利用 LSTM 神经网络为空中交通流量管理进行战术前对流预测

IF 2.3 4区 地球科学 Q3 METEOROLOGY & ATMOSPHERIC SCIENCES
Aniel Jardines, Manuel Soler, Javier García-Heras, Matteo Ponzano, Laure Raynaud
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引用次数: 0

摘要

本文旨在探索用于高分辨率数值天气预报(NWP)产品后处理的机器学习技术,以便及早发现对流。来自 Arome 集合预报系统的数据和来自法国气象局快速发展雷暴(RDT)产品的卫星观测数据被用来训练一个循环神经网络模型,以预测完全对流和中等对流区域。学习任务被表述为使用长短期记忆(LSTM)网络结构的二元分类问题。利用接收器运行特征曲线(ROC)、布赖尔评分和可靠性等指标,将 LSTM 模型的结果与预测对流的基于对象的概率方法进行比较。结果表明,在对中等对流区域进行分类时,LSTM 模型的表现与基于对象的概率基准相似,而在对完全对流区域进行分类时,LSTM 模型的技能有所提高。最后,在空中交通管理的背景下介绍了 LSTM 模型的结果,以展示机器学习模型在业务应用中的潜在用途。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Pre-tactical convection prediction for air traffic flow management using LSTM neural network

Pre-tactical convection prediction for air traffic flow management using LSTM neural network

This paper aims to explore machine learning techniques for post-processing high-resolution Numerical Weather Prediction (NWP) products for the early detection of convection. Data from the Arome Ensemble Prediction System and satellite observations from the Rapidly Developing Thunderstorm (RDT) product by Météo-France are used to train a recurrent neural network model to predict areas of total convection and moderate convection. The learning task is formulated as a binary classification problem using a long short-term memory (LSTM) network architecture. Results from the LSTM model are compared with an object-based probabilistic approach to forecast convection using metrics such as a receiver operating characteristics (ROC) curve, the Brier score and reliability. Results indicate that the LSTM model performs similarly to the object-based probabilistic benchmark when classifying moderate convection areas and shows improved skill when classifying areas of total convective. Finally, the LSTM model results are presented within an air traffic management context to showcase the potential use of machine learning models within an operational application.

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来源期刊
Meteorological Applications
Meteorological Applications 地学-气象与大气科学
CiteScore
5.70
自引率
3.70%
发文量
62
审稿时长
>12 weeks
期刊介绍: The aim of Meteorological Applications is to serve the needs of applied meteorologists, forecasters and users of meteorological services by publishing papers on all aspects of meteorological science, including: applications of meteorological, climatological, analytical and forecasting data, and their socio-economic benefits; forecasting, warning and service delivery techniques and methods; weather hazards, their analysis and prediction; performance, verification and value of numerical models and forecasting services; practical applications of ocean and climate models; education and training.
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