Solar Wind Forecast Research Articles

Probabilistic Solar Wind and Geomagnetic Forecasting Using an Analogue Ensemble or \u201cSimilar Day\u201d 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 [B_{mathrm{N}}] 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 B_{mathrm{N}}, 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.

Verification of high‐speed solar wind stream forecasts using operational solar wind models

AbstractHigh‐speed solar wind streams emanating from coronal holes are frequently impinging on the Earth's magnetosphere causing recurrent, medium‐level geomagnetic storm activity. Modeling high‐speed solar wind streams is thus an essential element of successful space weather forecasting. Here we evaluate high‐speed stream forecasts made by the empirical solar wind forecast (ESWF) and the semiempirical Wang‐Sheeley‐Arge (WSA) model based on the in situ plasma measurements from the Advanced Composition Explorer (ACE) spacecraft for the years 2011 to 2014. While the ESWF makes use of an empirical relation between the coronal hole area observed in Solar Dynamics Observatory (SDO)/Atmospheric Imaging Assembly (AIA) images and solar wind properties at the near‐Earth environment, the WSA model establishes a link between properties of the open magnetic field lines extending from the photosphere to the corona and the background solar wind conditions. We found that both solar wind models are capable of predicting the large‐scale features of the observed solar wind speed (root‐mean‐square error, RMSE ≈100 km/s) but tend to either overestimate (ESWF) or underestimate (WSA) the number of high‐speed solar wind streams (threat score, TS ≈ 0.37). The predicted high‐speed streams show typical uncertainties in the arrival time of about 1 day and uncertainties in the speed of about 100 km/s. General advantages and disadvantages of the investigated solar wind models are diagnosed and outlined.

ACTIVE REGION MORPHOLOGIES SELECTED FROM NEAR-SIDE HELIOSEISMIC DATA

We estimate the morphology of near-side active regions using near-side helioseismology. Active regions from two data sets, Air Force Data Assimilative Photospheric flux Transport synchronic maps and Global Oscillation Network Group near-side helioseismic maps, were matched and their morphologies compared. Our algorithm recognizes 382 helioseismic active regions between 2002 April 25 and 2005 December 31 and matches them to their corresponding magnetic active regions with 100% success. A magnetic active region occupies 30% of the area of its helioseismic signature. Recovered helioseismic tilt angles are in good agreement with magnetic tilt angles. Approximately 20% of helioseismic active regions can be decomposed into leading and trailing polarity. Leading polarity components show no discernible scaling relationship, but trailing magnetic polarity components occupy approximately 25% of the area of the trailing helioseismic component. A nearside phase-magnetic calibration is in close agreement with a previous far-side helioseismic calibration and provides confidence that these morphological relationships can be used with far-side helioseismic data. Including far-side active region morphology in synchronic maps will have implications for coronal magnetic topology predictions and solar wind forecasts.

A 27 day persistence model of near‐Earth solar wind conditions: A long lead‐time forecast and a benchmark for dynamical models

Geomagnetic activity has long been known to exhibit approximately 27 day periodicity, resulting from solar wind structures repeating each solar rotation. Thus a very simple near‐Earth solar wind forecast is 27 day persistence, wherein the near‐Earth solar wind conditions today are assumed to be identical to those 27 days previously. Effective use of such a persistence model as a forecast tool, however, requires the performance and uncertainty to be fully characterized. The first half of this study determines which solar wind parameters can be reliably forecast by persistence and how the forecast skill varies with the solar cycle. The second half of the study shows how persistence can provide a useful benchmark for more sophisticated forecast schemes, namely physics‐based numerical models. Point‐by‐point assessment methods, such as correlation and mean‐square error, find persistence skill comparable to numerical models during solar minimum, despite the 27 day lead time of persistence forecasts, versus 2–5 days for numerical schemes. At solar maximum, however, the dynamic nature of the corona means 27 day persistence is no longer a good approximation and skill scores suggest persistence is out‐performed by numerical models for almost all solar wind parameters. But point‐by‐point assessment techniques are not always a reliable indicator of usefulness as a forecast tool. An event‐based assessment method, which focusses key solar wind structures, finds persistence to be the most valuable forecast throughout the solar cycle. This reiterates the fact that the means of assessing the “best” forecast model must be specifically tailored to its intended use.

