Influences of misprediction costs on solar flare prediction

NASA Astrophysics Data System (ADS)

Huang, Xin; Wang, HuaNing; Dai, XingHua

2012-10-01

The mispredictive costs of flaring and non-flaring samples are different for different applications of solar flare prediction. Hence, solar flare prediction is considered a cost sensitive problem. A cost sensitive solar flare prediction model is built by modifying the basic decision tree algorithm. Inconsistency rate with the exhaustive search strategy is used to determine the optimal combination of magnetic field parameters in an active region. These selected parameters are applied as the inputs of the solar flare prediction model. The performance of the cost sensitive solar flare prediction model is evaluated for the different thresholds of solar flares. It is found that more flaring samples are correctly predicted and more non-flaring samples are wrongly predicted with the increase of the cost for wrongly predicting flaring samples as non-flaring samples, and the larger cost of wrongly predicting flaring samples as non-flaring samples is required for the higher threshold of solar flares. This can be considered as the guide line for choosing proper cost to meet the requirements in different applications.

Solar-Terrestrial Predictions

NASA Astrophysics Data System (ADS)

Thompson, R. J.; Cole, D. G.; Wilkinson, P. J.; Shea, M. A.; Smart, D.

1990-11-01

Volume 1: The following subject areas are covered: the magnetosphere environment; forecasting magnetically quiet periods; radiation hazards to human in deep space (a summary with special reference to large solar particle events); solar proton events (review and status); problems of the physics of solar-terrestrial interactions; prediction of solar proton fluxes from x-ray signatures; rhythms in solar activity and the prediction of episodes of large flares; the role of persistence in the 24-hour flare forecast; on the relationship between the observed sunspot number and the number of solar flares; the latitudinal distribution of coronal holes and geomagnetic storms due to coronal holes; and the signatures of flares in the interplanetary medium at 1 AU. Volume 2: The following subject areas were covered: a probability forecast for geomagnetic activity; cost recovery in solar-terrestrial predictions; magnetospheric specification and forecasting models; a geomagnetic forecast and monitoring system for power system operation; some aspects of predicting magnetospheric storms; some similarities in ionospheric disturbance characteristics in equatorial, mid-latitude, and sub-auroral regions; ionospheric support for low-VHF radio transmission; a new approach to prediction of ionospheric storms; a comparison of the total electron content of the ionosphere around L=4 at low sunspot numbers with the IRI model; the French ionospheric radio propagation predictions; behavior of the F2 layer at mid-latitudes; and the design of modern ionosondes.

Flare Prediction Using Photospheric and Coronal Image Data

NASA Astrophysics Data System (ADS)

Jonas, E.; Shankar, V.; Bobra, M.; Recht, B.

2016-12-01

We attempt to forecast M-and X-class solar flares using a machine-learning algorithm and five years of image data from both the Helioseismic and Magnetic Imager (HMI) and Atmospheric Imaging Assembly (AIA) instruments aboard the Solar Dynamics Observatory. HMI is the first instrument to continuously map the full-disk photospheric vector magnetic field from space (Schou et al., 2012). The AIA instrument maps the transition region and corona using various ultraviolet wavelengths (Lemen et al., 2012). HMI and AIA data are taken nearly simultaneously, providing an opportunity to study the entire solar atmosphere at a rapid cadence. Most flare forecasting efforts described in the literature use some parameterization of solar data - typically of the photospheric magnetic field within active regions. These numbers are considered to capture the information in any given image relevant to predicting solar flares. In our approach, we use HMI and AIA images of solar active regions and a deep convolutional kernel network to predict solar flares. This is effectively a series of shallow-but-wide random convolutional neural networks stacked and then trained with a large-scale block-weighted least squares solver. This algorithm automatically determines which patterns in the image data are most correlated with flaring activity and then uses these patterns to predict solar flares. Using the recently-developed KeystoneML machine learning framework, we construct a pipeline to process millions of images in a few hours on commodity cloud computing infrastructure. This is the first time vector magnetic field images have been combined with coronal imagery to forecast solar flares. This is also the first time such a large dataset of solar images, some 8.5 terabytes of images that together capture over 3000 active regions, has been used to forecast solar flares. We evaluate our method using various flare prediction windows defined in the literature (e.g. Ahmed et al., 2013) and a novel per-hour time series we've constructed which more closely mimics the demands of an operational solar flare prediction system. We estimate the performance of our algorithm using the True Skill Statistic (TSS; Bloomfield et al., 2012). We find that our algorithm gives a high TSS score and predictive abilities.

Predicting Solar Activity Using Machine-Learning Methods

NASA Astrophysics Data System (ADS)

Bobra, M.

2017-12-01

Of all the activity observed on the Sun, two of the most energetic events are flares and coronal mass ejections. However, we do not, as of yet, fully understand the physical mechanism that triggers solar eruptions. A machine-learning algorithm, which is favorable in cases where the amount of data is large, is one way to [1] empirically determine the signatures of this mechanism in solar image data and [2] use them to predict solar activity. In this talk, we discuss the application of various machine learning algorithms - specifically, a Support Vector Machine, a sparse linear regression (Lasso), and Convolutional Neural Network - to image data from the photosphere, chromosphere, transition region, and corona taken by instruments aboard the Solar Dynamics Observatory in order to predict solar activity on a variety of time scales. Such an approach may be useful since, at the present time, there are no physical models of flares available for real-time prediction. We discuss our results (Bobra and Couvidat, 2015; Bobra and Ilonidis, 2016; Jonas et al., 2017) as well as other attempts to predict flares using machine-learning (e.g. Ahmed et al., 2013; Nishizuka et al. 2017) and compare these results with the more traditional techniques used by the NOAA Space Weather Prediction Center (Crown, 2012). We also discuss some of the challenges in using machine-learning algorithms for space science applications.

Recent Progress of Solar Weather Forecasting at Naoc

NASA Astrophysics Data System (ADS)

He, Han; Wang, Huaning; Du, Zhanle; Zhang, Liyun; Huang, Xin; Yan, Yan; Fan, Yuliang; Zhu, Xiaoshuai; Guo, Xiaobo; Dai, Xinghua

The history of solar weather forecasting services at National Astronomical Observatories, Chinese Academy of Sciences (NAOC) can be traced back to 1960s. Nowadays, NAOC is the headquarters of the Regional Warning Center of China (RWC-China), which is one of the members of the International Space Environment Service (ISES). NAOC is responsible for exchanging data, information and space weather forecasts of RWC-China with other RWCs. The solar weather forecasting services at NAOC cover short-term prediction (within two or three days), medium-term prediction (within several weeks), and long-term prediction (in time scale of solar cycle) of solar activities. Most efforts of the short-term prediction research are concentrated on the solar eruptive phenomena, such as flares, coronal mass ejections (CMEs) and solar proton events, which are the key driving sources of strong space weather disturbances. Based on the high quality observation data of the latest space-based and ground-based solar telescopes and with the help of artificial intelligence techniques, new numerical models with quantitative analyses and physical consideration are being developed for the predictions of solar eruptive events. The 3-D computer simulation technology is being introduced for the operational solar weather service platform to visualize the monitoring of solar activities, the running of the prediction models, as well as the presenting of the forecasting results. A new generation operational solar weather monitoring and forecasting system is expected to be constructed in the near future at NAOC.

