Skilful precipitation nowcasting using deep generative models of radar

What

DeepMind trained Deep Generative Models on radar observations in the hopes of creating a model that can accurately model & predict precipitation events within the immediate future (1-2 hours).

Why

Nowcasting (the act of predicting the weather in the next hour or so) is an extremely important task for a variety of fields.(Sports, transportation, news) Accurate weather predictions can improve safety, increase quality of life, and optimize logistical decisions. Current weather prediction systems have a long compute times & struggle with accurate predictions within 2-hours from a given point in time. Machine Learning methods can fill in this gap and provide quick weather forecasts for the immediate future.

How

  • The GAN approach is used for their Deep Generative Models.
    • The generator context is fed four consectuve radar observations
    • Two loss functions are used.
      • The first loss function/spatial discriminator is a CNN that attempts to distinguish real radar observations from generated ones.
      • The second loss/temporal discriminator is a 3D CNN that attempts to distinguish real & generated radar observations, but enforces a temporal consistency & penalizes predictions that skip or jump forward in time.
  • The precipitation events are 256 x 256 crops extracted from the radar stream.
    • The stream length is 110 minutes which results in 22 frames.
    • Trained on radar observations from 2016-2018
    • Radar observations from 2019 are used as a test set.

Key Findings

  • DeepMindā€™s Deep Generative Models achieved high ratings from meterologists surveyed. Over 89% ranked it as their preferred choice compared to existing methods.
  • More work is needed to improve accuracy on rare & intense weather events
  • The model can generate predictions within seconds on just a NIVIDIA V100 GPU.
  • DeepMindā€™s work shows you do not need to encode physical assumptions about the weather to achive promising results. This is not news to ML practioners but more proof of a networks ability to model complex phenomena.

Open Questions

  • Would a models performance improve if some assumptions/meterological knowledge was encoded?
  • If trained on radar observations from a variety of regions, would a models ability to predict improve?

My perspective

This is my first time attempting to summarize a paper in this field. So Iā€™ll save you my opinion until its informed by more papers & model building experience. A lot of the information I gleaned from this paper is all new to me. You can load the model & make predictions using this notebook provided by DeepMind. If you find the methodology interesting go ahead & give the paper a read for details I did not include!

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