Reducing emission-driven ozone uncertainties in climate and air-quality models using machine learning

Add to your list(s) Download to your calendar using vCal

• Tomás Sherwen
• Tuesday 16 June 2020, 11:00-12:30
• https://ukri.zoom.us/j/92946956029.

If you have a question about this talk, please contact Jonathan Rosser.

“We use global, regional, and local models to evaluate the chemical composition of the air we breathe and explore questions motivated by air quality and climate change. These models require 2D input values for variables such as concentrations of chemicals at the sea surface. However, it is neither desirable nor possible to observe all species in all locations for reasons including technical challenges and available resources. Therefore, we need ways to translate the sparse sea-surface observations into spatial fields for models. Currently, this is often done by using simplistic parameterisations through spatially resolved proxies, such as sea-surface temperature or ocean colour data retrieved by satellites. This approach’s accuracy suffers when there is a paucity of data or a lack of full understanding of chemical or physical processes that dictate concentrations. Machine learning techniques offer strong potential to improve and refine parameterisations based on spatially resolved proxies. An example is sea-surface halogen species; due to their potential to destroy ozone, a climate and air-quality gas, improving their parameterisation is important. Here I present examples of machine learning techniques that can be applied to sea-surface concentrations of species, including iodide (I-), bromoform (CH3Br) and dibromomethane (CH2Br2). I show this approach has skill for these species and is transferable to other species and problems in environmental chemistry. “

This talk is part of the AI4ER Seminar Series series.

This talk is included in these lists:

• AI4ER Seminar Series
• Cambridge Centre for Data-Driven Discovery (C2D3)
• Cambridge talks
• Chris Davis' list
• Interested Talks
• Trust & Technology Initiative - interesting events
• bld31
• https://ukri.zoom.us/j/92946956029
• ndk22's list
• ob366-ai4er
• rp587

Note that ex-directory lists are not shown.