一种基于注意力机制和时空推理的强烈对流性降水预测方法
Ziliang Zhao1, Zhangu Wang2, Guoyu Zhao3
1College of Transportation, Shandong University of Science and Technology, Shandong, 266590, Qingdao, China.
Scientific reports
|August 16, 2024
概括
准确预测强烈的对流降水是具有挑战性的. 这项研究引入了一个ResNet-Attention-BiLSTM模型,通过增强特征提取和时空推理来提高降水预测的准确性.
科学领域:
- 气象学 天气学
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 准确预测强烈的对流降雨仍然是天气预报的一个重大挑战.
- 目前的雷达回声抽取方法存在不足的输入信息和低于最佳的模型架构问题.
研究的目的:
- 开发一个先进的降水预测模型,用于强烈的对流事件.
- 通过雷达观测,提高天气预报的准确性和有效性.
主要方法:
- 一种新的预测模型,ResNet-Attention-BiLSTM,集成了残余神经网络 (ResNet),注意力机制和双向长期短期记忆 (BiLSTM).
- ResNet用于关键极端天气信息提取和残留学习,以减轻预测模型问题.
- 适应性特征融合和降水图像数据特征增强提取的注意力机制.
- 时空推理用于捕捉雷达观测序列中的时间依赖性.
主要成果:
- 通过ResNet-Attention-BiLSTM模型,比现有方法显著改进.
- 平均绝对百分比误差在不同范围 (1公里,3公里,7公里) 中减少了高达18.72%.
- 确定系数 (R2) 增加至10.89%,表明预测性能提高.
结论:
- 拟议的ResNet-Attention-BiLSTM模型是有效的,并且实现了强有力的对流性降水预测的最新性能.
- 整合ResNet,attention和BiLSTM为时空天气预报提供了一个强大的方法.
- 该模型能够在雷达数据中捕捉复杂的时空动态,从而更准确地预测降水情况.
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