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A Neural Weather Model for Eight-Hour Precipitation Forecasting
| | 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...
| | tech.preferred.jp
Neural network potential with charge transfer - Preferred Networks Research & Development
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| | The post is contributed by Mr. Kohei Shinohara, who joined PFN summer internship 2021. Japanese version is available here. Introduction I'm Kohei
| | blog.research.google
MetNet-2: Deep Learning for 12-Hour Precipitation Forecasting Google Research Blog
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| | www.analyticsvidhya.com
Interpretable Machine Learning | LIME In Machine Learning
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| Interpretable machine learning is key to understanding how machine learning models work. In this article learn about LIME and python implementation of it.