Domain of Influence Analysis: Implications for Data Assimilation in Space Weather Forecasting

Millas, Dimitrios; Innocenti, Maria Elena; Laperre, Brecht; Raeder, J.; Poedts, Stefaan; Lapenta, Giovanni

doi:10.3389/fspas.2020.571286

Cited by 4 publications

(3 citation statements)

References 82 publications

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“…A detailed description of the model and typical examples of OpenGGCM applications can be found in Raeder (2003), Raeder et al (2001b), Raeder and Lu (2005), Connor et al (2016), Raeder et al (2001a), Ge et al (2011), Raeder et al (2010, Ferdousi and Raeder (2016), Dorelli (2004), Raeder (2006), Berchem et al (1995), Moretto et al (2006), Vennerstrom et al (2005), Anderson et al (2017), Zhu et al (2009), Zhou et al (2012), andShi et al (2014), to name a few. Of particular relevance to this study is OpenGGCM-CTIM-RCM simulations that have recently been used for a domain of influence analysis, a technique rooted in data assimilation that can be used to understand what the most promising locations are for monitoring (i.e., spacecraft placing) in a complex system such as the magnetosphere (Millas et al, 2020).…”

Section: Global Magnetospheric Simulationsmentioning

confidence: 99%

Unsupervised classification of simulated magnetospheric regions

et al. 2021

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Abstract. In magnetospheric missions, burst-mode data sampling should be triggered in the presence of processes of scientific or operational interest. We present an unsupervised classification method for magnetospheric regions that could constitute the first step of a multistep method for the automatic identification of magnetospheric processes of interest. Our method is based on self-organizing maps (SOMs), and we test it preliminarily on data points from global magnetospheric simulations obtained with the OpenGGCM-CTIM-RCM code. The dimensionality of the data is reduced with principal component analysis before classification. The classification relies exclusively on local plasma properties at the selected data points, without information on their neighborhood or on their temporal evolution. We classify the SOM nodes into an automatically selected number of classes, and we obtain clusters that map to well-defined magnetospheric regions. We validate our classification results by plotting the classified data in the simulated space and by comparing with k-means classification. For the sake of result interpretability, we examine the SOM feature maps (magnetospheric variables are called features in the context of classification), and we use them to unlock information on the clusters. We repeat the classification experiments using different sets of features, we quantitatively compare different classification results, and we obtain insights on which magnetospheric variables make more effective features for unsupervised classification.

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Section: Global Magnetospheric Simulationsmentioning

confidence: 99%

Unsupervised classification of simulated magnetospheric regions

et al. 2021

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“…Of particular relevance to this study, OpenGGCM-CTIM-RCM simulations have recently been used for a Domain of Influence Analysis, a technique rooted in Data Assimilation that can be used to understand what are the most promising locations for monitoring (i.e. spacecraft placing) in a complex system such as the magnetosphere (Millas et al, 2020 (Raeder, 2003), which in this work has 325x150x150 cells, sufficient for our large scale classification purposes, while running for few hours on a modest number of cores. The simulated time is four hours.…”

Section: Global Magnetospheric Simulationsmentioning

confidence: 99%

Unsupervised classification of simulated magnetospheric regions

Innocenti

Amaya

Raeder

et al. 2021

Preprint

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Abstract. In magnetospheric missions, burst mode data sampling should be triggered in the presence of processes of scientific or opera- tional interest. We present an unsupervised classification method for magnetospheric regions, that could constitute the first-step of a multi-step method for the automatic identification of magnetospheric processes of interest. Our method is based on Self Organizing Maps (SOMs), and we test it preliminarily on data points from global magnetospheric simulations obtained with the OpenGGCM-CTIM-RCM code. The classification relies exclusively on local plasma properties at the selected data points, without information on their neighborhood or on their temporal evolution. We classify the SOM nodes into an automatically selected number of classes, and we obtain clusters that map to well defined magnetospheric regions. For the sake of result interpretability, we examine the SOM feature maps (magnetospheric variables are called features in the context of classification), and we use them to unlock information on the clusters. We repeat the classification experiments using different sets of features, and we obtain insights on which magnetospheric variables make more effective features for unsupervised classification.

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“…With the help of the ADAPT model, the photosphere DA uses physics‐based temporal evolution magnetic field observations of the Sun's surface to further optimize the initial boundary conditions of the corona model. DA can also be used for solar wind prediction, leading to an improvement in forecast skill of near‐Earth space (Anderson et al., 2009; Meadors et al., 2020; Millas et al., 2020). Twin experiments have been conducted to assess the performance of the Local Ensemble Transform Kalman Filter (Durazo et al., 2017; Elvidge & Angling, 2019) in the solar wind model ENLIL (Lang et al., 2017), showing the potential of DA in the improvement of solar wind prediction.…”

Section: Introductionmentioning

confidence: 99%

Research on the Effect of Data Assimilation for Three‐Dimensional MHD Simulation of Solar Wind

2023

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As an important part of space weather forecasting, the prediction of solar wind parameters in the near‐Earth space is particularly significant. The introduction of data assimilation (DA) method can improve the reliability of numerical prediction. In this study, we use a three‐dimensional (3D) magnetohydrodynamics (MHD) numerical model with Kalman filter to infer the impact of the DA on solar wind modeling. We use the 3D MHD numerical model with near‐Earth in situ observations from the OMNI database to reconstruct solar wind parameters between 21.5 solar radii and 1 AU. The period from 2018 to 2021 is simulated, when the solar activity in the decay of the 24th solar cycle to the rising of 25th solar cycle. The numerical model generates two separate results, one without DA and one with DA directly performed on the model‐only results. Statistical analysis of observed, modeled and assimilated solar wind parameters at 1 AU reveals that assimilating simulations provide a more accurate forecast than the model‐only results with a sharp reduction in the root mean square error and an increase of correlation coefficient.

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Domain of Influence Analysis: Implications for Data Assimilation in Space Weather Forecasting

Cited by 4 publications

References 82 publications

Unsupervised classification of simulated magnetospheric regions

Unsupervised classification of simulated magnetospheric regions

Unsupervised classification of simulated magnetospheric regions

Research on the Effect of Data Assimilation for Three‐Dimensional MHD Simulation of Solar Wind

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