使用观测和数值天气预报数据进行基于告知器的温度预测
Jimin Jun1, Hong Kook Kim1,2
1School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju 61005, Republic of Korea.
Sensors (Basel, Switzerland)
|August 26, 2023
概括
本研究介绍了一种基于Informer的模型,用于准确的温度预测,通过整合周期性数据和解决长期依赖性来改进天气预报,优于以前的方法.
科学领域:
- 气象学和大气科学 气象学和大气科学
- 人工智能和机器学习
- 时间序列分析时间序列分析
背景情况:
- 像CNN-BLSTM这样的深度学习模型在温度预测方面表现有希望,但在时间数据集成和长期依赖方面存在困难.
- 现有的模型表现出性能降低与延长的预测时间长度,限制其实际应用.
- 在天气预报中,需要先进的模型,能够有效地处理时间动态和多样化的数据源,这一点至关重要.
研究的目的:
- 提出一种基于Informer的新型温度预测模型,克服现有的深度学习方法的局限性.
- 调查将时间周期信息和自动天气站 (AWS) 和局部数据同化和预测系统 (LDAPS) 的数据融合的影响.
- 评估模型在缓解长期依赖问题和在延长时间内提高预测准确性的表现.
主要方法:
- 开发了一个基于Informer的深度学习架构,这是变压器的一个变体,专门用于时间序列数据.
- 将时间周期信息集成到模型的输入中,以增强对时间模式的学习.
- 实施了结合AWS和LDAPS数据的融合操作,以评估它们对预测准确性的单独和综合影响.
- 利用根-平均-平方误差 (RMSE) 和平均绝对误差 (MAE) 在各种预测视界 (6-336小时) 中进行定量性绩效评估.
主要成果:
- 拟议的基于Informer的模型在温度预测方面表现优于CNN-BLSTM模型.
- 该模型实现了平均RMSE的相对减少0.25°C和MAE的相对减少0.203°C.
- 整合周期性信息和合并AWS/LDAPS数据有助于提高预测准确度.
- 该模型有效地解决了长期依赖问题,在更长的预测间隔内保持性能.
结论:
- 基于Informer的温度预测模型比传统的深度学习方法有了显著的进步,特别是在长期预测方面.
- 时间周期数据和多源天气信息的有效整合对于提高预测准确性至关重要.
- 该模型能够减轻长期依赖,使其成为运营天气预报应用的有希望的工具.
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