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391
result(s) for
"Solar activity Forecasting."
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Solar storms
Presents a range of views about the extent and type of damage that could result from solar storms. Proposed warning systems and other protective measures are also addressed.
Book
Forecasting the Time Series of Sunspot Numbers
Forecasting the solar cycle is of great importance for weather prediction and environmental monitoring, and also constitutes a difficult scientific benchmark in nonlinear dynamical modeling. This paper describes the identification of a model and its use in the forecasting the time series comprised of Wolf’s sunspot numbers. A key feature of this procedure is that the original time series is first transformed into a symmetrical space where the dynamics of the solar dynamo are unfolded in a better way, thus improving the model. The nonlinear model obtained is parsimonious and has both deterministic and stochastic parts. Monte Carlo simulation of the whole model produces very consistent results with the deterministic part of the model but allows for the determination of confidence bands. The obtained model was used to predict cycles 24 and 25, although the forecast of the latter is seen as a crude approximation, given the long prediction horizon required. As for the 24th cycle, two estimates were obtained with peaks of 65±16 and of 87±13 units of sunspot numbers. The simulated results suggest that the 24th cycle will be shorter and less active than the preceding one.
Journal Article
Predicting Solar Eruptions
\"An international group of scientists announced they have discovered a new way of predicting when violent magnetic bursts will occur on the surface of the sun.\" (UPI) Learn more about the magnetic bursts and how scientists may be able to predict them.
Newspaper Article
The sun also surprises
The sun is the energy source that drives life on Earth; nevertheless, outside of spacecraft, astronauts are at risk of enduring deadly levels of radiation from solar flares. The Space Environment Service Center in Boulder has numerous customers who can benefit from accurate forecasts of solar activity. It is noted that by heating the upper atmosphere and expanding it, surges of ultraviolet radiation increase drag on satellites, such as SKYLAB which fell from Earth orbit in 1979. In March 1989, the third-largest solar storm ever recorded bore down on the Earth, and near the northern hub of the Earth's magnetic field, at HydroQuebec's James Bay facility, five power lines overloaded, leaving six million Canadians without power. The cause was the nearly unprecedented geomagnetic storm. Emphasis is placed on the career of solar forecaster and researcher Patrick McIntosh, whose weather and sunspot observation project won two consecutive top awards in the Illinois State Science Fair, and a full scholarship to Harvard. Also discussed is the fact that, according to the Babcock model and observations made since its introduction in 1960, all small-scale activity that flashes and sizzles on the sun's surface is the result of two characteristics of the solar atmosphere -- convection and differential rotation -- and their interaction with the sun's magnetic field, which is aligned roughly along the north-south pole, and every 22 years -- or two sunspot cycles -- the polarity reverses. McIntosh has found that the elements of solar activity behave in ways familiar to the earthly observer, with sunspots moving across the surface like a squall line, and a solar Coriolis force giving some of the nastier sunspot regions vorticity: they spin like hurricanes, though many thousands of times larger. It is also noted that the well-known and much-studied Maunder Minimum - a dearth of sunspots that coincided with a cooler climate in Europe from 1645 to 1715 - has shown itself to be not a quirk but one of many regular solar minima revealed by paleoclimatological records such as ice cores and tree rings.
Magazine Article
Earth's deadly future
\"The first things to go will be Earth's glaciers and polar ice caps. Warming surface temperatures will turn ice to water, leading to a slow but steady rise in sea levels. But it doesn't stop there. Eventually, temperatures will rise high enough for seawater to boil away, leaving Earth bereft of this vital substance...Ask most people familiar with astronomy when to expect this coming apocalypse, and you'll hear answers of around 5 billion years--once the Sun swells into a red giant. But the end is nearer than that.\" (Astronomy) This article outlines how changes to the structure of the Sun over the next several billion years are expected to increase its intensity and make Earth uninhabitable for all life.