An hybrid neuro-wavelet approach for long-term prediction of solar wind

AbstractNowadays the interest for space weather and solar wind forecasting is increasing to become a main relevance problem especially for telecommunication industry, military, and for scientific research. At present the goal for weather forecasting reach the ultimate high ground of the cosmos where the environment can affect the technological instrumentation. Some interests then rise about the correct prediction of space events, like ionized turbulence in the ionosphere or impacts from the energetic particles in the Van Allen belts, then of the intensity and features of the solar wind and magnetospheric response. The problem of data prediction can be faced using hybrid computation methods so as wavelet decomposition and recurrent neural networks (RNNs). Wavelet analysis was used in order to reduce the data redundancies so obtaining representation which can express their intrinsic structure. The main advantage of the wavelet use is the ability to pack the energy of a signal, and in turn the relevant carried informations, in few significant uncoupled coefficients. Neural networks (NNs) are a promising technique to exploit the complexity of non-linear data correlation. To obtain a correct prediction of solar wind an RNN was designed starting on the data series. As reported in literature, because of the temporal memory of the data an Adaptative Amplitude Real Time Recurrent Learning algorithm was used for a full connected RNN with temporal delays. The inputs for the RNN were given by the set of coefficients coming from the biorthogonal wavelet decomposition of the solar wind velocity time series. The experimental data were collected during the NASA mission WIND. It is a spin stabilized spacecraft launched in 1994 in a halo orbit around the L1 point. The data are provided by the SWE, a subsystem of the main craft designed to measure the flux of thermal protons and positive ions.

Exploiting solar wind time series correlation with magnetospheric response by using an hybrid neuro-wavelet approach

AbstractThe studies about the Sun rise a strong interest regarding modifications caused by the solar activity on the Earth. For almost a century in literature was discussed the problem of forecasting and analysis of the space weather, which in his definition covers both the near-earth space and the biospheric affection due to the environmental interaction with the Sun. In particular in the last years increased the attention for magnetospheric response in conjunction with the technological infrastructure and the biosphere itself. This to prevent i.e. spacecraft failures or possible treats to human health. Since the main effect of the activity of the Sun is the solar wind, rises the aim to found a correlation between itself and the localized variations induced on the magnetosphere being the purpose to predict long-term variation of the magnetic field from solar wind time series. As recently proposed for solar wind forecasting, an hybrid approach will be here used than joining the wavelet analysis with the prediction capabilities of recurrent neural networks with an adaptive amplitude activation function algorithm in order to avoid the need to standardize or rescaling the input signal and to match the exact range of the activation function.

Solar Wind Sources in the Late Declining Phase of Cycle 23: Effects of the Weak Solar Polar Field on High Speed Streams

The declining phases of solar cycles are known for their high speed solar wind streams that dominate the geomagnetic responses during this period. Outstanding questions about these streams, which can provide the fastest winds of the solar cycle, concern their solar origins, persistence, and predictability. The declining phase of cycle 23 has lasted significantly longer than the corresponding phases of the previous two cycles. Solar magnetograph observations suggest that the solar polar magnetic field is also ∼ 2 – 3 times weaker. The launch of STEREO in late 2006 provided additional incentive to examine the origins of what is observed at 1 AU in the recent cycle, with the OMNI data base at the NSSDC available as an Earth/L1 baseline for comparisons. Here we focus on the year 2007 when the solar corona exhibited large, long-lived mid-to-low latitude coronal holes and polar hole extensions observed by both SOHO and STEREO imagers. STEREO provides in situ measurements consistent with rigidly corotating solar wind stream structure at up to ∼ 45° heliolongitude separation by late 2007. This stability justifies the use of magnetogram-based steady 3D solar wind models to map the observed high speed winds back to their coronal sources. We apply the WSA solar wind model currently running at the NOAA Space Weather Prediction Center with the expectation that it should perform its best at this quiet time. The model comparisons confirm the origins of the observed high speed streams expected from the solar images, but also reveal uncertainties in the solar wind source mapping associated with this cycle’s weaker solar polar fields. Overall, the results illustrate the importance of having accurate polar fields in synoptic maps used in solar wind forecast models. At the most fundamental level, they demonstrate the control of the solar polar fields over the high speed wind sources, and thus one specific connection between the solar dynamo and the solar wind character.

Real-time solar wind forecasting: Capabilities and challenges

A user-friendly, real-time, observation-driven system for forecasting solar wind and interplanetary magnetic field conditions is described. The forecast system presently uses the Hakamada–Akasofu–Fry (version 2) kinematic solar wind model to predict, in real-time, solar wind conditions in the heliosphere, including at the location of Mars, and beyond. Properly characterizing and predicting this region of the space environment are essential steps towards improving the accuracy of “downstream” space weather models used to forecast the space radiation environment and geomagnetic activity. Representative modeling results are presented and the conclusion is made that uncertainty in determining the physical parameters needed for model inputs from real-time solar observations is the biggest factor limiting the accuracy of solar wind models used for space weather analysis and prediction. Future directions include extending the forecast system via a hybrid approach to include 3D MHD modeling.