Time-Series Embeddings from Language Models: A Tool for Wind Direction Nowcasting

IF 2.8 3区 地球科学 Q3 METEOROLOGY & ATMOSPHERIC SCIENCES
Décio Alves, Fábio Mendonça, Sheikh Shanawaz Mostafa, Fernando Morgado-Dias
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引用次数: 0

Abstract

Wind direction nowcasting is crucial in various sectors, particularly for ensuring aviation operations and safety. In this context, the TELMo (Time-series Embeddings from Language Models) model, a sophisticated deep learning architecture, has been introduced in this work for enhanced wind-direction nowcasting. Developed by using three years of data from multiple stations in the complex terrain of an international airport, TELMo incorporates the horizontal u (east–west) and v (north–south) wind components to significantly reduce forecasting errors. On a day with high wind direction variability, TELMo achieved mean absolute error values of 5.66 for 2-min, 10.59 for 10-min, and 14.79 for 20-min forecasts, processed within a swift 9-ms/step timeframe. Standard degree-based analysis, in comparison, yielded lower performance, emphasizing the effectiveness of the u and v components. In contrast, a Vanilla neural network, representing a shallow-learning approach, underperformed in all analyses, highlighting the superiority of deep learning methodologies in wind direction nowcasting. TELMo is an efficient model, capable of accurately forecasting wind direction for air traffic operations, with an error less than 20° in 97.49% of the predictions, aligning with recommended international thresholds. This model design enables its applicability across various geographical locations, making it a versatile tool in global aviation meteorology.

语言模型的时间序列嵌入:风向预报工具
风向预报在各行各业都至关重要,尤其是在确保航空运营和安全方面。在此背景下,本研究引入了一种复杂的深度学习架构--TELMo(语言模型的时间序列嵌入)模型,用于增强风向预报。TELMo 是利用一个国际机场复杂地形中多个站点的三年数据开发的,它结合了水平 u(东西向)和 v(南北向)风向分量,可显著减少预报误差。在风向变化较大的一天,TELMo 在 9 毫秒/步的快速时间框架内,2 分钟、10 分钟和 20 分钟预报的平均绝对误差分别为 5.66、10.59 和 14.79。相比之下,基于度数的标准分析性能较低,凸显了 u 和 v 部分的有效性。相比之下,代表浅层学习方法的香草神经网络在所有分析中都表现不佳,凸显了深度学习方法在风向预报中的优越性。TELMo 是一个高效的模型,能够为空中交通运行准确预测风向,97.49% 的预测误差小于 20°,符合国际推荐的阈值。该模型的设计使其适用于不同的地理位置,成为全球航空气象学的多功能工具。
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来源期刊
Journal of Meteorological Research
Journal of Meteorological Research METEOROLOGY & ATMOSPHERIC SCIENCES-
CiteScore
6.20
自引率
6.20%
发文量
54
期刊介绍: Journal of Meteorological Research (previously known as Acta Meteorologica Sinica) publishes the latest achievements and developments in the field of atmospheric sciences. Coverage is broad, including topics such as pure and applied meteorology; climatology and climate change; marine meteorology; atmospheric physics and chemistry; cloud physics and weather modification; numerical weather prediction; data assimilation; atmospheric sounding and remote sensing; atmospheric environment and air pollution; radar and satellite meteorology; agricultural and forest meteorology and more.
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