PREDICTION OF SOLAR FLARES USING UNIQUE SIGNATURES OF MAGNETIC FIELD IMAGES

DOE Office of Scientific and Technical Information (OSTI.GOV)

Raboonik, Abbas; Safari, Hossein; Alipour, Nasibe

Prediction of solar flares is an important task in solar physics. The occurrence of solar flares is highly dependent on the structure and topology of solar magnetic fields. A new method for predicting large (M- and X-class) flares is presented, which uses machine learning methods applied to the Zernike moments (ZM) of magnetograms observed by the Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory for a period of six years from 2010 June 2 to 2016 August 1. Magnetic field images consisting of the radial component of the magnetic field are converted to finite sets of ZMs andmore » fed to the support vector machine classifier. ZMs have the capability to elicit unique features from any 2D image, which may allow more accurate classification. The results indicate whether an arbitrary active region has the potential to produce at least one large flare. We show that the majority of large flares can be predicted within 48 hr before their occurrence, with only 10 false negatives out of 385 flaring active region magnetograms and 21 false positives out of 179 non-flaring active region magnetograms. Our method may provide a useful tool for the prediction of solar flares, which can be employed alongside other forecasting methods.« less

A physical mechanism for the prediction of the sunspot number during solar cycle 21. [graphs (charts)

NASA Technical Reports Server (NTRS)

Schatten, K. H.; Scherrer, P. H.; Svalgaard, L.; Wilcox, J. M.

1978-01-01

On physical grounds it is suggested that the sun's polar field strength near a solar minimum is closely related to the following cycle's solar activity. Four methods of estimating the sun's polar magnetic field strength near solar minimum are employed to provide an estimate of cycle 21's yearly mean sunspot number at solar maximum of 140 plus or minus 20. This estimate is considered to be a first order attempt to predict the cycle's activity using one parameter of physical importance.

Long term solar activity and ionospheric prediction services rendered by the National Physical Laboratory, New Delhi

NASA Technical Reports Server (NTRS)

Reddy, B. M.; Aggarwal, S.; Lakshmi, D. R.; Shastri, S.; Mitra, A. P.

1979-01-01

The data base used in solar and ionospheric prediction services is described. Present prediction techniques are discussed and compared with actual observations. Future prediction techniques using computers are also discussed.

Atmospheric drag model calibrations for spacecraft lifetime prediction

NASA Technical Reports Server (NTRS)

Binebrink, A. L.; Radomski, M. S.; Samii, M. V.

1989-01-01

Although solar activity prediction uncertainty normally dominates decay prediction error budget for near-Earth spacecraft, the effect of drag force modeling errors for given levels of solar activity needs to be considered. Two atmospheric density models, the modified Harris-Priester model and the Jacchia-Roberts model, to reproduce the decay histories of the Solar Mesosphere Explorer (SME) and Solar Maximum Mission (SMM) spacecraft in the 490- to 540-kilometer altitude range were analyzed. Historical solar activity data were used in the input to the density computations. For each spacecraft and atmospheric model, a drag scaling adjustment factor was determined for a high-solar-activity year, such that the observed annual decay in the mean semimajor axis was reproduced by an averaged variation-of-parameters (VOP) orbit propagation. The SME (SMM) calibration was performed using calendar year 1983 (1982). The resulting calibration factors differ by 20 to 40 percent from the predictions of the prelaunch ballistic coefficients. The orbit propagations for each spacecraft were extended to the middle of 1988 using the calibrated drag models. For the Jaccia-Roberts density model, the observed decay in the mean semimajor axis of SME (SMM) over the 4.5-year (5.5-year) predictive period was reproduced to within 1.5 (4.4) percent. The corresponding figure for the Harris-Priester model was 8.6 (20.6) percent. Detailed results and conclusions regarding the importance of accurate drag force modeling for lifetime predictions are presented.

Solar-terrestrial Predictions Proceedings. Volume 1: Prediction Group Reports

NASA Technical Reports Server (NTRS)

Donnelly, R. F. (Editor)

1979-01-01

The current practice in solar terrestrial predictions is reviewed with emphasis of prediction, warning, and monitoring services. Topics covered include: ionosphere-reflected HF radio propagation; radiation hazards for manned space flights and high altitude and high latitude aircraft flights; and geomagnetic activity.

Comparison of ionospheric F2 peak parameters foF2 and hmF2 with IRI2001 at Hainan

NASA Astrophysics Data System (ADS)

Wang, X.; Shi, J. K.; Wang, G. J.; Gong, Y.

2009-06-01

Monthly median values of foF2, hmF2 and M(3000)F2 parameters, with quarter-hourly time interval resolution for the diurnal variation, obtained with DPS4 digisonde at Hainan (19.5°N, 109.1°E; Geomagnetic coordinates: 178.95°E, 8.1°N) are used to investigate the low-latitude ionospheric variations and comparisons with the International Reference Ionosphere (IRI) model predictions. The data used for the present study covers the period from February 2002 to April 2007, which is characterized by a wide range of solar activity, ranging from high solar activity (2002) to low solar activity (2007). The results show that (1) Generally, IRI predictions follow well the diurnal and seasonal variation patterns of the experimental values of foF2, especially in the summer of 2002. However, there are systematic deviation between experimental values and IRI predictions with either CCIR or URSI coefficients. Generally IRI model greatly underestimate the values of foF2 from about noon to sunrise of next day, especially in the afternoon, and slightly overestimate them from sunrise to about noon. It seems that there are bigger deviations between IRI Model predictions and the experimental observations for the moderate solar activity. (2) Generally the IRI-predicted hmF2 values using CCIR M(3000)F2 option shows a poor agreement with the experimental results, but there is a relatively good agreement in summer at low solar activity. The deviation between the IRI-predicted hmF2 using CCIR M(3000)F2 and observed hmF2 is bigger from noon to sunset and around sunrise especially at high solar activity. The occurrence time of hmF2 peak (about 1200 LT) of the IRI model predictions is earlier than that of observations (around 1500 LT). The agreement between the IRI hmF2 obtained with the measured M(3000)F2 and the observed hmF2 is very good except that IRI overestimates slightly hmF2 in the daytime in summer at high solar activity and underestimates it in the nighttime with lower values near sunrise at low solar activity.

Predicting space climate change

NASA Astrophysics Data System (ADS)

Balcerak, Ernie

2011-10-01

Galactic cosmic rays and solar energetic particles can be hazardous to humans in space, damage spacecraft and satellites, pose threats to aircraft electronics, and expose aircrew and passengers to radiation. A new study shows that these threats are likely to increase in coming years as the Sun approaches the end of the period of high solar activity known as “grand solar maximum,” which has persisted through the past several decades. High solar activity can help protect the Earth by repelling incoming galactic cosmic rays. Understanding the past record can help scientists predict future conditions. Barnard et al. analyzed a 9300-year record of galactic cosmic ray and solar activity based on cosmogenic isotopes in ice cores as well as on neutron monitor data. They used this to predict future variations in galactic cosmic ray flux, near-Earth interplanetary magnetic field, sunspot number, and probability of large solar energetic particle events. The researchers found that the risk of space weather radiation events will likely increase noticeably over the next century compared with recent decades and that lower solar activity will lead to increased galactic cosmic ray levels. (Geophysical Research Letters, doi:10.1029/2011GL048489, 2011)

The role of predicted solar activity in TOPEX/Poseidon orbit maintenance maneuver design

NASA Technical Reports Server (NTRS)

Frauenholz, Raymond B.; Shapiro, Bruce E.

