debra Hudson et autres

Artificial Intelligence (AI) weather models are now reaching operational-grade performance for some variables, but like traditional Numerical Weather Prediction (NWP) models, they exhibit systematic biases and reliability issues. We test the application of the Bureau of Meteorology's existing statistical post-processing system, IMPROVER, …

Observing and forecasting the retreat of northern Australia’s rainy season

Tim Cowan, Emily Hinds, Andrew G. Marshall, Matthew C. Wheeler et autres

According to the Australian Bureau of Meteorology, the northern Australian wet season extends through to April, which also formally marks the end of Australia’s tropical cyclone season. Mid-autumn is when the tropical dry season transition period begins, when crop farmers prepare land …

au, us (code pays fourni par la source)

Machine learning for numerical weather and climate modelling: a review

Catherine de Burgh-Day, Tennessee Leeuwenburg

Abstract. Machine learning (ML) is increasing in popularity in the field of weather and climate modelling. Applications range from improved solvers and preconditioners, to parameterization scheme emulation and replacement, and more recently even to full ML-based weather and climate prediction models. While …

au (code pays fourni par la source)

Reply on RC2

Catherine de Burgh-Day

Abstract. Machine learning (ML) is increasing in popularity in the field of weather and climate modelling. Applications range from improved solvers and preconditioners, to parametrisation scheme emulation and replacement, and recently even to full ML-based weather and climate prediction models. While ML …

au (code pays fourni par la source)

Comment on egusphere-2023-350

Catherine de Burgh-Day, Tennessee Leeuwenburg

Abstract. Machine learning (ML) is increasing in popularity in the field of weather and climate modelling. Applications range from improved solvers and preconditioners, to parametrisation scheme emulation and replacement, and recently even to full ML-based weather and climate prediction models. While ML …

au (code pays fourni par la source)

a quick addition

Catherine de Burgh-Day, Tennessee Leeuwenburg

Abstract. Machine learning (ML) is increasing in popularity in the field of weather and climate modelling. Applications range from improved solvers and preconditioners, to parametrisation scheme emulation and replacement, and recently even to full ML-based weather and climate prediction models. While ML …

au (code pays fourni par la source)