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SUMMARY:Reducing emission-driven ozone uncertainties in climate and air-qu
 ality models using machine learning - Tomás Sherwen
DTSTART:20200616T100000Z
DTEND:20200616T113000Z
UID:TALK142138@talks.cam.ac.uk
CONTACT:Jonathan Rosser
DESCRIPTION:"We use global\, regional\, and local models to evaluate the c
 hemical composition of the air we breathe and explore questions motivated 
 by air quality and climate change. These models require 2D input values fo
 r variables such as concentrations of chemicals at the sea surface. Howeve
 r\, it is neither desirable nor possible to observe all species in all loc
 ations 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 s
 implistic parameterisations through spatially resolved proxies\, such as s
 ea-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 concent
 rations. 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 impor
 tant. Here I present examples of machine learning techniques that can be a
 pplied to sea-surface concentrations of species\, including iodide (I-)\, 
 bromoform (CH3Br) and dibromomethane (CH2Br2). I show this approach has sk
 ill for these species and is transferable to other species and problems in
  environmental chemistry. \n"
LOCATION:https://ukri.zoom.us/j/92946956029
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