Summary of Gptcast: a Weather Language Model For Precipitation Nowcasting, by Gabriele Franch et al.
GPTCast: a weather language model for precipitation nowcasting
by Gabriele Franch, Elena Tomasi, Rishabh Wanjari, Virginia Poli, Chiara Cardinali, Pier Paolo Alberoni, Marco Cristoforetti
First submitted to arxiv on: 2 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Atmospheric and Oceanic Physics (physics.ao-ph)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel generative deep-learning method called GPTCast is introduced for predicting precipitation using radar-based data. Inspired by advancements in large language models (LLMs), the approach employs a GPT model as a forecaster to learn spatiotemporal precipitation dynamics from tokenized radar images. The tokenizer uses a Quantized Variational Autoencoder with a novel reconstruction loss that promotes faithful reconstruction of high rainfall rates. This leads to realistic ensemble forecasts and accurate uncertainty estimation. The model is trained without randomness, learning all variability solely from the data, which is then exposed by the model at inference for ensemble generation. GPTCast outperforms state-of-the-art ensemble extrapolation methods using a 6-year radar dataset over the Emilia-Romagna region in Northern Italy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GPTCast is a new way to predict the weather based on radar images. It uses a special type of artificial intelligence called a generative deep-learning model. This model looks at pictures of rain and learns how to make predictions about where it will fall next. The model is really good at making these predictions and can even tell us how likely it is that something will happen. GPTCast works better than other methods for predicting the weather, and it uses a special way of looking at data to make sure its predictions are accurate. |
Keywords
* Artificial intelligence * Deep learning * Gpt * Inference * Spatiotemporal * Tokenizer * Variational autoencoder