1992-01-01

Following launch in June 1992, the TOPEX/Poseidon satellite will be placed in a near-circular frozen orbit at an altitude of about 1336 km. Orbit maintenance maneuvers are planned to assure all nodes of the 127-orbit 10-day repeat ground track remain within a 2 km equatorial longitude bandwidth. Orbit determination, maneuver execution, and atmospheric drag prediction errors limit overall targeting performance. This paper focuses on the effects of drag modeling errors, with primary emphasis on the role of SESC solar activity predictions, especially the 27-day outlook of the 10.7 cm solar flux and geomagnetic index used by a simplified version of the Jacchia-Roberts density model developed for this TOPEX/Poseidon application. For data evaluated from 1983-90, the SESC outlook performed better than a simpler persistence strategy, especially during the first 7-10 days. A targeting example illustrates the use of ground track biasing to compensate for expected orbit predictions errors, emphasizing the role of solar activity prediction errors.

Solar Cycle 24 and the Solar Dynamo

NASA Technical Reports Server (NTRS)

Pesnell, W. D.; Schatten, K.

2007-01-01

We will discuss the polar field precursor method for solar activity prediction, which predicts cycle 24 will be significantly lower than recent activity cycles, and some new ideas rejuvenating Babcock's shallow surface dynamo. The polar field precursor method is based on Babcock and Leighton's dynamo models wherein the polar field at solar minimum plays a major role in generating the next cycle's toroidal field and sunspots. Thus, by examining the polar fields of the Sun near solar minimum, a forecast for the next cycle's activity is obtained. With the current low value for the Sun's polar fields, this method predicts solar cycle 24 will be one of the lowest in recent times, with smoothed F10.7 radio flux values peaking near 135 plus or minus 35 (2 sigma), in the 2012-2013 timeframe (equivalent to smoothed Rz near 80 plus or minus 35 [2 sigma]). One may have to consider solar activity as far back as the early 20th century to find a cycle of comparable magnitude. We discuss unusual behavior in the Sun's polar fields that support this prediction. Normally, the solar precursor method is consistent with the geomagnetic precursor method, wherein geomagnetic variations are thought to be a good measure of the Sun's polar field strength. Because of the unusual polar field, the Earth does not appear to be currently bathed in the Sun's extended polar field (the interplanetary field), hence negating the primal cause behind the geomagnetic precursor technique. We also discuss how percolation may support Babcock's original shallow solar dynamo. In this process ephemeral regions from the solar magnetic carpet, guided by shallow surface fields, may collect to form pores and sunspots.

Solar Cycle #24 and the Solar Dynamo

NASA Technical Reports Server (NTRS)

Schatten, Kenneth; Pesnell, W. Dean

2007-01-01

We focus on two solar aspects related to flight dynamics. These are the solar dynamo and long-term solar activity predictions. The nature of the solar dynamo is central to solar activity predictions, and these predictions are important for orbital planning of satellites in low earth orbit (LEO). The reason is that the solar ultraviolet (UV) and extreme ultraviolet (EUV) spectral irradiances inflate the upper atmospheric layers of the Earth, forming the thermosphere and exosphere through which these satellites orbit. Concerning the dynamo, we discuss some recent novel approaches towards its understanding. For solar predictions we concentrate on a solar precursor method, in which the Sun's polar field plays a major role in forecasting the next cycle s activity based upon the Babcock-Leighton dynamo. With a current low value for the Sun s polar field, this method predicts that solar cycle #24 will be one of the lowest in recent times, with smoothed F10.7 radio flux values peaking near 130 plus or minus 30 (2 sigma), in the 2013 timeframe. One may have to consider solar activity as far back as the early 20th century to find a cycle of comparable magnitude. Concomitant effects of low solar activity upon satellites in LEO will need to be considered, such as enhancements in orbital debris. Support for our prediction of a low solar cycle #24 is borne out by the lack of new cycle sunspots at least through the first half of 2007. Usually at the present epoch in the solar cycle (approx. 7+ years after the last solar maximum), for a normal size following cycle, new cycle sunspots would be seen. The lack of their appearance at this time is only consistent with a low cycle #24. Polar field observations of a weak magnitude are consistent with unusual structures seen in the Sun s corona. Polar coronal holes are the hallmarks of the Sun's open field structures. At present, it appears that the polar coronal holes are relatively weak, and there have been many equatorial coronal holes. This appears consistent with a weakening polar field, but coronal hole data must be scrutinized carefully as observing techniques have changed. We also discuss new solar dynamo ideas, and the SODA (SOlar Dynamo Amplitude) index, which provides the user with the ability to track the Sun's hidden, dynamo magnetic fields throughout the various stages of the Sun's cycle. Our solar dynamo ideas are a modernization and rejuvenation of the Babcock-Leighton original idea of a shallow solar dynamo, using modern observations that appear to support their shallow dynamo viewpoint. We are in awe of being able to see an object the size of the Sun undergoing as dramatic a change as our model provides in a few short years. The Sun, however, has undergone changes as rapid as this before! The weather on the Sun is at least as fickle as the weather on the Earth.

Solar Cycle #24 and the Solar Dynamo

NASA Technical Reports Server (NTRS)

Pesnell, W. Dean; Schatten, Kenneth

2007-01-01

We focus on two solar aspects related to flight dynamics. These are the solar dynamo and long-term solar activity predictions. The nature of the solar dynamo is central to solar activity predictions, and these predictions are important for orbital planning of satellites in low earth orbit (LEO). The reason is that the solar ultraviolet (UV) and extreme ultraviolet (EUV) spectral irradiances inflate the upper atmospheric layers of the Earth, forming the thermosphere and exosphere through which these satellites orbit. Concerning the dynamo, we discuss some recent novel approaches towards its understanding. For solar predictions we concentrate on a solar precursor method, in which the Sun s polar field plays a major role in forecasting the next cycle s activity based upon the Babcock- Leighton dynamo. With a current low value for the Sun s polar field, this method predicts that solar cycle #24 will be one of the lowest in recent times, with smoothed F10.7 radio flux values peaking near 130+ 30 (2 4, in the 2013 timeframe. One may have to consider solar activity as far back as the early 20th century to find a cycle of comparable magnitude. Concomitant effects of low solar activity upon satellites in LEO will need to be considered, such as enhancements in orbital debris. Support for our prediction of a low solar cycle #24 is borne out by the lack of new cycle sunspots at least through the first half of 2007. Usually at the present epoch in the solar cycle (-7+ years after the last solar maximum), for a normal size following cycle, new cycle sunspots would be seen. The lack of their appearance at this time is only consistent with a low cycle #24. Polar field observations of a weak magnitude are consistent with unusual structures seen in the Sun s corona. Polar coronal holes are the hallmarks of the Sun s open field structures. At present, it appears that the polar coronal holes are relatively weak, and there have been many equatorial coronal holes. This appears consistent with a weakening polar field, but coronal hole data must be scrutinized carefully as observing techniques have changed. We also discuss new solar dynamo ideas, and the SODA (Solar Dynamo Amplitude) index, which provides the user with the ability to track the Sun s hidden, dynamo magnetic fields throughout the various stages of the Sun s cycle. Our solar dynamo ideas are a modernization and rejuvenation of the Babcock-Leighton original idea of a shallow solar dynamo, using modem observations that appear to support their shallow dynamo viewpoint. We are in awe of being able to see an object the size of the Sun undergoing as dramatic a change as our model provides in a few short years. The Sun, however, has undergone changes as rapid as this before! The weather on the Sun is at least as fickle as the weather on the Earth.

An Automated Solar Synoptic Analysis Software System

NASA Astrophysics Data System (ADS)

Hong, S.; Lee, S.; Oh, S.; Kim, J.; Lee, J.; Kim, Y.; Lee, J.; Moon, Y.; Lee, D.

