Assessing the quality of models of the ambient solar wind

In this paper we present an assessment of the status of models of the global Solar Wind in the inner heliosphere. We limit our discussion to the class of models designed to provide solar wind forecasts, excluding those designed for the purpose of testing physical processes in idealized configurations. In addition, we limit our discussion to modeling of the ‘ambient’ wind in the absence of coronal mass ejections. In this assessment we cover use of the models both in forecast mode and as tools for scientific research. We present a brief history of the development of these models, discussing the range of physical approximations in use. We discuss the limitations of the data inputs available to these models and its impact on their quality. We also discuss current model development trends

Probabilistic solar wind and geomagnetic forecasting using an analogue ensemble or "Similar Day" approach

Effective space-weather prediction and mitigation requires accurate forecasting of near-Earth solar-wind conditions. Numerical magnetohydrodynamic models of the solar wind, driven by remote solar observations, are gaining skill at forecasting the large-scale solar-wind features that give rise to near-Earth variations over days and weeks. There remains a need for accurate short-term (hours to days) solar-wind forecasts, however. In this study we investigate the analogue ensemble (AnEn), or “similar day”, approach that was developed for atmospheric weather forecasting. The central premise of the AnEn is that past variations that are analogous or similar to current conditions can be used to provide a good estimate of future variations. By considering an ensemble of past analogues, the AnEn forecast is inherently probabilistic and provides a measure of the forecast uncertainty. We show that forecasts of solar-wind speed can be improved by considering both speed and density when determining past analogues, whereas forecasts of the out-of-ecliptic magnetic field [ BNBN ] are improved by also considering the in-ecliptic magnetic-field components. In general, the best forecasts are found by considering only the previous 6 – 12 hours of observations. Using these parameters, the AnEn provides a valuable probabilistic forecast for solar-wind speed, density, and in-ecliptic magnetic field over lead times from a few hours to around four days. For BNBN , which is central to space-weather disturbance, the AnEn only provides a valuable forecast out to around six to seven hours. As the inherent predictability of this parameter is low, this is still likely a marked improvement over other forecast methods. We also investigate the use of the AnEn in forecasting geomagnetic indices Dst and Kp. The AnEn provides a valuable probabilistic forecast of both indices out to around four days. We outline a number of future improvements to AnEn forecasts of near-Earth solar-wind and geomagnetic conditions

Using gradient boosting regression to improve ambient solar wind model predictions

Studying the ambient solar wind, a continuous pressure‐driven plasma flow emanating from our Sun, is an important component of space weather research. The ambient solar wind flows in interplanetary space determine how solar storms evolve through the heliosphere before reaching Earth, and especially during solar minimum are themselves a driver of activity in the Earth’s magnetic field. Accurately forecasting the ambient solar wind flow is therefore imperative to space weather awareness. Here we present a machine learning approach in which solutions from magnetic models of the solar corona are used to output the solar wind conditions near the Earth. The results are compared to observations and existing models in a comprehensive validation analysis, and the new model outperforms existing models in almost all measures. In addition, this approach offers a new perspective to discuss the role of different input data to ambient solar wind modeling, and what this tells us about the underlying physical processes. The final model discussed here represents an extremely fast, well‐validated and open‐source approach to the forecasting of ambient solar wind at Earth

Twentyâ four hour predictions of the solar wind speed peaks by the probability distribution function model

Abrupt transitions from slow to fast solar wind represent a concern for the space weather forecasting community. They may cause geomagnetic storms that can eventually affect systems in orbit and on the ground. Therefore, the probability distribution function (PDF) model was improved to predict enhancements in the solar wind speed. New probability distribution functions allow for the prediction of the peak amplitude and the time to the peak while providing an interval of uncertainty on the prediction. It was found that 60% of the positive predictions were correct, while 91% of the negative predictions were correct, and 20% to 33% of the peaks in the speed were found by the model. This represents a considerable improvement upon the first version of the PDF model. A direct comparison with the Wangâ Sheeleyâ Arge model shows that the PDF model is quite similar, except that it leads to fewer false positive predictions and misses fewer events, especially when the peak reaches very high speeds.Key PointsConfusion matrices were calculated to assess the ability of the new PDF model to predict highâ speed eventsAmong the positive predictions, 60.4% are correct, 91.4% of the negative predictions are correct, and 20.3% of the peaks in the speed are foundEnsemble predictions of highâ speed events by the PDF model provide the forecast community with an interval of uncertainty on the predictionPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134779/1/swe20366.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/134779/2/swe20366_am.pd

