Multi-step strategies on short-term stratospheric wind prediction using neural networks

doi:10.21203/rs.3.rs-7699720/v1

Multi-step strategies on short-term stratospheric wind prediction using neural networks

2025 · doi:10.21203/rs.3.rs-7699720/v1

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This preprint studies neural network approaches for multi-step short-term forecasting of zonal wind in the stratosphere, using convolutional neural networks (CNN) and LSTM models for single-point and regional predictions. It compares four multi-step strategies (including multi-input multi-output, DirRec, and hierarchical time aggregation with DirRec) against a recursive approach, with the stratospheric wind modeled for a specific longitude–latitude domain. The authors report that CNN architecture outperforms LSTM, and that MIMO, DirRec, and HTA yield higher accuracy than recursive prediction, while DirRec demands significantly higher computational cost; HTA is better for 1–6 step forecasts and MIMO for 7–12 steps. A major caveat explicitly stated is that the work is a preprint that has not been peer reviewed. The paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.

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Abstract

Abstract Artificial intelligence (AI) weather forecasting has advanced rapidly owing to its high prediction accuracy and exceptional computational efficiency. However, research on stratospheric forecasting and multi-step prediction strategies remains relatively underdeveloped compared to studies focused on the troposphere or model architecture improvements. Stratospheric wind plays a crucial role in the flight performance of balloon experiments. Although Numerical Weather Prediction can generate forecasts by solving atmospheric partial differential equations, its expensive computational cost fundamentally limits the capability of short-term prediction. In recent years, artificial intelligence technology has been increasingly applied to atmospheric predictions. In this study, we build convolutional neural network (CNN) and long and short-term memory (LSTM) network models for single point (95.5° E, 37.5° N) and area (90–100° E, 30–40° N) predictions of the short-term zonal wind in the stratosphere. The convolution architecture is found to outperform the LSTM models. Four prediction strategies including multi-input multi-output (MIMO), DirRec, and hierarchical time aggregation (HTA) and DirRec are implemented and compared to achieve multi-step forecasting. MIMO, DirRec, and HTA strategies are found to have higher prediction accuracy than the recursive strategy, and the DirRec strategy requires significantly higher computational costs. Hence HTA and MIMO are considered more suitable in stratospheric wind pre-diction. The HTA strategy is better for forecasts in 1-6th steps, while the MIMO strategy is optimal for forecasts in 7-12th steps. The HTA performs faster in training time than multi-input multi-output, but slower inference time. Our comparison is helpful for selecting appropriate strategies for different neural network–based forecast scenarios.

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Multi-step strategies on short-term stratospheric wind prediction using neural networks | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Multi-step strategies on short-term stratospheric wind prediction using neural networks Zhengqing Liu, Junfeng Yang, Dan Liu, Jianmei Wang, Yiming Zhang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7699720/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Artificial intelligence (AI) weather forecasting has advanced rapidly owing to its high prediction accuracy and exceptional computational efficiency. However, research on stratospheric forecasting and multi-step prediction strategies remains relatively underdeveloped compared to studies focused on the troposphere or model architecture improvements. Stratospheric wind plays a crucial role in the flight performance of balloon experiments. Although Numerical Weather Prediction can generate forecasts by solving atmospheric partial differential equations, its expensive computational cost fundamentally limits the capability of short-term prediction. In recent years, artificial intelligence technology has been increasingly applied to atmospheric predictions. In this study, we build convolutional neural network (CNN) and long and short-term memory (LSTM) network models for single point (95.5° E, 37.5° N) and area (90–100° E, 30–40° N) predictions of the short-term zonal wind in the stratosphere. The convolution architecture is found to outperform the LSTM models. Four prediction strategies including multi-input multi-output (MIMO), DirRec, and hierarchical time aggregation (HTA) and DirRec are implemented and compared to achieve multi-step forecasting. MIMO, DirRec, and HTA strategies are found to have higher prediction accuracy than the recursive strategy, and the DirRec strategy requires significantly higher computational costs. Hence HTA and MIMO are considered more suitable in stratospheric wind pre-diction. The HTA strategy is better for forecasts in 1-6th steps, while the MIMO strategy is optimal for forecasts in 7-12th steps. The HTA performs faster in training time than multi-input multi-output, but slower inference time. Our comparison is helpful for selecting appropriate strategies for different neural network–based forecast scenarios. Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 30 Nov, 2025 Reviewers invited by journal 13 Oct, 2025 Editor assigned by journal 24 Sep, 2025 Submission checks completed at journal 24 Sep, 2025 First submitted to journal 24 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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However, research on stratospheric forecasting and multi-step prediction strategies remains relatively underdeveloped compared to studies focused on the troposphere or model architecture improvements. Stratospheric wind plays a crucial role in the flight performance of balloon experiments. Although Numerical Weather Prediction can generate forecasts by solving atmospheric partial differential equations, its expensive computational cost fundamentally limits the capability of short-term prediction. In recent years, artificial intelligence technology has been increasingly applied to atmospheric predictions. In this study, we build convolutional neural network (CNN) and long and short-term memory (LSTM) network models for single point (95.5\u0026deg; E, 37.5\u0026deg; N) and area (90\u0026ndash;100\u0026deg; E, 30\u0026ndash;40\u0026deg; N) predictions of the short-term zonal wind in the stratosphere. The convolution architecture is found to outperform the LSTM models. Four prediction strategies including multi-input multi-output (MIMO), DirRec, and hierarchical time aggregation (HTA) and DirRec are implemented and compared to achieve multi-step forecasting. MIMO, DirRec, and HTA strategies are found to have higher prediction accuracy than the recursive strategy, and the DirRec strategy requires significantly higher computational costs. Hence HTA and MIMO are considered more suitable in stratospheric wind pre-diction. The HTA strategy is better for forecasts in 1-6th steps, while the MIMO strategy is optimal for forecasts in 7-12th steps. The HTA performs faster in training time than multi-input multi-output, but slower inference time. 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