2012-12-01

We have developed an automated software system of identifying solar active regions, filament channels, and coronal holes, those are three major solar sources causing the space weather. Space weather forecasters of NOAA Space Weather Prediction Center produce the solar synoptic drawings as a daily basis to predict solar activities, i.e., solar flares, filament eruptions, high speed solar wind streams, and co-rotating interaction regions as well as their possible effects to the Earth. As an attempt to emulate this process with a fully automated and consistent way, we developed a software system named ASSA(Automated Solar Synoptic Analysis). When identifying solar active regions, ASSA uses high-resolution SDO HMI intensitygram and magnetogram as inputs and providing McIntosh classification and Mt. Wilson magnetic classification of each active region by applying appropriate image processing techniques such as thresholding, morphology extraction, and region growing. At the same time, it also extracts morphological and physical properties of active regions in a quantitative way for the short-term prediction of flares and CMEs. When identifying filament channels and coronal holes, images of global H-alpha network and SDO AIA 193 are used for morphological identification and also SDO HMI magnetograms for quantitative verification. The output results of ASSA are routinely checked and validated against NOAA's daily SRS(Solar Region Summary) and UCOHO(URSIgram code for coronal hole information). A couple of preliminary scientific results are to be presented using available output results. ASSA will be deployed at the Korean Space Weather Center and serve its customers in an operational status by the end of 2012.

Actividad solar del ciclo 23. Predicción del máximo y fase decreciente utilizando redes neuronales

NASA Astrophysics Data System (ADS)

Parodi, M. A.; Ceccatto, H. A.; Piacentini, R. D.; García, P. J.

Different methods have been proposed in order to predict the maximum amplitude of solar cycles, either as a consequence of the intrinsic importance of this event and because of its relation with solar storms and possible effects upon satellites, communication systems, etc. In this work, a neural network solar activity prediction is presented, measured through the sunspot number (SSN). The 16-units neural network, with a 12:3:1 architecture, was trained in a ``feed-forward" propagation way and learning by the so called ``back propagation rule". The annual mean SSN data in the 1700-1975 and 1987-1998 periods were used as the training set. The solar cycle 21 (1976-1986) was taken as the cross-validation data set. After performing the network training we obtained a prediction of the maximum annual mean for the current solar cycle 23, SSNmax= 135 ±17 at the year 2000, which is 13% smaller than the International Consensus Commitee's mean maximum prediction obtained through ``precursor techniques". On the other hand, our prediction is only about 4% smaller than the Consensus's neural network mean prediction. A ``multiple step" prediction technique was also performed and SSN annual mean predicted values for the near-maximum (from the present year 1999 to beyond the maximum) and the declining activity of solar cycle 23 are presented in this work. The sensibility of predictions is also tested. To do so, we changed the interval width and comparated our results with those of a previous neural network prediction and those of others authors using differents methods.

Solar cycle predicts folate-sensitive neonatal genotypes at discrete phases of the first trimester of pregnancy: a novel folate-related human embryo loss hypothesis.

PubMed

Lucock, Mark; Glanville, Tracey; Yates, Zoë; Walker, James; Furst, John; Simpson, Nigel

2012-08-01

Folate, a key periconceptional nutrient, is ultraviolet light (UV-R) sensitive. We therefore hypothesise that a relationship exists between sunspot activity, a proxy for total solar irradiance (particularly UV-R) reaching Earth, and the occurrence of folate-sensitive, epigenomic-related neonatal genotypes during the first trimester of pregnancy. Limited data is provided to support the hypothesis that the solar cycle predicts folate-related human embryo loss: 379 neonates born at latitude 54°N between 1998 and 2000 were examined for three folate-sensitive, epigenome-related polymorphisms, with solar activity for trimester one accessed via the Royal Greenwich Observatory-US Air force/National Oceanic and Atmospheric Administration Sunspot Database (34,110 total observation days). Logistic regression showed solar activity predicts C677T-methylenetetrahydrofolate reductase (C677T-MTHFR) and A66G-methionine synthase reductase (A66G-MSR) genotype at discrete phases of trimester one. Total and maximal sunspot activity predicts C677T-MTHFR genotype for days 31-60 of trimester one (p=0.0181 and 0.0366, respectively) and A66G-MSR genotype for days 61-90 of trimester one (p=0.0072 and 0.0105, respectively). Loss of UV-R sensitive folate associated with the sunspot cycle might therefore interact with variant folate genes to perturb DNA methylation and/or elaboration of the primary base sequence (thymidylate synthesis), as well as increase embryo-toxic homocysteine. We hypothesise that this may influence embryo viability leading to 677CC-MTHFR and 66GG-MSR embryo loss at times of increased solar activity. This provides an interesting and plausible link between well recognised 'folate gene originated developmental disorders' and 'solar activity/seasonality modulated developmental disorders'. Copyright © 2012 Elsevier Ltd. All rights reserved.

DATA ASSIMILATION APPROACH FOR FORECAST OF SOLAR ACTIVITY CYCLES

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kitiashvili, Irina N., E-mail: irina.n.kitiashvili@nasa.gov

Numerous attempts to predict future solar cycles are mostly based on empirical relations derived from observations of previous cycles, and they yield a wide range of predicted strengths and durations of the cycles. Results obtained with current dynamo models also deviate strongly from each other, thus raising questions about criteria to quantify the reliability of such predictions. The primary difficulties in modeling future solar activity are shortcomings of both the dynamo models and observations that do not allow us to determine the current and past states of the global solar magnetic structure and its dynamics. Data assimilation is a relativelymore » new approach to develop physics-based predictions and estimate their uncertainties in situations where the physical properties of a system are not well-known. This paper presents an application of the ensemble Kalman filter method for modeling and prediction of solar cycles through use of a low-order nonlinear dynamo model that includes the essential physics and can describe general properties of the sunspot cycles. Despite the simplicity of this model, the data assimilation approach provides reasonable estimates for the strengths of future solar cycles. In particular, the prediction of Cycle 24 calculated and published in 2008 is so far holding up quite well. In this paper, I will present my first attempt to predict Cycle 25 using the data assimilation approach, and discuss the uncertainties of that prediction.« less

High solar activity predictions through an artificial neural network

NASA Astrophysics Data System (ADS)

Orozco-Del-Castillo, M. G.; Ortiz-Alemán, J. C.; Couder-Castañeda, C.; Hernández-Gómez, J. J.; Solís-Santomé, A.

The effects of high-energy particles coming from the Sun on human health as well as in the integrity of outer space electronics make the prediction of periods of high solar activity (HSA) a task of significant importance. Since periodicities in solar indexes have been identified, long-term predictions can be achieved. In this paper, we present a method based on an artificial neural network to find a pattern in some harmonics which represent such periodicities. We used data from 1973 to 2010 to train the neural network, and different historical data for its validation. We also used the neural network along with a statistical analysis of its performance with known data to predict periods of HSA with different confidence intervals according to the three-sigma rule associated with solar cycles 24-26, which we found to occur before 2040.

Predicting the Where and the How Big of Solar Flares

NASA Astrophysics Data System (ADS)

Barnes, Graham; Leka, K. D.; Gilchrist, Stuart

2017-08-01

The approach to predicting solar flares generally characterizes global properties of a solar active region, for example the total magnetic flux or the total length of a sheared magnetic neutral line, and compares new data (from which to make a prediction) to similar observations of active regions and their associated propensity for flare production. We take here a different tack, examining solar active regions in the context of their energy storage capacity. Specifically, we characterize not the region as a whole, but summarize the energy-release prospects of different sub-regions within, using a sub-area analysis of the photospheric boundary, the CFIT non-linear force-free extrapolation code, and the Minimum Current Corona model. We present here early results from this approach whose objective is to understand the different pathways available for regions to release stored energy, thus eventually providing better estimates of the where (what sub-areas are storing how much energy) and the how big (how much energy is stored, and how much is available for release) of solar flares.

