KUL_OpenGGCM

The 50 simulations included in this ensemble are global magnetospheric simulations done with the OpenGGCM model, whose mathematical formulation is described in Raeder, 2003. The reference simulation uses as Sunwards boundary conditions the observed solar wind values at 1 AU starting from May 8th, 2004, 09:00 UTC onwards. The 50 ensemble simulations are generated by perturbing the component of the input solar wind velocity vx, as in

formula

with vxavg the average value over the sampled period. The different solar wind ram pressure alters the internal magnetospheric dynamics in the different simulations of the ensemble.

In Millas et al, 2020, we use this ensemble for Representer Analysis (RA) and Domain of Influence (DoI) analysis, using the simulations as a proxy for the magnetospheric system. RA and DoI analysis are powerful statistical tools used to estimate the effectiveness of Data Assimilation techniques when applied to a specific model, without assimilating actual data. Intuitively, large absolute values of the DOI, calculated with respect to an observation point, means that observations at that location would provide significant information of that field in the specific, large |DOI| area, but less so in areas with lower |DOI|. This analysis can be used in several ways. For example, it allows us to optimize assimilation strategies, it may uncover model biases that can then be addressed by further model development, and it can be used to optimize the observation systems that provide operational data for Data Analysis.

In the video, we observe consistently low values of the DoI in the plasma sheet, on a scale from -1 to 1, since the region is highly dynamics. These results advocate for monitoring of the plasma sheet via constellations of satellites.

Evolution of the Domain of Influence in the OpenGGCM ensemble, with monitor point in the plasma sheet (star)

References:

Millas, D., Innocenti, M. E., Laperre, B., Raeder, J., Poedts, S., & Lapenta, G. (2020). Domain of influence analysis: Implications for data assimilation in space weather forecasting. Frontiers in Astronomy and Space Sciences, 7, 73. https://www.frontiersin.org/articles/10.3389/fspas.2020.571286/full https://arxiv.org/pdf/2009.04211.pdf

Raeder, J. (2003). “Global magnetohydrodynamics–a tutorial,” in Space Plasma Simulation, eds J. Büchner, C. T. Dum, and M. Scholer (Berlin; Heidelberg; New York, NY: Springer Verlag), 212–246. doi: 10.1007/3-540-36530-3_11