The radial evolution of solar wind speeds

The WSA-ENLIL model predicts significant evolution of the solar wind speed. Along a flux tube the solar wind speed at 1.0 AU and beyond is found to be significantly altered from the solar wind speed in the outer corona at 0.1 AU, with most of the change occurring within a few tenths of an AU from the Sun. The evolution of the solar wind speed is most pronounced during solar minimum for solar wind with observed speeds at 1.0 AU between 400 and 500 km/s, while the fastest and slowest solar wind experiences little acceleration or deceleration. Solar wind ionic charge state observations made near 1.0 AU during solar minimum are found to be consistent with a large fraction of the intermediate-speed solar wind having been accelerated or decelerated from slower or faster speeds. This paper sets the groundwork for understanding the evolution of wind speed with distance, which is critical for interpreting the solar wind composition observations near Earth and throughout the inner heliosphere. We show from composition observations that the intermediate-speed solar wind (400-500 km/s) represents a mix of what was originally fast and slow solar wind, which implies a more bimodal solar wind in the corona than observed at 1.0 AU

Ambient solar wind's effect on ICME transit times

Most empirical and numerical models of Interplanetary Coronal Mass Ejection (ICME) propagation use the initial CME velocity as their primary, if not only, observational input. These models generally predict a wide spread of 1 AU transit times for ICMEs with the same initial velocity. We use a 3D coupled MHD model of the corona and heliosphere to determine the ambient solar wind's effect on the propagation of ICMEs from 30 solar radii to 1 AU. We quantitatively characterize this deceleration by the velocity of the upstream ambient solar wind. The effects of varying solar wind parameters on the ICME transit time are quantified and can explain the observed spread in transit times for ICMEs of the same initial velocity. We develop an adjustment formula that can be used in conjunction with other models to reduce the spread in predicted transit times of Earth-directed ICMEs

Understanding shock dynamics in the inner heliosphere with modeling and type II radio data: A statistical study

We study two methods of predicting interplanetary shock location and strength in the inner heliosphere: (1) the ENLIL simulation and (2) the kilometric type II (kmTII) prediction. To evaluate differences in the performance of the first method, we apply two sets of coronal mass ejections (CME) parameters from the cone-model fitting and flux-rope (FR) model fitting as input to the ENLIL model for 16 halo CMEs. The results show that the ENLIL model using the actual CME speeds from FR-fit provided an improved shock arrival time (SAT) prediction. The mean prediction errors for the FR and cone-model inputs are 4.90±5.92 h and 5.48±6.11 h, respectively. A deviation of 100 km s−1 from the actual CME speed has resulted in a SAT error of 3.46 h on average. The simulations show that the shock dynamics in the inner heliosphere agrees with the drag-based model. The shock acceleration can be divided as two phases: a faster deceleration phase within 50 Rs and a slower deceleration phase at distances beyond 50 Rs. The linear-fit deceleration in phase 1 is about 1 order of magnitude larger than that in phase 2. When applying the kmTII method to 14 DH-km CMEs, we found that combining the kmTII method with the ENLIL outputs improved the kmTII prediction. Due to a better modeling of plasma density upstream of shocks and the kmTII location, we are able to provide a more accurate shock time-distance and speed profiles. The mean kmTII prediction error using the ENLIL model density is 6.7±6.4 h; it is 8.4±10.4 h when the average solar wind plasma density is used. Applying the ENLIL density has reduced the mean kmTII prediction error by ∼2 h and the standard deviation by 4.0 h. Especially when we applied the combined approach to two interacting events, the kmTII prediction error was drastically reduced from 29.6 h to −4.9 h in one case and 10.6 h to 4.2 h in the other. Furthermore, the results derived from the kmTII method and the ENLIL simulation, together with white-light data, provide a valuable validation of shock formation location and strength. Such information has important implications for solar energetic particle acceleration.Fil: Xie, H.. NASA. Goddard Space Flight Center; Estados Unidos. Department of Physics. Catholic University of America; Estados UnidosFil: St. Cyr, O.C.. NASA. Goddard Space Flight Center; Estados UnidosFil: Gopalswamy, N.. NASA. Goddard Space Flight Center; Estados UnidosFil: Odstrcil, D.. George Mason University. Department of Computational and Data Sciences; Estados UnidosFil: Cremades Fernandez, Maria Hebe. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Tecnológica Nacional. Facultad Regional de Mendoza; Argentin