A Solar Cycle Prediction Puzzle's PossibleExplanation?

NASA Astrophysics Data System (ADS)

Luhmann, Janet

2007-05-01

A long-standing and intriguing puzzle of the last few decades has been Joan Feynman's (1982) discovery that the solar cycle (sunspot number) maximum trends follow the level of geomagnetic activity during the prior minimum phase. Recently Hathaway (GRL 33, 2006) used this relationship to make a prediction of the size of the next solar maximum. But the physical reason why this should work at all remains a matter of speculation. Although it has been suggested that geomagnetic activity around solar minimum is determined by the terrestrial magnetosphere's response to high speed solar wind streams which seem to often characterize the declining phase of the cycle, why should the occurrence of these streams portend the new solar maximum? Our improving understanding of solar wind sources may hold the key, and also tell us something useful about the solar dynamo.

Sun Series program for the REEDA System. [predicting orbital lifetime using sunspot values

NASA Technical Reports Server (NTRS)

Shankle, R. W.

1980-01-01

Modifications made to data bases and to four programs in a series of computer programs (Sun Series) which run on the REEDA HP minicomputer system to aid NASA's solar activity predictions used in orbital life time predictions are described. These programs utilize various mathematical smoothing technique and perform statistical and graphical analysis of various solar activity data bases residing on the REEDA System.

The Complexity of Solar and Geomagnetic Indices

NASA Astrophysics Data System (ADS)

Pesnell, W. Dean

2017-08-01

How far in advance can the sunspot number be predicted with any degree of confidence? Solar cycle predictions are needed to plan long-term space missions. Fleets of satellites circle the Earth collecting science data, protecting astronauts, and relaying information. All of these satellites are sensitive at some level to solar cycle effects. Statistical and timeseries analyses of the sunspot number are often used to predict solar activity. These methods have not been completely successful as the solar dynamo changes over time and one cycle's sunspots are not a faithful predictor of the next cycle's activity. In some ways, using these techniques is similar to asking whether the stock market can be predicted. It has been shown that the Dow Jones Industrial Average (DJIA) can be more accurately predicted during periods when it obeys certain statistical properties than at other times. The Hurst exponent is one such way to partition the data. Another measure of the complexity of a timeseries is the fractal dimension. We can use these measures of complexity to compare the sunspot number with other solar and geomagnetic indices. Our concentration is on how trends are removed by the various techniques, either internally or externally. Comparisons of the statistical properties of the various solar indices may guide us in understanding how the dynamo manifests in the various indices and the Sun.

Solar Activity Across the Scales: From Small-Scale Quiet-Sun Dynamics to Magnetic Activity Cycles

NASA Technical Reports Server (NTRS)

Kitiashvili, Irina N.; Collins, Nancy N.; Kosovichev, Alexander G.; Mansour, Nagi N.; Wray, Alan A.

2017-01-01

Observations as well as numerical and theoretical models show that solar dynamics is characterized by complicated interactions and energy exchanges among different temporal and spatial scales. It reveals magnetic self-organization processes from the smallest scale magnetized vortex tubes to the global activity variation known as the solar cycle. To understand these multiscale processes and their relationships, we use a two-fold approach: 1) realistic 3D radiative MHD simulations of local dynamics together with high resolution observations by IRIS, Hinode, and SDO; and 2) modeling of solar activity cycles by using simplified MHD dynamo models and mathematical data assimilation techniques. We present recent results of this approach, including the interpretation of observational results from NASA heliophysics missions and predictive capabilities. In particular, we discuss the links between small-scale dynamo processes in the convection zone and atmospheric dynamics, as well as an early prediction of Solar Cycle 25.

Solar activity across the scales: from small-scale quiet-Sun dynamics to magnetic activity cycles

NASA Astrophysics Data System (ADS)

Kitiashvili, I.; Collins, N.; Kosovichev, A. G.; Mansour, N. N.; Wray, A. A.

2017-12-01

Observations as well as numerical and theoretical models show that solar dynamics is characterized by complicated interactions and energy exchanges among different temporal and spatial scales. It reveals magnetic self-organization processes from the smallest scale magnetized vortex tubes to the global activity variation known as the solar cycle. To understand these multiscale processes and their relationships, we use a two-fold approach: 1) realistic 3D radiative MHD simulations of local dynamics together with high-resolution observations by IRIS, Hinode, and SDO; and 2) modeling of solar activity cycles by using simplified MHD dynamo models and mathematical data assimilation techniques. We present recent results of this approach, including the interpretation of observational results from NASA heliophysics missions and predictive capabilities. In particular, we discuss the links between small-scale dynamo processes in the convection zone and atmospheric dynamics, as well as an early prediction of Solar Cycle 25.

Earth Science

NASA Image and Video Library

1990-10-24

Solar Vector Magnetograph is used to predict solar flares, and other activities associated with sun spots. This research provides new understanding about weather on the Earth, and solar-related conditions in orbit.

Changes of Linearity in MF2 Index with R12 and Solar Activity Maximum

NASA Astrophysics Data System (ADS)

Villanueva, L.

2013-05-01

Critical frequency of F2 layer is related to the solar activity, and the sunspot number has been the standard index for ionospheric prediction programs. This layer, being considered the most important in HF radio communications due to its highest electron density, determines the maximum frequency coming back from ground base transmitter signals, and shows irregular variation in time and space. Nowadays the spatial variation, better understood due to the availability of TEC measurements, let Space Weather Centers have observations almost in real time. However, it is still the most difficult layer to predict in time. Short time variations are improved in IRI model, but long term predictions are only related to the well-known CCIR and URSI coefficients and Solar activity R12 predictions, (or ionospheric indexes in regional models). The concept of the "saturation" of the ionosphere is based on data observations around 3 solar cycles before 1970, (NBS, 1968). There is a linear relationship among MUF (0Km) and R12, for smooth Sunspot numbers R12 less than 100, but constant for higher R12, so, no rise of MUF is expected for R12 higher than 100. This recommendation has been used in most of the known Ionospheric prediction programs for HF Radio communication. In this work, observations of smoothed ionospheric index MF2 related to R12 are presented to find common features of the linear relationship, which is found to persist in different ranges of R12 depending on the specific maximum level of each solar cycle. In the analysis of individual solar cycles, the lapse of linearity is less than 100 for a low solar cycle and higher than 100 for a high solar cycle. To improve ionospheric predictions we can establish levels for solar cycle maximum sunspot numbers R12 around low 100, medium 150 and high 200 and specify the ranges of linearity of MUF(0Km) related to R12 which is not only 100 as assumed for all the solar cycles. For lower levels of solar cycle, discussions of present observations are presented.

Nonlinear techniques for forecasting solar activity directly from its time series

NASA Technical Reports Server (NTRS)

Ashrafi, S.; Roszman, L.; Cooley, J.

1992-01-01

Numerical techniques for constructing nonlinear predictive models to forecast solar flux directly from its time series are presented. This approach makes it possible to extract dynamical invariants of our system without reference to any underlying solar physics. We consider the dynamical evolution of solar activity in a reconstructed phase space that captures the attractor (strange), given a procedure for constructing a predictor of future solar activity, and discuss extraction of dynamical invariants such as Lyapunov exponents and attractor dimension.

Nonlinear techniques for forecasting solar activity directly from its time series

NASA Technical Reports Server (NTRS)

Ashrafi, S.; Roszman, L.; Cooley, J.

