Closing the Solar Cycle. This project aims to decisively settle the debate about the mechanism driving magnetic activity on the surface of the Sun. By drawing on extensive, big-data analysis of solar observations the project intends to use the technique of helioseismology to reveal differences in the statistical evolution of magnetic regions. Expected outcomes of this project will powerfully refine our models of the interaction between convective flows and magnetic fields in the Sun, resulting i ....Closing the Solar Cycle. This project aims to decisively settle the debate about the mechanism driving magnetic activity on the surface of the Sun. By drawing on extensive, big-data analysis of solar observations the project intends to use the technique of helioseismology to reveal differences in the statistical evolution of magnetic regions. Expected outcomes of this project will powerfully refine our models of the interaction between convective flows and magnetic fields in the Sun, resulting in a leap forward in solar dynamo theory, one of the fundamental problems in astrophysics. The anticipated benefits include moving from nowcasting to forecasting space weather, mitigating the billion dollar economic effects of geomagnetic storms.Read moreRead less
Ensemble modelling of space-weather drivers. This project aims to develop methods for forecasting the evolution of magnetic fields on the Sun's surface, and to use the results to drive an ensemble of numerical simulations of the evolution of the magnetic field in the overlying atmosphere. The project expects to create a new framework for forecasting the evolution of solar active regions, applying, for the first time, methods established in Numerical Weather Prediction. The expected outcomes are ....Ensemble modelling of space-weather drivers. This project aims to develop methods for forecasting the evolution of magnetic fields on the Sun's surface, and to use the results to drive an ensemble of numerical simulations of the evolution of the magnetic field in the overlying atmosphere. The project expects to create a new framework for forecasting the evolution of solar active regions, applying, for the first time, methods established in Numerical Weather Prediction. The expected outcomes are physics-based prediction of solar atmospheric magnetic field evolution, including explosive eruptions. The results should have significant benefit in improving prediction of extreme space weather events, which pose an increasing threat to our technologically-dependent society.Read moreRead less
Cloud-climate interaction over the Great Barrier Reef and Southwest Pacific. This project aims to investigate cloud-climate interactions of the Southwest Pacific trade wind region from the regional scale to local forcing over the Great Barrier Reef. The project expects to generate new knowledge in the nature and variability of the trade wind clouds, including their impact on the surface radiative budget, ocean temperatures and coral bleaching events. Potential changes of these clouds due to glob ....Cloud-climate interaction over the Great Barrier Reef and Southwest Pacific. This project aims to investigate cloud-climate interactions of the Southwest Pacific trade wind region from the regional scale to local forcing over the Great Barrier Reef. The project expects to generate new knowledge in the nature and variability of the trade wind clouds, including their impact on the surface radiative budget, ocean temperatures and coral bleaching events. Potential changes of these clouds due to global warming and ensuing impacts on the environment will be studied. Expected outcomes include better modelling of the Great Barrier Reef environment and improved estimates of low-cloud feedback. This should provide significant benefits in developing warning systems for bleaching events, and regional land and water management. Read moreRead less
A dynamical systems theory approach to machine learning. Forecasting the future state of a high-dimensional complex multi-scale system is a challenge we face in areas ranging from climate science to epidemiology. Even when basic physical mechanisms have been identified, the actual evolution equations are often unknown. This project will develop a computationally cheap machine learning framework for forecasting. The proposed mathematical framework provides a forecast together with a quantificati ....A dynamical systems theory approach to machine learning. Forecasting the future state of a high-dimensional complex multi-scale system is a challenge we face in areas ranging from climate science to epidemiology. Even when basic physical mechanisms have been identified, the actual evolution equations are often unknown. This project will develop a computationally cheap machine learning framework for forecasting. The proposed mathematical framework provides a forecast together with a quantification of its uncertainty. We will develop sophisticated mathematical theory underpinning the novel methodology, as well as applying it to the perennial problem of subgrid-scale parametrisation of tropical convection, a missing key element in current climate models.Read moreRead less
P-band soil moisture sensing from space. This project aims to develop radiative transfer models to demonstrate that a P-band radiometer capability can remotely sense the top ~15cm layer of soil moisture, through a series of tower and airborne field experiments. Timely soil moisture information on this near-surface layer is critical to improved water management for food production in the face of extreme climate variability. Current satellite technologies are limited to the top ~5cm layer of soil ....P-band soil moisture sensing from space. This project aims to develop radiative transfer models to demonstrate that a P-band radiometer capability can remotely sense the top ~15cm layer of soil moisture, through a series of tower and airborne field experiments. Timely soil moisture information on this near-surface layer is critical to improved water management for food production in the face of extreme climate variability. Current satellite technologies are limited to the top ~5cm layer of soil using an L-band radiometer. This project is expected to give farmers the soil moisture data they need to optimise their available water resources to maximise food productionRead moreRead less
Digitally-Integrated Smart Sensing of Diverse Airborne Grass Pollen Sources. Grass pollen is the main outdoor allergen source globally, triggering hayfever and asthma in up to 500 million people. With over 10,000 species, the influence of grass type, location and climate on pollen in the air is not yet known. This is a key issue since subtropical and temperate grasses differ in response to environmental factors. The project aims to use artificial intelligence on digital camera images to learn to ....Digitally-Integrated Smart Sensing of Diverse Airborne Grass Pollen Sources. Grass pollen is the main outdoor allergen source globally, triggering hayfever and asthma in up to 500 million people. With over 10,000 species, the influence of grass type, location and climate on pollen in the air is not yet known. This is a key issue since subtropical and temperate grasses differ in response to environmental factors. The project aims to use artificial intelligence on digital camera images to learn to see local grass flowers and integrate this with air sensors trained to detect grass pollen types. The expected outcomes are new capacities to track airborne grass pollen types. These outcomes can transform how pollen can be monitored to reduce the burden of allergies, and provide evidence of changing airborne pollen loads.
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