ORCID Profile
0000-0002-1975-0042
Current Organisation
Australian Bureau of Meteorology
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Publisher: Elsevier BV
Date: 12-2019
Publisher: Cambridge University Press (CUP)
Date: 2015
DOI: 10.1017/PASA.2015.39
Abstract: Using both a theoretical and an empirical approach, we have investigated the frequency of low redshift galaxy-galaxy lensing systems in which the signature of 3D weak lensing might be directly detectable. We find good agreement between these two approaches. Using data from the Galaxy and Mass Assembly redshift survey we estimate the frequency of detectable weak lensing at low redshift. We find that below a redshift of z ~ 0.6, the probability of a galaxy being weakly lensed by γ ⩾ 0.02 is ~ 0.01. We have also investigated the feasibility of measuring the scatter in the M * − M h relation using shear statistics. We estimate that for a shear measurement error of Δγ = 0.02 (consistent with the sensitivity of the Direct Shear Mapping technique), with a s le of ~$50,000 spatially and spectrally resolved galaxies, the scatter in the M * − M h relation could be measured. While there are currently no existing IFU surveys of this size, there are upcoming surveys that will provide this data (e.g The Hobby-Eberly Telescope Dark Energy Experiment (HETDEX), surveys with Hector, and the Square Kilometre Array (SKA)).
Publisher: Informa UK Limited
Date: 31-10-2018
Publisher: Copernicus GmbH
Date: 17-04-2023
DOI: 10.5194/EGUSPHERE-2023-350
Abstract: 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 has been used in this space for more than 25 years, it is only in the last 10 or so years that progress has accelerated to the point that ML applications are becoming competitive with numerical knowledge-based alternatives. In this review, we provide a roughly chronological summary of the application of ML to aspects of weather and climate modelling from early publications through to the latest progress at the time of writing. We also provide an overview of key ML concepts and terms. Our aim is to provide a primer for researchers and model developers to rapidly familiarize and update themselves with the world of ML in the context of weather and climate models.
Publisher: Springer Science and Business Media LLC
Date: 20-02-2021
Publisher: Oxford University Press (OUP)
Date: 11-06-2015
Publisher: CSIRO Publishing
Date: 09-12-2022
DOI: 10.1071/ES22026
Abstract: ACCESS-S2 is a major upgrade to the Australian Bureau of Meteorology’s multi-week to seasonal prediction system. It was made operational in October 2021, replacing ACCESS-S1. The focus of the upgrade is the addition of a new weakly coupled data assimilation system to provide initial conditions for atmosphere, ocean, land and ice fields. The model is based on the UK Met Office GloSea5-GC2 seasonal prediction system and is unchanged from ACCESS-S1, aside from minor corrections and enhancements. The performance of the assimilation system and the skill of the seasonal and multi-week forecasts have been assessed and compared to ACCESS-S1. There are improvements in the ACCESS-S2 initial conditions compared to ACCESS-S1, particularly for soil moisture and aspects of the ocean, notably the ocean currents. More realistic soil moisture initialisation has led to increased skill for forecasts over Australia, especially those of maximum temperature. The ACCESS-S2 system is shown to have increased skill of El Nino–Southern Oscillation forecasts over ACCESS-S1 during the challenging autumn forecast period. Analysis suggests that ACCESS-S2 will deliver improved operational forecast accuracy in comparison to ACCESS-S1. Assessments of the operational forecasts are underway. ACCESS-S2 represents another step forward in the development of seasonal forecast systems at the Bureau of Meteorology. However, key rainfall and sea surface temperature biases in ACCESS-S1 remain in ACCESS-S2, indicating where future efforts should be focused.
Publisher: CSIRO Publishing
Date: 09-03-2022
DOI: 10.1071/ES21012
Abstract: The Tasman Sea has been identified as a climate hotspot and has experienced several marine heatwaves (MHWs) in recent years. These events have impacted coastal regions of New Zealand (NZ), which has had a follow-on effect on local marine and aquaculture industries. Advance warning of extreme marine heat events would enable these industries to mitigate potential losses. Here we present an assessment of the forecast skill of the Australian Bureau of Meteorology’s seasonal prediction system, Australian Community Climate and Earth-System Simulator-Seasonal v1.0 (ACCESS-S1), for three key aquaculture regions around NZ: Hauraki Gulf, Western Cook Strait and Foveaux Strait. We investigate the skill of monthly sea surface temperature anomaly (SSTA) forecasts, and forecasts for SSTA exceeding the 90th percentile, which is an accepted MHW threshold. We find that the model has skill for predicting extreme heat events in all three regions at 0–2 month lead times. We then demonstrate that ACCESS-S1 was able to capture observed monthly SSTA exceeding the 90th percentile around coastal NZ during the 2019 Tasman Sea MHW at a lead time of 1 month. Finally, we discuss the relationship between SSTA in the Tasman Sea and SSTA in coastal regions of NZ, and thus the Tasman Sea as a source of model SSTA skill in the three key coastal regions. Results from this study show that skilful forecasts of ocean heat extremes in regional areas have the potential to enable marine operators in the aquaclture industry to mitigate losses due to MHWs, especially in a warming climate.
No related grants have been discovered for Catherine de Burgh-Day.