Satellite data are an essential source of information for understanding and predicting climate change.
Clouds significantly influence the global energy budget through direct forcing effects and also indirectly through aerosol cloud interactions. Clouds and aerosols are difficult to quantify, having highly variable composition and spatiotemporal distributions, however together they are responsible for some of the largest uncertainties in our understanding of climate change.
Satellite data measurements provide global long-term observations from which geophysical parameters can be derived. The climate data records produced can be used for time series analysis of climate variables, for process studies and the assimilation into or validation of climate models
My current research is primarily on creating advanced algorithms to retrieve the properties of aerosol and clouds and subsequently applying the data sets to climate problems. My focus is primarily on algorithms of cloud and aerosol properties from passive visible and IR satellites.
My research delves into the information on cloud and aerosols behind the beautiful images we often see from Meteorological organisations. From satellites we can get information on aerosol and cloud optical depth (thickness) and particle size, cloud amount and type, height, temperature , cloud liquid and ice water path as well as information on the top of atmosphere and down welling fluxes.
I am a key researcher behind the satellite aerosol and cloud property retrieval community code ORAC/CC4CL (https://github.com/ORAC-CC/orac/wiki) co developed by Monash University, University of Oxford, STFC-RAL Space, DWD and Colorado State University. This algorithm has been applied to the polar orbiting satellite SLSTR (Sea and land surface radiometer) on board Sentinel-3, AVHRR and MODIS and the meteorological geostationary satellites (e.g. SEVIRI and Himawari-AHI).
Previously I worked in the UK, first at the UK Met. Office, then at Rutherford Appleton Laboratory, STFC-RAL Space and the UK National Centre for Earth Observation I still maintain strong links with these groups. In 2017 I was seconded to The UK Department for Environment, Food and rural affairs where I worked initiating a data science community and with the DEFRA Earth Observation centre of Excellence.
Creating algorithms (using optimal estimation and machine learning techniques) for both polar and geostationary satellites to improve the long term climate quality records of aerosol and cloud.
Joint retrievals of surface and atmosphere ( e.g sea surface temperature and aerosol) using satellite data
Satellite retrievals of smoke and the application to air quality forecasts over Indonesia.
Volcanic Ash retrievals
Aerosol and cloud interactions
Using satellite data to understand Weather phenomena such as El Nino, and extreme weather events such as cyclones and hurricanes
Satelllite derived up welling and down welling solar fluxes for climate and solar energy applications.
Physics, PhD, The University of Melbourne
AATSR Science Advisory Group
- remote sensing
- machine learning
- optimal estimation