You can have missed an enormous development within the ML weather forecasting revolution over the vacations: GenCast: Google DeepMind’s recent generative model!  The importance of probabilistic weather forecasting can’t be overstated in various critical domains like flood forecasting, energy system planning, and transportation routing. Being capable of accurately gauge the uncertainty in forecasts, especially concerning extreme events, is pivotal for making well-informed decisions that involve significant cost-benefit considerations and effective mitigation strategies.

Traditionally, the approach to probabilistic forecasting involves creating ensembles from physics-based models, which sample from a joint distribution over spatio-temporally coherent weather trajectories. However, this method will be computationally expensive. An appealing alternative is using machine learning (ML) forecast models to generate ensembles. Yet, the present cutting-edge ML forecast models for medium-range weather primarily concentrate on producing deterministic forecasts that minimize mean-squared error.

Despite the improved skill scores related to these models, they face a limitation when it comes to lacking physical consistency. This limitation becomes more pronounced at longer lead times, impacting their ability to characterize the joint distribution of weather events precisely. 

The paper introduces a novel machine learning-based approach for probabilistic weather forecasting generally known as GenCast. This revolutionary method generates global, 15-day ensemble forecasts that show superior accuracy in comparison with the leading operational ensemble forecast, namely the European Centre for Medium-range Weather Forecasts (ECMWF)’s ENS, all while requiring significantly less computation time. GenCast operates by implicitly modeling the joint probability distribution of the weather state over space and time. It works on a 1° latitude-longitude grid, utilizing 12-hour time steps, and represents six surface variables and 6 atmospheric variables at 13 vertical pressure levels.

The evaluation of GenCast’s forecasts shows that it keeps detailed patterns and consistency in weather predictions. Comparisons with ENS indicate that GenCast’s ensembles are only as reliable, if no more so. GenCast is efficient—it may possibly create a 15-day forecast in a few minute using a Cloud TPU v4. This means generating a lot of forecasts (𝑁 ensemble members) in a short while is feasible with multiple TPUs. This efficiency opens up the opportunity of using much larger ensembles in the long run. 

In a broader context, GenCast signifies a big advancement in machine learning-based weather forecasting, demonstrating higher proficiency than the leading operational ensemble forecast at a 1° resolution. This development marks a pivotal step toward ushering in a brand new era of ensemble forecasting driven by machine learning, expanding its relevance and usefulness across a various array of domains. Moreover, as we glance ahead, GenCast offers a glimpse into the potential of embracing machine learning to revolutionize our understanding and prediction of complex weather patterns, with far-reaching implications for various industries and decision-makers.

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