1993-01-01

This paper presents numerical techniques for constructing nonlinear predictive models to forecast solar flux directly from its time series. This approach makes it possible to extract dynamical in variants of our system without reference to any underlying solar physics. We consider the dynamical evolution of solar activity in a reconstructed phase space that captures the attractor (strange), give a procedure for constructing a predictor of future solar activity, and discuss extraction of dynamical invariants such as Lyapunov exponents and attractor dimension.

Assessment of Predictive Capabilities of L1 Orbiters using Realtime Solar Wind Data

NASA Astrophysics Data System (ADS)

Holmes, J.; Kasper, J. C.; Welling, D. T.

2017-12-01

Realtime measurements of solar wind conditions at L1 point allow us to predict geomagnetic activity at Earth up to an hour in advance. These predictions are quantified in the form of geomagnetic indices such as Kp and Ap, allowing for a concise, standardized prediction and measurement system. For years, the Space Weather Prediction Center used ACE realtime solar wind data to develop its one and four-hour Kp forecasts, but has in the past year switched to using DSCOVR data as its source. In this study, the performance of both orbiters in predicting Kp over the course of one month was assessed in an attempt to determine whether or not switching to DSCOVR data has resulted in improved forecasts. The period of study was chosen to encompass a time when the satellites were close to each other, and when moderate to high activity was observed. Kp predictions were made using the Geospace Model, part of the Space Weather Modeling Framework, to simulate conditions based on observed solar wind parameters. The performance of each satellite was assessed by comparing the model output to observed data.

Dynamo-based scheme for forecasting the magnitude of solar activity cycles

NASA Technical Reports Server (NTRS)

Layden, A. C.; Fox, P. A.; Howard, J. M.; Sarajedini, A.; Schatten, K. H.

1991-01-01

This paper presents a general framework for forecasting the smoothed maximum level of solar activity in a given cycle, based on a simple understanding of the solar dynamo. This type of forecasting requires knowledge of the sun's polar magnetic field strength at the preceding activity minimum. Because direct measurements of this quantity are difficult to obtain, the quality of a number of proxy indicators already used by other authors is evaluated, which are physically related to the sun's polar field. These indicators are subjected to a rigorous statistical analysis, and the analysis technique for each indicator is specified in detail in order to simplify and systematize reanalysis for future use. It is found that several of these proxies are in fact poorly correlated or uncorrelated with solar activity, and thus are of little value for predicting activity maxima. Also presented is a scheme in which the predictions of the individual proxies are combined via an appropriately weighted mean to produce a compound prediction. The scheme is then applied to the current cycle 22, and a maximum smoothed international sunspot number of 171 + or - 26 is estimated.

SOLAR CYCLE PROPAGATION, MEMORY, AND PREDICTION: INSIGHTS FROM A CENTURY OF MAGNETIC PROXIES

DOE Office of Scientific and Technical Information (OSTI.GOV)

Munoz-Jaramillo, Andres; DeLuca, Edward E.; Dasi-Espuig, Maria

The solar cycle and its associated magnetic activity are the main drivers behind changes in the interplanetary environment and Earth's upper atmosphere (commonly referred to as space weather). These changes have a direct impact on the lifetime of space-based assets and can create hazards to astronauts in space. In recent years there has been an effort to develop accurate solar cycle predictions (with aims at predicting the long-term evolution of space weather), leading to nearly a hundred widely spread predictions for the amplitude of solar cycle 24. A major contributor to the disagreement is the lack of direct long-term databasesmore » covering different components of the solar magnetic field (toroidal versus poloidal). Here, we use sunspot area and polar faculae measurements spanning a full century (as our toroidal and poloidal field proxies) to study solar cycle propagation, memory, and prediction. Our results substantiate predictions based on the polar magnetic fields, whereas we find sunspot area to be uncorrelated with cycle amplitude unless multiplied by area-weighted average tilt. This suggests that the joint assimilation of tilt and sunspot area is a better choice (with aims to cycle prediction) than sunspot area alone, and adds to the evidence in favor of active region emergence and decay as the main mechanism of poloidal field generation (i.e., the Babcock-Leighton mechanism). Finally, by looking at the correlation between our poloidal and toroidal proxies across multiple cycles, we find solar cycle memory to be limited to only one cycle.« less

Periodicities observed on solar flux index (F10.7) during geomagnetic disturbances

NASA Astrophysics Data System (ADS)

Adhikari, B.; Narayan, C.; Chhatkuli, D. N.

2017-12-01

Solar activities change within the period of 11 years. Sometimes the greatest event occurs in the period of solar maxima and the lowest activity occurs in the period of solar minimum. During the time period of solar activity sunspots number will vary. A 10.7 cm solar flux measurement is a determination of the strength of solar radio emission. The solar flux index is more often used for the prediction and monitoring of the solar activity. This study mainly focused on the variation on solar flux index and amount of electromagnetic wave in the atmosphere. Both seasonal and yearly variation on solar F10.7 index. We also analyzed the dataset obatained from riometer.Both instruments show seasonal and yearly variations. We also observed the solar cycle dependence on solar flux index and found a strong dependence on solar activity. Results also show that solar intensities higher during the rising phase of solar cycle. We also observed periodicities on solar flux index using wavelet analysis. Through this analysis, it was found that the power intensities of solar flux index show a high spectral variability.

Predicting Major Solar Eruptions

NASA Astrophysics Data System (ADS)

Kohler, Susanna

2016-05-01

Coronal mass ejections (CMEs) and solar flares are two examples of major explosions from the surface of the Sun but theyre not the same thing, and they dont have to happen at the same time. A recent study examines whether we can predict which solar flares will be closely followed by larger-scale CMEs.Image of a solar flare from May 2013, as captured by NASAs Solar Dynamics Observatory. [NASA/SDO]Flares as a Precursor?A solar flare is a localized burst of energy and X-rays, whereas a CME is an enormous cloud of magnetic flux and plasma released from the Sun. We know that some magnetic activity on the surface of the Sun triggers both a flare and a CME, whereas other activity only triggers a confined flare with no CME.But what makes the difference? Understanding this can help us learn about the underlying physical drivers of flares and CMEs. It also might help us to better predict when a CME which can pose a risk to astronauts, disrupt radio transmissions, and cause damage to satellites might occur.In a recent study, Monica Bobra and Stathis Ilonidis (Stanford University) attempt to improve our ability to make these predictions by using a machine-learning algorithm.Classification by ComputerUsing a combination of 6 or more features results in a much better predictive success (measured by the True Skill Statistic; higher positive value = better prediction) for whether a flare will be accompanied by a CME. [Bobra Ilonidis 2016]Bobra and Ilonidis used magnetic-field data from an instrument on the Solar Dynamics Observatory to build a catalog of solar flares, 56 of which were accompanied by a CME and 364 of which were not. The catalog includes information about 18 different features associated with the photospheric magnetic field of each flaring active region (for example, the mean gradient of the horizontal magnetic field).The authors apply a machine-learning algorithm known as a binary classifier to this catalog. This algorithm tries to predict, given a set of features, whether an active region that produces a flare will also produce a CME. Bobra and Ilonidis then use a feature-selection algorithm to try to understand which features distinguish between flaring regions that dont produce a CME and those that do.Predictors of CMEsThe authors reach several interesting conclusions:Under the right conditions, their algorithm is able to predict whether an active region with a given set of features will produce a CME as well as a flare with a fairly high rate of success.None of the 18 features they tested are good predictors in isolation: its necessary to look at a combination of at least 6 features to have success predicting whether a flare will be accompanied by a CME.The features that are the best predictors are all intensive features ones that stay the same independent of the active regions size. Extensive features ones that change as the active region grows or shrinks are less successful predictors.Only the magnetic field properties of the photosphere were considered, so a logical next step is to extend this study to consider properties of the solar corona above active regions as well. In the meantime, these are interesting first results that may well help us better predict these major solar eruptions.BonusCheck out this video for a great description from NASA of the difference between solar flares and CMEs (as well as some awesome observations of both).CitationM. G. Bobra and S. Ilonidis 2016 ApJ 821 127. doi:10.3847/0004-637X/821/2/127

