Artificial Intelligence (AI) is nowadays a central key for many modern applications and research areas, e.g. autonomous driving, image / face recognition as well as system simulation and optimization. Consequently, AI gains more and more importance also in weather and climate related sciences. This session focuses on machine learning and computer vision techniques and aims at bringing together research with weather and climate related background with relevant contributions from computer sciences using these techniques.

Contributions from all kinds of machine learning and computer vision studies in weather and climate on a wide range of time-scales are encouraged, including
• All kinds of postprocessing studies of Numerical Weather Prediction (NWP) forecasts (including projects such as DeepRain, etc.)
• Nowcasting studies, studies using satellite data, radar data, and observational weather data
• Seasonal forecast studies
• Climate related studies

These studies may e.g. deal with one or more of the fields
• Pre-processing of weather and climate data for machine learning purposes (e.g. forecasts from Numerical Weather Prediction (NWP) models, observational / satellite / radar data, etc.)
• Dimensionality reduction of weather and climate data, extraction of relevant features
• All kinds of supervised and unsupervised learning techniques
• Application of computer vision algorithms
• Regression and classification tasks
• Artificial Neural Networks, Deep / Convolutional / Recurrent Neural Networks, LSTMs, Decision Trees, Support Vector Machines, Ensemble Learning and Random Forests, etc.