Session

DescriptionWeather and climate science offers an overwhelming amount of data both in terms of observations and model output. This data is used to understand parts of the non-linear behaviour of the Earth System (1) by extracting additional knowledge from the data or (2) by improving the quality of the models that are used for weather and climate predictions. An example of the former case is improved prediction of large scale phenomena such as El Nino. An example of the latter is the improvement of a Physics parameterisation scheme of atmosphere and ocean models through detailed analysis of the errors in a large number of datasets. One way to realise these opportunities is to use new tools from machine learning. This minisymposium showcases examples from current, practical, usage of machine learning in the weather and climate domain. Applications to the use cases (1) and (2) above will be discussed as well as where machine learning can assist in reducing the computational cost of models.