Huracan: A skillful end-to-end data-driven system for ensemble data assimilation and weather prediction
This work addresses the need for more efficient and accurate operational weather forecasting by providing an end-to-end system that reduces reliance on traditional NWP, representing a significant but incremental advance in the field.
The authors tackled the problem of end-to-end data-driven weather prediction by proposing Huracan, a system that combines ensemble data assimilation and forecasting using only observations as inputs, achieving accuracy comparable to state-of-the-art NWP on 75.4% of variable and lead time combinations.
Over the past few years, machine learning-based data-driven weather prediction has been transforming operational weather forecasting by providing more accurate forecasts while using a mere fraction of computing power compared to traditional numerical weather prediction (NWP). However, those models still rely on initial conditions from NWP, putting an upper limit on their forecast abilities. A few end-to-end systems have since been proposed, but they have yet to match the forecast skill of state-of-the-art NWP competitors. In this work, we propose Huracan, an observation-driven weather forecasting system which combines an ensemble data assimilation model with a forecast model to produce highly accurate forecasts relying only on observations as inputs. Huracan is not only the first to provide ensemble initial conditions and end-to-end ensemble weather forecasts, but also the first end-to-end system to achieve an accuracy comparable with that of ECMWF ENS, the state-of-the-art NWP competitor, despite using a smaller amount of available observation data. Notably, Huracan matches or exceeds the continuous ranked probability score of ECMWF ENS on 75.4% of the variable and lead time combinations. Our work is a major step forward in end-to-end data-driven weather prediction and opens up opportunities for further improving and revolutionizing operational weather forecasting.