An early prediction of 25th solar cycle using Hurst exponent

NASA Astrophysics Data System (ADS)

Singh, A. K.; Bhargawa, Asheesh

2017-11-01

The analysis of long memory processes in solar activity, space weather and other geophysical phenomena has been a major issue even after the availability of enough data. We have examined the data of various solar parameters like sunspot numbers, 10.7 cm radio flux, solar magnetic field, proton flux and Alfven Mach number observed for the year 1976-2016. We have done the statistical test for persistence of solar activity based on the value of Hurst exponent (H) which is one of the most classical applied methods known as rescaled range analysis. We have discussed the efficiency of this methodology as well as prediction content for next solar cycle based on long term memory. In the present study, Hurst exponent analysis has been used to investigate the persistence of above mentioned (five) solar activity parameters and a simplex projection analysis has been used to predict the ascension time and the maximum number of counts for 25th solar cycle. For available dataset of the year 1976-2016, we have calculated H = 0.86 and 0.82 for sunspot number and 10.7 cm radio flux respectively. Further we have calculated maximum number of counts for sunspot numbers and F10.7 cm index as 102.8± 24.6 and 137.25± 8.9 respectively. Using the simplex projection analysis, we have forecasted that the solar cycle 25th would start in the year 2021 (January) and would last up to the year 2031 (September) with its maxima in June 2024.

Using a Magnetic Flux Transport Model to Predict the Solar Cycle

NASA Technical Reports Server (NTRS)

Lyatskaya, S.; Hathaway, D.; Winebarger, A.

2007-01-01

We present the results of an investigation into the use of a magnetic flux transport model to predict the amplitude of future solar cycles. Recently Dikpati, de Toma, & Gilman (2006) showed how their dynamo model could be used to accurately predict the amplitudes of the last eight solar cycles and offered a prediction for the next solar cycle - a large amplitude cycle. Cameron & Schussler (2007) found that they could reproduce this predictive skill with a simple 1-dimensional surface flux transport model - provided they used the same parameters and data as Dikpati, de Toma, & Gilman. However, when they tried incorporating the data in what they argued was a more realistic manner, they found that the predictive skill dropped dramatically. We have written our own code for examining this problem and have incorporated updated and corrected data for the source terms - the emergence of magnetic flux in active regions. We present both the model itself and our results from it - in particular our tests of its effectiveness at predicting solar cycles.

Automatic prediction of solar flares and super geomagnetic storms

NASA Astrophysics Data System (ADS)

Song, Hui

Space weather is the response of our space environment to the constantly changing Sun. As the new technology advances, mankind has become more and more dependent on space system, satellite-based services. A geomagnetic storm, a disturbance in Earth's magnetosphere, may produce many harmful effects on Earth. Solar flares and Coronal Mass Ejections (CMEs) are believed to be the major causes of geomagnetic storms. Thus, establishing a real time forecasting method for them is very important in space weather study. The topics covered in this dissertation are: the relationship between magnetic gradient and magnetic shear of solar active regions; the relationship between solar flare index and magnetic features of solar active regions; based on these relationships a statistical ordinal logistic regression model is developed to predict the probability of solar flare occurrences in the next 24 hours; and finally the relationship between magnetic structures of CME source regions and geomagnetic storms, in particular, the super storms when the D st index decreases below -200 nT is studied and proved to be able to predict those super storms. The results are briefly summarized as follows: (1) There is a significant correlation between magnetic gradient and magnetic shear of active region. Furthermore, compared with magnetic shear, magnetic gradient might be a better proxy to locate where a large flare occurs. It appears to be more accurate in identification of sources of X-class flares than M-class flares; (2) Flare index, defined by weighting the SXR flares, is proved to have positive correlation with three magnetic features of active region; (3) A statistical ordinal logistic regression model is proposed for solar flare prediction. The results are much better than those data published in the NASA/SDAC service, and comparable to the data provided by the NOAA/SEC complicated expert system. To our knowledge, this is the first time that logistic regression model has been applied in solar physics to predict flare occurrences; (4) The magnetic orientation angle [straight theta], determined from a potential field model, is proved to be able to predict the probability of super geomagnetic storms (D= st <=-200nT). The results show that those active regions associated with | [straight theta]| < 90° are more likely to cause a super geomagnetic storm.

Using Data Assimilation Methods of Prediction of Solar Activity

NASA Technical Reports Server (NTRS)

Kitiashvili, Irina N.; Collins, Nancy S.

2017-01-01

The variable solar magnetic activity known as the 11-year solar cycle has the longest history of solar observations. These cycles dramatically affect conditions in the heliosphere and the Earth's space environment. Our current understanding of the physical processes that make up global solar dynamics and the dynamo that generates the magnetic fields is sketchy, resulting in unrealistic descriptions in theoretical and numerical models of the solar cycles. The absence of long-term observations of solar interior dynamics and photospheric magnetic fields hinders development of accurate dynamo models and their calibration. In such situations, mathematical data assimilation methods provide an optimal approach for combining the available observational data and their uncertainties with theoretical models in order to estimate the state of the solar dynamo and predict future cycles. In this presentation, we will discuss the implementation and performance of an Ensemble Kalman Filter data assimilation method based on the Parker migratory dynamo model, complemented by the equation of magnetic helicity conservation and long-term sunspot data series. This approach has allowed us to reproduce the general properties of solar cycles and has already demonstrated a good predictive capability for the current cycle, 24. We will discuss further development of this approach, which includes a more sophisticated dynamo model, synoptic magnetogram data, and employs the DART Data Assimilation Research Testbed.

The study on the new approach to the prediction of the solar flares: The statistical relation from the SOHO archive

NASA Astrophysics Data System (ADS)

Lee, S.; Oh, S.; Lee, J.; Hong, S.

2013-12-01

We have investigated the statistical relationship of the solar active region to predict the solar flare event analyzing the sunspot catalogue, which has been newly constructed from the SOHO MDI observation data during the period from 1996 to 2011 (Solar Cycle 23 & 24) by ASSA(Automatic Solar Synoptic Analyzer) algorithms. The prediction relation has been made by machine-learning algorithms to establish a short- term flare prediction model for operational use in near future. In this study, continuum and magnetogram images observed by SOHO has been processed to yield 15-year sunspot group catalogue that contains various physical parameters such as sunspot area, extent, asymmetry measure of largest penumbral sunspot, roughness of magnetic neutral line as well as McIntosh and Mt. Wilson classification results.The latest result of our study will be presented and the new approach to the prediction of the solar flare will be discussed.

New insight into Earth's weather through studies of Sun's magnetic fields

NASA Technical Reports Server (NTRS)

1990-01-01

Solar Vector Magnetograph is used to predict solar flares, and other activities associated with sun spots. This research provides new understanding about weather on the Earth, and solar-related conditions in orbit.

