About the symposium
We are currently living in the age of data, where more than ever before in human history data is being collected and available for use. For scientific purposes, the quantity and quality of both observation and model data has dramatically increased compared to the previous century.
Together with the recent advancements (in computer hardware capabilities and) machine learning algorithms, this data provides new, exciting opportunities for applications in meteorology and climate. These applications are very diverse and have a direct impact on our daily lives - ranging from more accurate weather/climate forecasts to improved air quality (maybe not the best examples). Within meteorology and climate, machine learning algorithms can be a valuable tool to extract the relevant information from the large quantities of available data. In scientific literature, recently there have already been examples where such techniques were successfully applied to improve for instance the accuracy, reduce the computation cost of simulations, or even improve our understanding of the physics. However, for most practical applications still many challenges remain.
We therefore aim with this symposium to inform the participants about the currently investigated applications involving machine learning techniques, both within the meteorology and climate science disciplines. In doing so, we hope to stimulate an interdisciplinary exchange of ideas that may help to identify other potential applications, and overcome some of the remaining challenges hindering the widespread usage of machine learning techniques within meteorology and climate.