Improved forecasts of solar wind parameters using the Kalman filter

M. E. Innocenti; G. Lapenta; B. Vršnak; F. Crespon; C. Skandrani; M. Temmer; A. Veronig; L. Bettarini; S. Markidis; M. Skender

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Improved forecasts of solar wind parameters using the Kalman filter

机译：使用卡尔曼滤波器改进对太阳风参数的预测

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Data assimilation through Kalman filtering is a powerful statistical tool that allows researchers to combine modeling and observations and thus to increase the degree of knowledge of a given system. The application of this technique to an empirical solar wind forecasting model which enables the forecasting of solar wind parameters from coronal hole observations is here described and discussed. The forecasts for the solar wind proton velocity and temperature and for the magnetic field magnitude with and without data assimilation are validated against Advanced Composition Explorer observations, and it is shown that Kalman filtering can improve the quality of the forecasts and extend the period of applicability of the baseline model. In a subset of cases, some degree of robustness toward solar transient activity not accounted for in the original model is also provided.

机译：通过卡尔曼滤波进行的数据同化是一个强大的统计工具，它使研究人员可以将建模和观察结合起来，从而增加了给定系统的知识程度。本文介绍并讨论了该技术在经验性太阳风预报模型中的应用，该模型能够根据冠状孔观测结果预测太阳风参数。根据Advanced Composition Explorer的观测结果验证了在有和没有数据同化的情况下对太阳风质子速度和温度以及磁场强度的预测，结果表明，卡尔曼滤波可以提高预测的质量，并延长适用时间。基线模型。在某些情况下，还提供了对原始模型未考虑的对太阳瞬变活动的一定程度的鲁棒性。

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来源
《Space Weather》 |2011年第10期|1-15|共15页
作者
M. E. Innocenti; G. Lapenta; B. Vršnak; F. Crespon; C. Skandrani; M. Temmer; A. Veronig; L. Bettarini; S. Markidis; M. Skender;
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作者单位

Centrum voor Plasma-Astrofysica, Departement Wiskunde, Katholieke Universiteit Leuven, Leuven, Belgium.;

Centrum voor Plasma-Astrofysica, Departement Wiskunde, Katholieke Universiteit Leuven, Leuven, Belgium.;

Faculty of Geodesy, Hvar Observatory, Zagreb, Croatia.;

Noveltis, Ramonville-Saint-Agne, France.;

Noveltis, Ramonville-Saint-Agne, France.;

Institute of Physics, University of Graz, Graz, Austria.;

Institute of Physics, University of Graz, Graz, Austria.;

Solar-Terrestrial Center of Excellence–SIDC, Royal Observatory of Belgium, Brussels, Belgium.;

Centrum voor Plasma-Astrofysica, Departement Wiskunde, Katholieke Universiteit Leuven, Leuven, Belgium.;

Faculty of Geodesy, Hvar Observatory, Zagreb, Croatia.;

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正文语种 eng
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Wind forecasting; Predictive models; Kalman filters; Forecasting; Data models; Wind speed;

机译：风速预报;预测模型;卡尔曼滤波器;预报;数据模型;风速;

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机译：基于卡尔曼滤波方法的健壮的预测框架，具有双重参数调整过程：在太阳能和光伏预测中的应用
2. Improving Forecast Skill of Lowland Hydrological Models Using Ensemble Kalman Filter and Unscented Kalman Filter [J] . Sun Y., Bao W., Valk K., Water resources research . 2020,第8Pta1期

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机译：基于Kalman滤波器的PV-柴油机混合系统具有极短期太阳辐射预测方法
5. Thermospheric composition forecasting using Kalman filtering techniques. [D] . Minter, Clifton Freeman, III. 2002

机译：使用卡尔曼滤波技术预测热层成分。
6. Probabilistic Solar Wind Forecasting Using Large Ensembles of Near‐Sun Conditions With a Simple One‐Dimensional Upwind Scheme [O] . Mathew J. Owens, Pete Riley -1

机译：使用简单的一维逆风方案利用近太阳条件的大集合进行概率太阳风预报
7. Improved forecasts of solar wind parameters using the Kalman filter [O] . M. E. Innocenti, G. Lapenta, B. Vršnak, 2011

机译：使用卡尔曼滤波器改进了太阳风参数的预测
8. Applications of Kalman Filtering and Maximum Likelihood Parameter Identification to Hydrologic Forecasting. [R] . Goldstein, J. D., Larimore, W. E. 1980

机译：卡尔曼滤波和极大似然参数识别在水文预报中的应用。

Improved forecasts of solar wind parameters using the Kalman filter

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