Magazine Article
Forecasting Solar Flares Using Magnetogram-based Predictors and Machine Learning
We propose a forecasting approach for solar flares based on data from Solar Cycle 24, taken by the
Helioseismic and Magnetic Imager
(HMI) on board the
Solar Dynamics Observatory
(SDO) mission. In particular, we use the Space-weather HMI Active Region Patches (SHARP) product that facilitates cut-out magnetograms of solar active regions (AR) in the Sun in near-realtime (NRT), taken over a five-year interval (2012 – 2016). Our approach utilizes a set of thirteen predictors, which are not included in the SHARP metadata, extracted from line-of-sight and vector photospheric magnetograms. We exploit several machine learning (ML) and conventional statistics techniques to predict flares of peak magnitude
>
M1
and
>
C1
within a 24 h forecast window. The ML methods used are multi-layer perceptrons (MLP), support vector machines (SVM), and random forests (RF). We conclude that random forests could be the prediction technique of choice for our sample, with the second-best method being multi-layer perceptrons, subject to an entropy objective function. A Monte Carlo simulation showed that the best-performing method gives accuracy
ACC
=
0.93
(
0.00
)
, true skill statistic
TSS
=
0.74
(
0.02
)
, and Heidke skill score
HSS
=
0.49
(
0.01
)
for
>
M1
flare prediction with probability threshold 15% and
ACC
=
0.84
(
0.00
)
,
TSS
=
0.60
(
0.01
)
, and
HSS
=
0.59
(
0.01
)
for
>
C1
flare prediction with probability threshold 35%.
Journal Article
Acceleration and Propagation of Solar Energetic Particles
Solar Energetic Particles (SEPs) are an important component of Space Weather, including radiation hazard to humans and electronic equipment, and the ionisation of the Earth’s atmosphere. We review the key observations of SEPs, our current understanding of their acceleration and transport, and discuss how this knowledge is incorporated within Space Weather forecasting tools. Mechanisms for acceleration during solar flares and at shocks driven by Coronal Mass Ejections (CMEs) are discussed, as well as the timing relationships between signatures of solar eruptive events and the detection of SEPs in interplanetary space. Evidence on how the parameters of SEP events are related to those of the parent solar activity is reviewed and transport effects influencing SEP propagation to near-Earth locations are examined. Finally, the approaches to forecasting Space Weather SEP effects are discussed. We conclude that both flare and CME shock acceleration contribute to Space Weather relevant SEP populations and need to be considered within forecasting tools.
Journal Article
Enhancing Solar Cycle 25 and 26 Forecasting with Vipin-Deep-Decomposed-Recomposed Rolling-window (vD2R2w) Model on Sunspot Number Observations
Effective predicting sunspot numbers (SSN) is the complex task of studying space weather, solar activity, satellite communication, and Earth’s climate. Developing a reliable SSN forecasting model is difficult because SSN time series exhibit complex patterns, nonlinearity, and nonstationarity characteristics. The state-of-the-art shows that deep-learning models often need help capturing SSN data’s intricate dynamics and long-term dependencies. The SSN time series’ decomposed trend and seasonal and residual characteristics may provide better information on long-term dependencies and associated dynamics for effective learning. In this research, the vipin-deep-decomposed-recomposed rolling-window (vD2R2w) models have been proposed with a combination of time-series decomposition, deep-learning models, and a rolling-window method to predict the SSN accurately. The proposed vD2R2w models have been evaluated over four datasets and consistently outperform traditional deep-learning models. The model improves the performance in terms of RMSE, MAPE, and
R
2
over the datasets as SSN_Daily: 84.18% (RMSE), 10.38% (MAPE), and 3.504% (
R
2
); SSN_Monthly: 39.5% (RMSE), 26.06% (MAPE), and 7.258% (
R
2
); SSN_MonthlyMean: 178.32% (RMSE), 54.83% (MAPE), and 1.56% (
R
2
); and SSN_Yearly: 6.06% (RMSE), 10.36% (MAPE), and 1.366% (
R
2
). Further, the superiority of the vD2R2w models is validated through AIC & BIC, Diebold Mariano test, and Friedman ranking statistical tests. Additionally, the vD2R2w model has forecasted the peak value of Solar Cycles (SC) and time, i.e., SC25: 127.16 (± 6.83) in 2025 and SC26: 191.71 (± 43.37) in 2035. The analysis of proposed model performances and statistical validation over various measures with four SSNs have concluded that the vD2R2w model outperforms the traditional models and is a reliable framework for SSN time series forecasting. Implementing the proposed model may benefit domains such as space-weather monitoring, satellite communication planning, and solar energy forecasting that rely on accurate SSN predictions.
