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Register a new userEnsemble Forecasting of Major Solar Flares: Methods for Combining Models
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One essential component of operational space weather forecasting is the prediction of solar flares. With a multitude of flare forecasting methods now available online it is still unclear which of these methods performs best, and none are substantially better than climatological forecasts. Space weather researchers are increasingly looking towards methods used by the terrestrial weather community to improve current forecasting techniques. Ensemble forecasting has been used in numerical weather prediction for many years as a way to combine different predictions in order to obtain a more accurate result. Here we construct ensemble forecasts for major solar flares by linearly combining the full-disk probabilistic forecasts from a group of operational forecasting methods (ASAP, ASSA, MAG4, MOSWOC, NOAA, and MCSTAT). Forecasts from each method are weighted by a factor that accounts for the methods ability to predict previous events, and several performance metrics (both probabilistic and categorical) are considered. It is found that most ensembles achieve a better skill metric (between 5% and 15%) than any of the members alone. Moreover, over 90% of ensembles perform better (as measured by forecast attributes) than a simple equal-weights average. Finally, ensemble uncertainties are highly dependent on the internal metric being optimized and they are estimated to be less than 20% for probabilities greater than 0.2. This simple multi-model, linear ensemble technique can provide operational space weather centres with the basis for constructing a versatile ensemble forecasting system -- an improved starting point to their forecasts that can be tailored to different end-user needs.
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Little is known about the origin of the high-energy and sustained emission from solar Long-Duration Gamma-Ray Flares (LDGRFs), identified with the Compton Gamma Ray Observatory (CGRO), the Solar Maximum Mission (SMM), and now Fermi. Though Fermi/Large Area Space Telescope (LAT) has identified dozens of flares with LDGRF signature, the nature of this phenomenon has been a challenge to explain both due to the extreme energies and long durations. The highest-energy emission has generally been attributed to pion production from the interaction of >300 MeV protons with the ambient matter. The extended duration suggests that particle acceleration occurs over large volumes extending high in the corona, either from stochastic acceleration within large coronal loops or from back precipitation from coronal mass ejection driven shocks. It is possible to test these models by making direct comparison between the properties of the accelerated ion population producing the gamma-ray emission derived from the Fermi/LAT observations, and the characteristics of solar energetic particles (SEPs) measured by the Payload for Matter-Antimatter Exploration and Light Nuclei Astrophysics (PAMELA) spacecraft in the energy range corresponding to the pion-related emission detected with Fermi. For fourteen of these events we compare the two populations -- SEPs in space and the interacting particles at the Sun -- and discuss the implications in terms of potential sources. Our analysis shows that the two proton numbers are poorly correlated, with their ratio spanning more than five orders of magnitude, suggesting that the back precipitation of shock-acceleration particles is unlikely the source of the LDGRF emission.
Space weather indices are commonly used to drive operational forecasts of various geospace systems, including the thermosphere for mass density and satellite drag. The drivers serve as proxies for various processes that cause energy flow and deposition in the geospace system. Forecasts of neutral mass density is a major uncertainty in operational orbit prediction and collision avoidance for objects in low earth orbit (LEO). For the strongly driven system, accuracy of space weather driver forecasts is crucial for operations. The High Accuracy Satellite Drag Model (HASDM) currently employed by the United States Air Force in an operational environment is driven by four (4) solar and two (2) geomagnetic proxies. Space Environment Technologies (SET) is contracted by the space command to provide forecasts for the drivers. This work performs a comprehensive assessment for the performance of the driver forecast models. The goal is to provide a benchmark for future improvements of the forecast models. Using an archived data set spanning six (6) years and 15,000 forecasts across solar cycle 24, we quantify the temporal statistics of the model performance.
The ambient solar wind flows and fields influence the complex propagation dynamics of coronal mass ejections in the interplanetary medium and play an essential role in shaping Earths space weather environment. A critical scientific goal in the space weather research and prediction community is to develop, implement and optimize numerical models for specifying the large-scale properties of solar wind conditions at the inner boundary of the heliospheric model domain. Here we present an adaptive prediction system that fuses information from in situ measurements of the solar wind into numerical models to better match the global solar wind model solutions near the Sun with prevailing physical conditions in the vicinity of Earth. In this way, we attempt to advance the predictive capabilities of well-established solar wind models for specifying solar wind speed, including the Wang-Sheeley-Arge (WSA) model. In particular, we use the Heliospheric Upwind eXtrapolation (HUX) model for mapping the solar wind solutions from the near-Sun environment to the vicinity of Earth. In addition, we present the newly developed Tunable HUX (THUX) model which solves the viscous form of the underlying Burgers equation. We perform a statistical analysis of the resulting solar wind predictions for the time 2006-2015. The proposed prediction scheme improves all the investigated coronal/heliospheric model combinations and produces better estimates of the solar wind state at Earth than our reference baseline model. We discuss why this is the case, and conclude that our findings have important implications for future practice in applied space weather research and prediction.
Solar activity, ranging from the background solar wind to energetic coronal mass ejections (CMEs), is the main driver of the conditions in the interplanetary space and in the terrestrial space environment, known as space weather. A better understanding of the Sun-Earth connection carries enormous potential to mitigate negative space weather effects with economic and social benefits. Effective space weather forecasting relies on data and models. In this paper, we discuss some of the most used space weather models, and propose suitable locations for data gathering with space weather purposes. We report on the application of textit{Representer analysis (RA)} and textit{Domain of Influence (DOI) analysis} to three models simulating different stages of the Sun-Earth connection: the OpenGGCM and Tsyganenko models, focusing on solar wind - magnetosphere interaction, and the PLUTO model, used to simulate CME propagation in interplanetary space. Our analysis is promising for space weather purposes for several reasons. First, we obtain quantitative information about the most useful locations of observation points, such as solar wind monitors. For example, we find that the absolute values of the DOI are extremely low in the magnetospheric plasma sheet. Since knowledge of that particular sub-system is crucial for space weather, enhanced monitoring of the region would be most beneficial. Second, we are able to better characterize the models. Although the current analysis focuses on spatial rather than temporal correlations, we find that time-independent models are less useful for Data Assimilation activities than time-dependent models. Third, we take the first steps towards the ambitious goal of identifying the most relevant heliospheric parameters for modelling CME propagation in the heliosphere, their arrival time, and their geoeffectiveness at Earth.
We simulate decaying turbulence in a homogeneous pair plasma using three dimensional electromagnetic particle-in-cell (PIC) method. A uniform background magnetic field permeates the plasma such that the magnetic pressure is three times larger than the thermal pressure and the turbulence is generated by counter-propagating shear Alfven waves. The energy predominately cascades transverse to the background magnetic field, rendering the turbulence anisotropic at smaller scales. We simultaneously move several ion species of varying charge to mass ratios in our simulation and show that the particles of smaller charge to mass ratios are heated and accelerated to non-thermal energies at a faster rate, in accordance with the enhancement of heavy ions and non-thermal tail in their energy spectrum observed in the impulsive solar flares. We further show that the heavy ions are energized mostly in the direction perpendicular to the background magnetic field with a rate consistent with our analytical estimate of the rate of heating due to cyclotron resonance with the Alfven waves of which a large fraction is due to obliquely propagating waves.
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