Accurately forecasting the weather is an important task, as many real-world
processes and decisions depend on future meteorological conditions. The NeurIPS
2022 challenge entitled Weather4cast poses the problem of predicting rainfall
events for the next eight hours given the preceding hour of satellite
observations as a context. Motivated by the recent success of transformer-based
architectures in computer vision, we implement and propose two methodologies
based on this architecture to tackle this challenge. We find that ensembling
different transformers with some baseline models achieves the best performance
we could measure on the unseen test data. Our approach has been ranked 3rd in
the competition.

本文介绍了通过使用基于 transformer 的算法来解决 NeurIPS 2022 中的 Weather4cast 挑战，预测在接下来的 8 小时内的降雨事件，并通过将不同的 transformer 和基线模型进行集合，获得了最好的性能。

使用可视 Transformer 解决 3D 图像 Weather4cast 挑战

Solving the Weather4cast Challenge via Visual Transformers for 3D Images

The Weather4cast 2021 competition gave the participants a task of predicting
the time evolution of two-dimensional fields of satellite-based meteorological
data. This paper describes the author's efforts, after initial success in the
first stage of the competition, to improve the model further in the second
stage. The improvements consisted of a shallower model variant that is
competitive against the deeper version, adoption of the AdaBelief optimizer,
improved handling of one of the predicted variables where the training set was
found not to represent the validation set well, and ensembling multiple models
to improve the results further. The largest quantitative improvements to the
competition metrics can be attributed to the increased amount of training data
available in the second stage of the competition, followed by the effects of
model ensembling. Qualitative results show that the model can predict the time
evolution of the fields, including the motion of the fields over time, starting
with sharp predictions for the immediate future and blurring of the outputs in
later frames to account for the increased uncertainty.

该研究承担了预测卫星气象数据的二维场的时间演变的任务，使其成为一个更加具有竞争力的浅层模型版本，并采用了 AdaBelief 优化器和改进处理，其中预测变量训练集与验证集不匹配时，通过使用多个模型来提高表现。