Validation for solar wind prediction at Earth: Comparison of coronal and heliospheric models installed at the CCMC

Multiple coronal and heliospheric models have been recently upgraded at the Community Coordinated Modeling Center (CCMC), including the Wang‐Sheeley‐Arge (WSA)‐Enlil model, MHD‐Around‐a‐Sphere (MAS)‐Enlil model, Space Weather Modeling Framework (SWMF), and heliospheric tomography using interplanetary scintillation data. To investigate the effects of photospheric magnetograms from different sources, different coronal models, and different model versions on the model performance, we run these models in 10 combinations. Choosing seven Carrington rotations in 2007 as the time window, we compare the modeling results with the Operating Mission as Nodes on the Internet data for near‐Earth space environment during the late declining phase of solar cycle 23. Visual comparison is proved to be a necessary addition to the quantitative assessment of the models' capabilities in reproducing the time series and statistics of solar wind parameters. The MAS‐Enlil model captures the time patterns of solar wind parameters better, while the WSA‐Enlil model matches with the time series of normalized solar wind parameters better. Models generally overestimate slow wind temperature and underestimate fast wind temperature and magnetic field. Using improved algorithms, we have identified magnetic field sector boundaries (SBs) and slow‐to‐fast stream interaction regions (SIRs) as focused structures. The success rate of capturing them and the time offset vary largely with models. For this quiet period, the new version of MAS‐Enlil model works best for SBs, while heliospheric tomography works best for SIRs. The new version of SWMF with more physics added needs more development. General strengths and weaknesses for each model are diagnosed to provide an unbiased reference to model developers and users.Key PointsPerformance metrics for solar wind simulation are developedTen model combinations are validated with strengths and weaknesses diagnosedStream interactions are captured 30–80% of the time and by 0.5–2.5 days offPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/111912/1/swe20222.pd

Validation for global solar wind prediction using Ulysses comparison: Multiple coronal and heliospheric models installed at the Community Coordinated Modeling Center

The prediction of the background global solar wind is a necessary part of space weather forecasting. Several coronal and heliospheric models have been installed and/or recently upgraded at the Community Coordinated Modeling Center (CCMC), including the Wangâ Sheelyâ Arge (WSA)â Enlil model, MHDâ Aroundâ aâ Sphere (MAS)â Enlil model, Space Weather Modeling Framework (SWMF), and heliospheric tomography using interplanetary scintillation data. Ulysses recorded the last fast latitudinal scan from southern to northern poles in 2007. By comparing the modeling results with Ulysses observations over seven Carrington rotations, we have extended our thirdâ party validation from the previous nearâ Earth solar wind to middle to high latitudes, in the same late declining phase of solar cycle 23. Besides visual comparison, we have quantitatively assessed the models’ capabilities in reproducing the time series, statistics, and latitudinal variations of solar wind parameters for a specific range of model parameter settings, inputs, and grid configurations available at CCMC. The WSAâ Enlil model results vary with three different magnetogram inputs. The MASâ Enlil model captures the solar wind parameters well, despite its underestimation of the speed at middle to high latitudes. The new version of SWMF misses many solar wind variations probably because it uses lower grid resolution than other models. The interplanetary scintillationâ tomography cannot capture the latitudinal variations of solar wind well yet. Because the model performance varies with parameter settings which are optimized for different epochs or flow states, the performance metric study provided here can serve as a template that researchers can use to validate the models for the time periods and conditions of interest to them.Key PointsPerformance metrics for the global solar wind prediction are developedEight model combinations are validated with strengths and weaknesses identified for each modelEffect of model internal parameter settings and magnetogram input is discussedPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134247/1/swe20343_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/134247/2/swe20343.pd

Role of periodic loading‐unloading in the magnetotail versus interplanetary magnetic field B z flipping in the ring current buildup

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/94813/1/jgra19183.pd

Multiscale modeling of magnetospheric reconnection

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/95438/1/jgra18824.pd

Buildup of the ring current during periodic loading‐unloading cycles in the magnetotail driven by steady southward interplanetary magnetic field

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/95239/1/jgra18825.pd

Magnetic Flux of EUV Arcade and Dimming Regions as a Relevant Parameter for Early Diagnostics of Solar Eruptions - Sources of Non-Recurrent Geomagnetic Storms and Forbush Decreases

This study aims at the early diagnostics of geoeffectiveness of coronal mass ejections (CMEs) from quantitative parameters of the accompanying EUV dimming and arcade events. We study events of the 23th solar cycle, in which major non-recurrent geomagnetic storms (GMS) with Dst <-100 nT are sufficiently reliably identified with their solar sources in the central part of the disk. Using the SOHO/EIT 195 A images and MDI magnetograms, we select significant dimming and arcade areas and calculate summarized unsigned magnetic fluxes in these regions at the photospheric level. The high relevance of this eruption parameter is displayed by its pronounced correlation with the Forbush decrease (FD) magnitude, which, unlike GMSs, does not depend on the sign of the Bz component but is determined by global characteristics of ICMEs. Correlations with the same magnetic flux in the solar source region are found for the GMS intensity (at the first step, without taking into account factors determining the Bz component near the Earth), as well as for the temporal intervals between the solar eruptions and the GMS onset and peak times. The larger the magnetic flux, the stronger the FD and GMS intensities are and the shorter the ICME transit time is. The revealed correlations indicate that the main quantitative characteristics of major non-recurrent space weather disturbances are largely determined by measurable parameters of solar eruptions, in particular, by the magnetic flux in dimming areas and arcades, and can be tentatively estimated in advance with a lead time from 1 to 4 days. For GMS intensity, the revealed dependencies allow one to estimate a possible value, which can be expected if the Bz component is negative.Comment: 27 pages, 5 figures. Accepted for publication in Solar Physic

Review of solar energetic particle models

Solar Energetic Particle (SEP) events are interesting from a scientific perspective as they are the product of a broad set of physical processes from the corona out through the extent of the heliosphere, and provide insight into processes of particle acceleration and transport that are widely applicable in astrophysics. From the operations perspective, SEP events pose a radiation hazard for aviation, electronics in space, and human space exploration, in particular for missions outside of the Earth’s protective magnetosphere including to the Moon and Mars. Thus, it is critical to improve the scientific understanding of SEP events and use this understanding to develop and improve SEP forecasting capabilities to support operations. Many SEP models exist or are in development using a wide variety of approaches and with differing goals. These include computationally intensive physics-based models, fast and light empirical models, machine learning-based models, and mixed-model approaches. The aim of this paper is to summarize all of the SEP models currently developed in the scientific community, including a description of model approach, inputs and outputs, free parameters, and any published validations or comparisons with data.</p