Journal Article
Global Ionospheric TEC Forecasting for Geomagnetic Storm Time Using a Deep Learning‐Based Multi‐Model Ensemble Method
In recent years, deep learning has been extensively used for ionospheric total electron content (TEC) prediction, and many models can yield promising prediction results, particularly under quiet conditions. Owing to the ionosphere's intricate and dramatic changes during geomagnetic storms, the high‐reliable prediction of the storm‐time ionospheric TEC remains a challenging problem. In this study, we developed a new deep learning‐based multi‐model ensemble method (DLMEM) to forecast geomagnetic storm‐time ionospheric TEC that combines the Random Forest (RF) model, the Extreme Gradient Boosting (XGBoost) algorithm, and the Gated Recurrent Unit (GRU) network with the attention mechanism. Seven features in 170 geomagnetic storm events, including the three components Bx, By and Bz of interplanetary magnetic field (IMF), the Kp and Dst indices of geomagnetic activity data, the F10.7 index of solar activity data and global TEC data, were used for modeling. The test set results showed that the DLMEM model can reduce the root mean square errors (RMSE) by an average of 43.6% in comparison to our previously presented model Ion‐LSTM, especially during the recovery period of geomagnetic storms. Furthermore, compared to Ion‐LSTM, the RMSE values of the low‐, middle‐ and high‐latitude single‐station forecast TEC can be greatly decreased by 33%, 53% and 59%, respectively. It was shown that the new model allows for more precise short‐term global ionospheric forecasting during geomagnetic storms, enabling real‐time monitoring of ionospheric changes.
Journal Article
Comprehensive Analysis of the Geoeffective Solar Event of 21 June 2015: Effects on the Magnetosphere, Plasmasphere, and Ionosphere Systems
A full-halo coronal mass ejection (CME) left the Sun on 21 June 2015 from active region (AR) NOAA 12371. It encountered Earth on 22 June 2015 and generated a strong geomagnetic storm whose minimum Dst value was −204 nT. The CME was associated with an M2-class flare observed at 01:42 UT, located near disk center (N12 E16). Using satellite data from solar, heliospheric, and magnetospheric missions and ground-based instruments, we performed a comprehensive Sun-to-Earth analysis. In particular, we analyzed the active region evolution using ground-based and satellite instruments (Big Bear Solar Observatory (BBSO),
Interface Region Imaging Spectrograph
(IRIS),
Hinode, Atmospheric Imaging Assembly
(AIA) onboard the
Solar Dynamics Observatory
(SDO),
Reuven Ramaty High Energy Solar Spectroscopic Imager
(RHESSI), covering H
α
, EUV, UV, and X-ray data); the AR magnetograms, using data from SDO/
Helioseismic and Magnetic Imager
(HMI); the high-energy particle data, using the
Payload for Antimatter Matter Exploration and Light-nuclei Astrophysics
(PAMELA) instrument; and the Rome neutron monitor measurements to assess the effects of the interplanetary perturbation on cosmic-ray intensity. We also evaluated the 1 – 8 Å soft X-ray data and the
∼
1
MHz type III radio burst time-integrated intensity (or fluence) of the flare in order to predict the associated solar energetic particle (SEP) event using the model developed by Laurenza
et al.
(
Space Weather
7
(4),
2009
). In addition, using ground-based observations from lower to higher latitudes (
International Real-time Magnetic Observatory Network
(INTERMAGNET) and
European Quasi-Meridional Magnetometer Array
(EMMA)), we reconstructed the ionospheric current system associated with the geomagnetic sudden impulse (SI). Furthermore,
Super Dual Auroral Radar Network
(SuperDARN) measurements were used to image the global ionospheric polar convection during the SI and during the principal phases of the geomagnetic storm. In addition, to investigate the influence of the disturbed electric field on the low-latitude ionosphere induced by geomagnetic storms, we focused on the morphology of the crests of the equatorial ionospheric anomaly by the simultaneous use of the
Global Navigation Satellite System
(GNSS) receivers, ionosondes, and Langmuir probes onboard the
Swarm
constellation satellites. Moreover, we investigated the dynamics of the plasmasphere during the different phases of the geomagnetic storm by examining the time evolution of the radial profiles of the equatorial plasma mass density derived from field line resonances detected at the EMMA network (
1.5
<
L
<
6.5
). Finally, we present the general features of the geomagnetic response to the CME by applying innovative data analysis tools that allow us to investigate the time variation of ground-based observations of the Earth’s magnetic field during the associated geomagnetic storm.
Journal Article