Distributed Deep Learning for Precipitation Nowcasting

Understand

Effective training of Deep Neural Networks requires massive amounts of data and compute.

• As a result, longer times are needed to train complex models requiring large datasets, which can severely limit research on model development and the exploitation of all available data.
• In this paper, this problem is investigated in the context of precipitation nowcasting, a term used to describe highly detailed short-term forecasts of precipitation and other hazardous weather.
• Convolutional Neural Networks (CNNs) are a powerful class of models that are well-suited for this task; however, the high resolution input weather imagery combined with model complexity required to process this data makes training CNNs to solve this task time consuming.