Google’s AI predicts local precipitation patterns ‘instantaneously’

Google hopes to tap AI and machine learning to make speedy local weather predictions. In a paper and accompanying blog post, the tech giant detailed an AI system that uses satellite images to produce “nearly instantaneous” and high-resolution forecasts — on average, with a roughly one kilometer resolution and a latency of only 5-10 minutes. The researchers behind it say it outperforms traditional models “even at these early stages of development.” The system takes a data-driven and physics-free approach to weather modeling, meaning it learns to approximate atmospheric physics from examples alone and not by incorporating prior knowledge. Underpinning it is a convolutional neural network that takes as input images of weather patterns and transforms them into new output images. As the Google researchers explain, a convolutional network comprises a sequence of layers where each layer is a set of mathematical operations. In this case, it’s a U-Net, where the layers are arranged in an encoding phase that decreases the resolution of images passing through them. A separate decoding phase expands the low-dimensional image representations created during the encoding phase.