Small Satellite Mission Concepts for Space Weather Research and as Pathfinders for Operations

International audienceRecent advances in miniaturization and commercial availability of critical satellite subsystems and detector technology have made small satellites (SmallSats, including CubeSats) an attractive, low-cost potential solution for space weather research and operational needs. Motivated by the first International Workshop on SmallSats for Space Weather Research and Forecasting, held in Washington, DC on 1-4 August 2017, we discuss the need for advanced space weather measurement capabilities, driven by analyses from the World Meteorological Organization (WMO), and how SmallSats can efficiently fill these measurement gaps. We present some current, recent missions and proposed/upcoming mission concepts using SmallSats that enhance space weather research and provide prototyping pathways for future operational applications; how they relate to the WMO requirements; and what challenges remain to be overcome to meet the WMO goals and operational needs in the future. With additional investment from cognizant funding agencies worldwide, SmallSats—including standalone missions and constellations—could significantly enhance space weather research and, eventually, operations, by reducing costs and enabling new measurements not feasible from traditional, large, monolithic missions

Addressing Gaps in Space Weather Operations and Understanding With Small Satellites

Gaps in space weather observations that can be addressed with small satellites are identified. Potential improvements in solar inputs to space weather models, space radiation control, estimations of energy budget of the upper Earth’s atmosphere, and satellite drag modeling are briefly discussed. Key observables, instruments, and observation strategies by small satellites are recommended. Tracking optimization for small satellites is proposed.Key PointsEnhancing space weather operations and understanding with small satellites are discussedKey observables and small satellite strategies are recommendedPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/167058/1/swe21089_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/167058/2/swe21089.pd

Addressing Gaps in Space Weather Operations and Understanding With Small Satellites

Gaps in space weather observations that can be addressed with small satellites are identified. Potential improvements in solar inputs to space weather models, space radiation control, estimations of energy budget of the upper Earth’s atmosphere, and satellite drag modeling are briefly discussed. Key observables, instruments, and observation strategies by small satellites are recommended. Tracking optimization for small satellites is proposed.Key PointsEnhancing space weather operations and understanding with small satellites are discussedKey observables and small satellite strategies are recommendedPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/167058/1/swe21089_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/167058/2/swe21089.pd

Predictions of the solar wind speed by the probability distribution function model

The near‐Earth space environment is strongly driven by the solar wind and interplanetary magnetic field. This study presents a model for predicting the solar wind speed up to 5 days in advance. Probability distribution functions (PDFs) were created that relate the current solar wind speed and slope to the future solar wind speed, as well as the solar wind speed to the solar wind speed one solar rotation in the future. It was found that a major limitation of this type of technique is that the solar wind periodicity is close to 27 days but can be from about 22 to 32 days. Further, the optimum lag between two solar rotations can change from day to day, making a prediction of the future solar wind speed based solely on the solar wind speed approximately 27 days ago quite difficult. It was found that using a linear combination of the solar wind speed one solar rotation ago and a prediction of the solar wind speed based on the current speed and slope is optimal. The linear weights change as a function of the prediction horizon, with shorter prediction times putting more weight on the prediction based on the current solar wind speed and the longer prediction times based on an even spread between the two. For all prediction horizons from 8 h up to 120 h, the PDF Model is shown to be better than using the current solar wind speed (i.e., persistence), and better than the Wang‐Sheeley‐Arge Model for prediction horizons of 24 h. Key Points Solar wind speed prediction up to 5 days Probability distribution functions of the solar wind velocity Periodicity of the solar wind velocity related to the rotation of the SunPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/108057/1/swe20148.pd