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MetNet-2: Deep Learning for 12-Hour Precipitation Forecasting
| | blog.research.google
MetNet-2: Deep Learning for 12-Hour Precipitation Forecasting Google Research Blog
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| | gmd.copernicus.org
GMD - Deep learning model based on multi-scale feature fusion for precipitation nowcasting
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| | Abstract. Forecasting heavy precipitation accurately is a challenging task for most deep learning (DL)-based models. To address this, we present a novel DL architecture called multi-scale feature fusion (MFF) that can forecast precipitation with a lead time of up to 3?h. The MFF model uses convolution kernels with varying sizes to create multi-scale receptive fields. This helps to capture the movement features of precipitation systems, such as their shape, movement direction, and speed. Additionally, the...
| | niklasriewald.com
Paper Review: "MetNet: A Neural Weather Model for Precipitation Forecasting" | Niklas Riewald
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| | Is it possible to replace numerical weather prediction with deep learning? Certainly not yet. But first work is being done to investigate this question. In this post I want to discuss a paper called "MetNet: A Neural Weather Model for Precipitation Forecasting" that Google Research published back in March 2020 and in which they try...
| | research.google
RT-1: Robotics Transformer for real-world control at scale
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| Posted Keerthana Gopalakrishnan and Kanishka Rao, Google Research, Robotics at Google Major recent advances in multiple subfields of machine learni...