From Predicting Solar Activity to Forecasting Space Weather: Practical Examples of Research-to-Operations and Operations-to-Research

The successful transition of research to operations (R2O) and operations to research (O2R) requires, above all, interaction between the two communities. We explore the role that close interaction and ongoing communication played in the successful fielding of three separate developments: an observation platform, a numerical model, and a visualization and specification tool. Additionally, we will examine how these three pieces came together to revolutionize interplanetary coronal mass ejection (ICME) arrival forecasts. A discussion of the importance of education and training in ensuring a positive outcome from R2O activity follows. We describe efforts by the meteorological community to make research results more accessible to forecasters and the applicability of these efforts to the transfer of space-weather research.We end with a forecaster "wish list" for R2O transitions. Ongoing, two-way communication between the research and operations communities is the thread connecting it all.Comment: 18 pages, 3 figures, Solar Physics in pres

Metrics for solar wind prediction models: Comparison of empirical, hybrid, and physics-based schemes with 8 years of L1 observations

Space weather effects on technological systems originate with energy carried from the Sun to the terrestrial environment by the solar wind. In this study, we present results of modeling of solar corona-heliosphere processes to predict solar wind conditions at the L1 Lagrangian point upstream of Earth. In particular we calculate performance metrics for (1) empirical, (2) hybrid empirical/physics-based, and (3) full physics-based coupled corona-heliosphere models over an 8-year period (1995–2002). L1 measurements of the radial solar wind speed are the primary basis for validation of the coronal and heliosphere models studied, though other solar wind parameters are also considered. The models are from the Center for Integrated Space-Weather Modeling (CISM) which has developed a coupled model of the whole Sun-to-Earth system, from the solar photosphere to the terrestrial thermosphere. Simple point-by-point analysis techniques, such as mean-square-error and correlation coefficients, indicate that the empirical coronal-heliosphere model currently gives the best forecast of solar wind speed at 1 AU. A more detailed analysis shows that errors in the physics-based models are predominately the result of small timing offsets to solar wind structures and that the large-scale features of the solar wind are actually well modeled. We suggest that additional “tuning” of the coupling between the coronal and heliosphere models could lead to a significant improvement of their accuracy. Furthermore, we note that the physics-based models accurately capture dynamic effects at solar wind stream interaction regions, such as magnetic field compression, flow deflection, and density buildup, which the empirical scheme cannot

The distribution of solar wind speeds during solar minimum: calibration for numerical solar wind modeling constraints on the source of the slow solar wind

It took the solar polar passage of Ulysses in the early 1990s to establish the global structure of the solar wind speed during solar minimum. However, it remains unclear if the solar wind is composed of two distinct populations of solar wind from different sources (e.g., closed loops which open up to produce the slow solar wind) or if the fast and slow solar wind rely on the superradial expansion of the magnetic field to account for the observed solar wind speed variation. We investigate the solar wind in the inner corona using the Wang-Sheeley-Arge (WSA) coronal model incorporating a new empirical magnetic topology–velocity relationship calibrated for use at 0.1 AU. In this study the empirical solar wind speed relationship was determined by using Helios perihelion observations, along with results from Riley et al. (2003) and Schwadron et al. (2005) as constraints. The new relationship was tested by using it to drive the ENLIL 3-D MHD solar wind model and obtain solar wind parameters at Earth (1.0 AU) and Ulysses (1.4 AU). The improvements in speed, its variability, and the occurrence of high-speed enhancements provide confidence that the new velocity relationship better determines the solar wind speed in the outer corona (0.1 AU). An analysis of this improved velocity field within the WSA model suggests the existence of two distinct mechanisms of the solar wind generation, one for fast and one for slow solar wind, implying that a combination of present theories may be necessary to explain solar wind observations