Meteorological Neural Network 2 (MetNet-2) - a new probabilistic weather model featuring a forecasting range of up to 12 hours of lead time at a frequency of 2 minutes.
Blog post: Google AI Blog: MetNet-2: Deep Learning for 12-Hour Precipitation Forecasting 8


Within weather forecasting, deep learning techniques have shown particular promise for nowcasting 1 — i.e., predicting weather up to 2-6 hours ahead. Previous work has focused on using direct neural network models for weather data 1, extending neural forecasts from 0 to 8 hours with the MetNet architecture, generating continuations of radar data for up to 90 minutes ahead, and interpreting the weather information learned by these neural networks. Still, there is an opportunity for deep learning to extend improvements to longer-range forecasts.
To that end, in “Skillful Twelve Hour Precipitation Forecasts Using Large Context Neural Networks”, we push the forecasting boundaries of our neural precipitation model to 12 hour predictions while keeping a spatial resolution of 1 km and a time resolution of 2 minutes. By quadrupling the input context, adopting a richer weather input state, and extending the architecture to capture longer-range spatial dependencies, MetNet-2 substantially improves on the performance of its predecessor, MetNet. Compared to physics-based models, MetNet-2 outperforms the state-of-the-art HREF ensemble model for weather forecasts up to 12 hours ahead.


Interpreting What MetNet-2 Learns About Weather
Because MetNet-2 does not use hand-crafted physical equations, its performance inspires a natural question: What kind of physical relations about the weather does it learn from the data during training? Using advanced interpretability tools 1, we further trace the impact of various input features on MetNet-2’s performance at different forecast timelines. Perhaps the most surprising finding is that MetNet-2 appears to emulate the physics described by Quasi-Geostrophic Theory, which is used as an effective approximation of large-scale weather phenomena. MetNet-2 was able to pick up on changes in the atmospheric forces, at the scale of a typical high- or low-pressure system (i.e., the synoptic scale), that bring about favorable conditions for precipitation, a key tenet of the theory.
Conclusion
MetNet-2 represents a step toward enabling a new modeling paradigm for weather forecasting that does not rely on hand-coding the physics of weather phenomena, but rather embraces end-to-end learning from observations to weather targets and parallel forecasting on low-precision hardware. Yet many challenges remain on the path to fully achieving this goal, including incorporating more raw data about the atmosphere directly (rather than using the pre-processed starting state from physical models), broadening the set of weather phenomena, increasing the lead time horizon to days and weeks, and widening the geographic coverage beyond the United States.


MetNet-2 architecture1448×575 208 KB

(MetNet-2 architecture - paper 5)

Also, check out:

  
    
    
    [Research ] Nowcasting the Next Hour of Rain (by DeepMind) Research & Models
  
  
    GitHub: https://github.com/deepmind/deepmind-research/tree/master/nowcasting 
Colab: Google Colab 
Paper: Skilful precipitation nowcasting using deep generative models of radar | Nature 

Recently introduced deep learning methods use radar to directly predict future rain rates, free of physical constraints5,6. While they accurately predict low-intensity rainfall, their operational utility is limited because their lack of constraints produces blurry nowcasts at longer lead times, yielding poor …