论文标题
通过Visual Transformers求解Weather4cast挑战3D图像
Solving the Weather4cast Challenge via Visual Transformers for 3D Images
论文作者
论文摘要
准确地预测天气是一项重要任务，因为许多现实世界的过程和决策取决于未来的气象条件。鉴于前一个小时的卫星观测作为背景，Neurips 2022挑战标题为“ Weather4cast”提出了预测接下来八个小时的降雨事件的问题。由于最近基于变压器的架构在计算机视觉中取得成功的动机，我们基于该体系结构实施并提出了两种方法，以应对这一挑战。我们发现，将不同的变压器与一些基线模型相结合可以实现我们在看不见的测试数据上可以衡量的最佳性能。我们的方法在比赛中排名第三。
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.
