论文标题
雨雷达图像和风向预测的融合在深度学习模型中
Fusion of rain radar images and wind forecasts in a deep learning model applied to rain nowcasting
论文作者
论文摘要
短期或中期降雨预测是一项主要任务，其中有多种环境应用，例如农业管理或洪水风险监测。现有的数据驱动方法，尤其是深度学习模型，在此任务中仅使用降雨雷达图像作为输入显示了重要的技能。为了确定使用其他气象参数（例如风）是否会改善预测，我们培训了一个深度学习模型，以融合了天气预报模型产生的降雨雷达图像和风速。将网络与仅在雷达数据，基本持续模型和基于光流的方法的方法中进行了比较。我们的网络的表现优于8％的F1得分，该F1得分在30分钟的地平线时间为预测的中等和较高降雨事件上计算出的F1得分。此外，与仅使用降雨雷达图像训练的相同架构相同的架构的表现优于7％。合并雨水和风数据也已被证明可以稳定训练过程，并能够进行重大改进，尤其是在难以预测的高降水降雨中。
Short- or mid-term rainfall forecasting is a major task with several environmental applications such as agricultural management or flood risk monitoring. Existing data-driven approaches, especially deep learning models, have shown significant skill at this task, using only rainfall radar images as inputs. In order to determine whether using other meteorological parameters such as wind would improve forecasts, we trained a deep learning model on a fusion of rainfall radar images and wind velocity produced by a weather forecast model. The network was compared to a similar architecture trained only on radar data, to a basic persistence model and to an approach based on optical flow. Our network outperforms by 8% the F1-score calculated for the optical flow on moderate and higher rain events for forecasts at a horizon time of 30 min. Furthermore, it outperforms by 7% the same architecture trained using only rainfall radar images. Merging rain and wind data has also proven to stabilize the training process and enabled significant improvement especially on the difficult-to-predict high precipitation rainfalls.
