从太阳风数据预测扰动风暴时间指数(Dst)的MagNet-A数据科学竞赛

IF 3.8 2区 地球科学 Q2 ASTRONOMY & ASTROPHYSICS
Manoj Nair, Rob Redmon, Li‐Yin Young, Arnaud Chulliat, Belinda Trotta, Christine Chung, Greg Lipstein, Isaac Slavitt
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引用次数: 0

摘要

太阳风和地球磁层之间增强的相互作用可能导致空间天气和地磁风暴,这些天气和地磁风暴有可能破坏关键技术,如磁导航、无线电通信和电网。地磁风暴的强度是用扰动-风暴-时间(Dst)指数来测量的。Dst指数是通过平均四个近赤道观测站观测到的磁场水平分量来计算的,并用于驱动地磁扰动模型。Dst指数作为磁层动力学的关键指标,用于驱动高清晰度地磁实时模型等地磁扰动模型。自1975年以来,已经提出了预报模式,仅从拉格朗日- 1位置的太阳风观测来预测Dst。然而,虽然最近的机器学习(ML)模型通常比其他方法表现得更好,但许多模型不适合操作使用。最近数据科学研究的指数级增长和机器学习工具的民主化,为具有明确约束和评估指标的特定问题解决任务的众包提供了可能性。为此,美国国家海洋和大气管理局(NOAA)的国家环境信息中心和科罗拉多大学环境科学合作研究所开展了一项名为“磁铁:地磁场模型”的开放数据科学挑战。该挑战吸引了622名参与者,使用各种ML方法提交了1197个模型。满足评估标准的顶级模型在操作上可行,可再培训,适合NOAA的业务需求。文章总结了比赛结果和经验教训。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
MagNet—A Data‐Science Competition to Predict Disturbance Storm‐Time Index (Dst) From Solar Wind Data
Abstract Enhanced interaction between solar‐wind and Earth's magnetosphere can cause space weather and geomagnetic storms that have the potential to damage critical technologies, such as magnetic navigation, radio communications, and power grids. The severity of a geomagnetic storm is measured using the disturbance‐storm‐time ( Dst ) index. The Dst index is calculated by averaging the horizontal component of the magnetic field observed at four near‐equatorial observatories and is used to drive geomagnetic disturbance models. As a key specification of the magnetospheric dynamics, the Dst index is used to drive geomagnetic disturbance models such as the High Definition Geomagnetic Model—Real Time. Since 1975, forecasting models have been proposed to forecast Dst solely from solar wind observations at the Lagrangian‐1 position. However, while the recent Machine‐Learning (ML) models generally perform better than other approaches, many are unsuitable for operational use. Recent exponential growth in data‐science research and the democratization of ML tools have opened up the possibility of crowd‐sourcing specific problem‐solving tasks with clear constraints and evaluation metrics. To this end, National Oceanic and Atmospheric Administration (NOAA)'s National Centers for Environmental Information and the University of Colorado's Cooperative Institute for Research in Environmental Sciences conducted an open data‐science challenge called “MagNet: Model the Geomagnetic Field.” The challenge attracted 622 participants, resulting in 1,197 model submissions that used various ML approaches. The top models that met the evaluation criteria are operationally viable and retrainable and suitable for NOAA's operational needs. The paper summarizes the competition results and lessons learned.
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来源期刊
CiteScore
5.90
自引率
29.70%
发文量
166
审稿时长
>12 weeks
期刊介绍: Space Weather: The International Journal of Research and Applications (SWE) is devoted to understanding and forecasting space weather. The scope of understanding and forecasting includes: origins, propagation and interactions of solar-produced processes within geospace; interactions in Earth’s space-atmosphere interface region produced by disturbances from above and below; influences of cosmic rays on humans, hardware, and signals; and comparisons of these types of interactions and influences with the atmospheres of neighboring planets and Earth’s moon. Manuscripts should emphasize impacts on technical systems including telecommunications, transportation, electric power, satellite navigation, avionics/spacecraft design and operations, human spaceflight, and other systems. Manuscripts that describe models or space environment climatology should clearly state how the results can be applied.
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