Dr Caroline Poulsen Honours Projects

Measuring Australian Methane emissions from Space

Supervisor: Caroline Poulsen and Paul Krummel/Zoe Loh CSIRO
Field of Study: Climate: Satellite Remote Sensing of the Atmosphere
Support Offered: Potential short-term paid Industrial Traineeship at CSIRO may be offered.
Collaborating Organisation: CSIRO Aspendale
Preferred Program: Honours or MSc

Methane is an important green house gas. In Australia fugitive emissions from the gas industry arise from leaks from equipment, from deliberate or accidental venting or from flaring of gas at every stage of the gas supply system. The estimation of methane emissions from the gas industry is generally not perfomed through direct emission measurements. Instead, emission estimates are prepared using sets of model parameters or emission factors derived from sample datasets recorded in published studies. The recently launched Sentinel-5P satellite makes routine (daily) measurements of Methane concentrations at up to 4km resolution over Australia which offers unprecedent global coverage to explore natural and anthropogenic emissions.

This project will look at identifying locations of methane emissions in Australia by producing seasonal data sets of methane concentrations detected by Sentinel-5P and comparing, validating and verifying them with measurements and/or modelled Methane emissions made by CSIRO where available. The initial focus of the validation will be detection and validation of Methane in the Surat Basin.

This project involves computational analysis of large data sets, so coding skills, including familiarity with a languages such as Python, R, or MATLAB are highly desirable. Alternatively you should have a strong enthusiasm to learn.

For further details contact Caroline Poulsen

Using Machine Learning to detect supercooled clouds.

Supervisor: Caroline Poulsen
Field of Study: Climate : Satellite Remote Sensing of the Atmosphere
Support Offered: High performance computing, data skills training
Preferred Program: Honours or MSc

The Southern Ocean is a critical region for global climate, yet large cloud related solar radiation biases over the Southern Ocean are a long-standing problem in climate models and are poorly understood, leading to biases in simulated sea surface temperatures. Supercooled liquid clouds are central to understanding and simulating the Southern Ocean environment. Accurate observations and time series of supercooled clouds are necessary to understand the climate impact of this critical cloud type  and how it is varying with climate change. The proposed project involves using machine learning to identify super cooled clouds in geostationary Himawari satellite data and validating the results against lidar and radar observations.

This project involves computational analysis of large data sets, so coding skills, including familiarity with a languages such as Python, R, or MATLAB are highly desirable. Alternatively you should have a strong enthusiasm to learn.

For further details contact Caroline Poulsen

Investigating differences in satellite cloud climatologies

Supervisor: Caroline Poulsen
Field of Study: Climate: Satellite Remote Sensing of the Atmosphere
Preferred Program: Honours or MSc

Clouds play a vital role in our climate by regulating the amount of solar energy that reaches the surface and the amount of the Earth's energy that is radiated back into space. The more energy that is trapped by the planet, the warmer our climate will grow. If less energy is collected, the climate will become cooler. How clouds will respond to a changing climate is highly uncertain. Satellite cloud climatology’s are need to provide observations of how clouds are varying in time. A number of satellite cloud climatology’s from different instruments and using different algorithms have been generated over the last decade, i.e. ISCCP, Patmos-X, Cloud CCI and MODIS.  While the general characteristics of clouds and associated trends are similar in the data sets, towards the polar regions the products diverge significantly, particularly the microphysical properties such as effective radius and optical depth. This is a critical region for climate. In this project you will delve into the different satellite products to identify the cause of the differences.

This project involves computational analysis of large data sets, so coding skills, including familiarity with a languages such as Python, R, or MATLAB are highly desirable. Alternatively you should have a strong enthusiasm to learn.

For further details contact Caroline Poulsen

Using satellite data to understand Australia’s solar radiation potential

Supervisor: Caroline Poulsen
Field of Study: Solar Radiation: Satellite Remote Sensing
Preferred Program: Honours or MSc

Information about solar radiation/energy is useful to understand the best locations for investing in solar technology. The properties and coverage of aerosols and clouds are the key variables which determine the amount of solar radiation available for energy generation.  Satellite climatologies of radiative fluxes can be used to understand the variation of incoming solar radiation across the country and how it varies with time. In this project you will look at trends in solar radiation for the Australian continents from the ESA  (European Space Agency) CCI cloud and flux climatology and validate the satellite measurements with ground based observations to evaluate the accuracy of the products.

This project involves computational analysis of large data sets, so coding skills, including familiarity with a languages such as Python, R, or MATLAB are highly desirable. Alternatively you should have a strong enthusiasm to learn.

For further details contact Caroline Poulsen