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