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Google's AI predicts local precipitation patterns ‘instantaneously'

[2020.01.13, Mon 20:05] 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. Underpinning it is a convolutional neural network that takes as input images of weather patterns and transforms them into new output images. Inputs to the U-Net contain one channel per multispectral satellite image in a sequence of observations over a given hour. If there were 10 satellite images collected in an hour and each of those images was taken at 10 wavelengths, the image input would have 100 channels. "The numerical model used in HRRR can make better long term predictions, in part because it uses a full 3D physical model - cloud formation is harder to observe from 2D images, and so it is harder for methods to learn convective processes," explained the researchers. Early last year, IBM launched a new forecasting system developed by The Weather Company - the weather forecasting and information technology company it acquired in 2016 - capable of providing "High-precision" and local forecasting across the globe.
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