基于深度学习的夏季湍流强度卫星观测估计

IF 1.9 4区 地球科学 Q2 ENGINEERING, OCEAN Journal of Atmospheric and Oceanic Technology Pub Date : 2023-09-07 DOI:10.1175/jtech-d-22-0137.1
Yoonjin Lee, Soo-Hyun Kim, Yoo-Jeong Noh, Jung-Hoon Kim
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引用次数: 0

摘要

乱流是我们在飞行中最希望避免的。基于数值天气预报(NWP)模型的湍流诊断方法为飞行员提供了宝贵的指导。一般情况下,基于nwp的湍流诊断具有较高的检测精度。然而,仍有改进的空间,例如捕获对流诱导的湍流。在这种情况下,观测数据有利于正确定位对流区域,并有助于提供相应的湍流信息。地球静止卫星资料是利用其水汽波段信息进行高层湍流探测的常用资料。地球静止运行环境卫星(GOES)-16携带先进基线成像仪(ABI),使我们能够以改进的空间、时间和光谱分辨率进一步观测大气。它的三个水汽带使我们能够观察到大气的不同垂直部分,并且从它的红外窗口带可以推断对流活动。ABI的多光谱信息有助于推断不同垂直水平的湍流强度。本研究开发了基于U-Net的机器学习模型,该模型将ABI图像作为输入,以估计三个垂直水平的湍流强度:10-18 kft, 18-24 kft和24 kft以上。在6种不同的u - net模型中,过滤器大小为3的U-Net3+模型对试点报告(PIREP)的性能最好。通过两个案例分析,展示了U-Net3+模型的优缺点。结果在24kft以上往往被高估,但10- 18kft和18- 24kft的估计值与PIREP非常吻合,特别是在对流区域附近。
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Deep learning-based summertime turbulence intensity estimation using satellite observations
Turbulence is what we want to avoid the most during flight. Numerical weather prediction (NWP) model-based methods for diagnosing turbulence have offered valuable guidance for pilots. NWP-based turbulence diagnostics show high accuracy in detecting turbulence in general. However, there is still room for improvements such as capturing convectively induced turbulence. In such cases, observation data can be beneficial to correctly locate convective regions and help provide corresponding turbulence information. Geostationary satellite data is commonly used for upper-level turbulence detection by utilizing its water vapor band information. The Geostationary Operational Environmental Satellite (GOES)-16 carries the Advanced Baseline Imager (ABI) which enables us to observe further down the atmosphere with improved spatial, temporal, and spectral resolutions. Its three water vapor bands allow us to observe different vertical parts of the atmosphere, and from its infrared window bands, convective activity can be inferred. Such multi-spectral information from ABI can be helpful in inferring turbulence intensity at different vertical levels. This study develops U-Net based machine learning models that take ABI imagery as inputs to estimate turbulence intensity at three vertical levels: 10-18 kft, 18-24 kft, and above 24 kft. Among six different U-Net-based models, U-Net3+ model with a filter size of three showed the best performance against the pilot report (PIREP). Two case studies are presented to show the strengths and weaknesses of the U-Net3+ model. The results tend to be overestimated above 24 kft, but estimates of 10-18 kft and 18-24 kft agree well with the PIREP, especially near convective regions.
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来源期刊
CiteScore
4.50
自引率
9.10%
发文量
135
审稿时长
3 months
期刊介绍: The Journal of Atmospheric and Oceanic Technology (JTECH) publishes research describing instrumentation and methods used in atmospheric and oceanic research, including remote sensing instruments; measurements, validation, and data analysis techniques from satellites, aircraft, balloons, and surface-based platforms; in situ instruments, measurements, and methods for data acquisition, analysis, and interpretation and assimilation in numerical models; and information systems and algorithms.
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