文章基本信息

标题：Validation for global solar wind prediction using Ulysses comparison: Multiple coronal and heliospheric models installed at the Community Coordinated Modeling Center
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作者：L. K. Jian ; P. J. MacNeice ; M. L. Mays 等
期刊名称：Space Weather
印刷版ISSN：1542-7390
出版年度：2016
卷号：14
期号：8
页码：592-611
DOI：10.1002/2016SW001435
语种：English
出版社：American Geophysical Union
摘要：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 Wang-Sheely-Arge (WSA)-Enlil model, MHD-Around-a-Sphere (MAS)-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 third-party validation from the previous near-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 WSA-Enlil model results vary with three different magnetogram inputs. The MAS-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 scintillation-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.