Solar radio proxies for improved satellite orbit prediction

NASA Astrophysics Data System (ADS)

Yaya, Philippe; Hecker, Louis; Dudok de Wit, Thierry; Fèvre, Clémence Le; Bruinsma, Sean

2017-12-01

Specification and forecasting of solar drivers to thermosphere density models is critical for satellite orbit prediction and debris avoidance. Satellite operators routinely forecast orbits up to 30 days into the future. This requires forecasts of the drivers to these orbit prediction models such as the solar Extreme-UV (EUV) flux and geomagnetic activity. Most density models use the 10.7 cm radio flux (F10.7 index) as a proxy for solar EUV. However, daily measurements at other centimetric wavelengths have also been performed by the Nobeyama Radio Observatory (Japan) since the 1950's, thereby offering prospects for improving orbit modeling. Here we present a pre-operational service at the Collecte Localisation Satellites company that collects these different observations in one single homogeneous dataset and provides a 30 days forecast on a daily basis. Interpolation and preprocessing algorithms were developed to fill in missing data and remove anomalous values. We compared various empirical time series prediction techniques and selected a multi-wavelength non-recursive analogue neural network. The prediction of the 30 cm flux, and to a lesser extent that of the 10.7 cm flux, performs better than NOAA's present prediction of the 10.7 cm flux, especially during periods of high solar activity. In addition, we find that the DTM-2013 density model (Drag Temperature Model) performs better with (past and predicted) values of the 30 cm radio flux than with the 10.7 flux.

Space Environment Modelling with the Use of Artificial Intelligence Methods

NASA Astrophysics Data System (ADS)

Lundstedt, H.; Wintoft, P.; Wu, J.-G.; Gleisner, H.; Dovheden, V.

1996-12-01

Space based technological systems are affected by the space weather in many ways. Several severe failures of satellites have been reported at times of space storms. Our society also increasingly depends on satellites for communication, navigation, exploration, and research. Predictions of the conditions in the satellite environment have therefore become very important. We will here present predictions made with the use of artificial intelligence (AI) techniques, such as artificial neural networks (ANN) and hybrids of AT methods. We are developing a space weather model based on intelligence hybrid systems (IHS). The model consists of different forecast modules, each module predicts the space weather on a specific time-scale. The time-scales range from minutes to months with the fundamental time-scale of 1-5 minutes, 1-3 hours, 1-3 days, and 27 days. Solar and solar wind data are used as input data. From solar magnetic field measurements, either made on the ground at Wilcox Solar Observatory (WSO) at Stanford, or made from space by the satellite SOHO, solar wind parameters can be predicted and modelled with ANN and MHD models. Magnetograms from WSO are available on a daily basis. However, from SOHO magnetograms will be available every 90 minutes. SOHO magnetograms as input to ANNs will therefore make it possible to even predict solar transient events. Geomagnetic storm activity can today be predicted with very high accuracy by means of ANN methods using solar wind input data. However, at present real-time solar wind data are only available during part of the day from the satellite WIND. With the launch of ACE in 1997, solar wind data will on the other hand be available during 24 hours per day. The conditions of the satellite environment are not only disturbed at times of geomagnetic storms but also at times of intense solar radiation and highly energetic particles. These events are associated with increased solar activity. Predictions of these events are therefore also handled with the modules in the Lund Space Weather Model. Interesting Links: Lund Space Weather and AI Center

Have We Entered a 21st Century Prolonged Minimum of Solar Activity? Updated Implications of a 1987 Prediction

NASA Astrophysics Data System (ADS)

Shirley, James H.

2009-05-01

Fairbridge and Shirley (1987) predicted that a new prolonged minimum of solar activity would be underway by the year 2013 (Solar Physics 110, 191). While it is much too early to tell if this prediction will be fully realized, recent observations document a striking reduction in the Sun's general level of activity. While other forecasts of reduced future activity levels on decadal time scales have appeared, the Fairbridge-Shirley (FS) prediction is unique in pinpointing the current epoch. We are unaware of any forecast method that shows a better correspondence with the actual behavior of the Sun to this point. The FS prediction was based on the present-day recurrence of two physical indicators that were correlated in time with the occurrence of the Wolf, Sporer, and Maunder Minima. The amplitude of the inertial revolution of the axis of symmetry of the Sun's orbital motion about the solar system barycenter, and the direction in space of that axis, each bear a relationship to the occurrence of the prolonged minima of the historic record. The FS prediction appeared before the importance of solar meridional flows was generally appreciated, and before the existence and role of the tachocline was suspected. We will update and restate some of the physical implications of the FS results, along with those of some more recent investigations, particularly with reference to orbit-spin coupling hypotheses (Shirley, 2006: M.N.R.A.S. 368, 280). New investigations combining and integrating modern dynamo models with physical solutions describing key aspects of the variability of the solar motion may lead to significant advances in our ability to forecast future changes in the Sun. Acknowledgement: This work was supported by the resources of the author. No part of this work was performed at the Jet Propulsion Laboratory under a contract from NASA.

Prediction of global ionospheric VTEC maps using an adaptive autoregressive model

NASA Astrophysics Data System (ADS)

Wang, Cheng; Xin, Shaoming; Liu, Xiaolu; Shi, Chuang; Fan, Lei

2018-02-01

In this contribution, an adaptive autoregressive model is proposed and developed to predict global ionospheric vertical total electron content maps (VTEC). Specifically, the spherical harmonic (SH) coefficients are predicted based on the autoregressive model, and the order of the autoregressive model is determined adaptively using the F-test method. To test our method, final CODE and IGS global ionospheric map (GIM) products, as well as altimeter TEC data during low and mid-to-high solar activity period collected by JASON, are used to evaluate the precision of our forecasting products. Results indicate that the predicted products derived from the model proposed in this paper have good consistency with the final GIMs in low solar activity, where the annual mean of the root-mean-square value is approximately 1.5 TECU. However, the performance of predicted vertical TEC in periods of mid-to-high solar activity has less accuracy than that during low solar activity periods, especially in the equatorial ionization anomaly region and the Southern Hemisphere. Additionally, in comparison with forecasting products, the final IGS GIMs have the best consistency with altimeter TEC data. Future work is needed to investigate the performance of forecasting products using the proposed method in an operational environment, rather than using the SH coefficients from the final CODE products, to understand the real-time applicability of the method.

Prediction of the total cycle 24 of solar activity by several autoregressive methods and by the precursor method

NASA Astrophysics Data System (ADS)

Ozheredov, V. A.; Breus, T. K.; Obridko, V. N.

2012-12-01

As follows from the statement of the Third Official Solar Cycle 24 Prediction Panel created by the National Aeronautics and Space Administration (NASA), the National Oceanic and Atmospheric Administration (NOAA), and the International Space Environment Service (ISES) based on the results of an analysis of many solar cycle 24 predictions, there has been no consensus on the amplitude and time of the maximum. There are two different scenarios: 90 units and August 2012 or 140 units and October 2011. The aim of our study is to revise the solar cycle 24 predictions by a comparative analysis of data obtained by three different methods: the singular spectral method, the nonlinear neural-based method, and the precursor method. As a precursor for solar cycle 24, we used the dynamics of the solar magnetic fields forming solar spots with Wolf numbers Rz. According to the prediction on the basis of the neural-based approach, it was established that the maximum of solar cycle 24 is expected to be 70. The precursor method predicted 50 units for the amplitude and April of 2012 for the time of the maximum. In view of the fact that the data used in the precursor method were averaged over 4.4 years, the amplitude of the maximum can be 20-30% larger (i.e., around 60-70 units), which is close to the values predicted by the neural-based method. The protracted minimum of solar cycle 23 and predicted low values of the maximum of solar cycle 24 are reminiscent of the historical Dalton minimum.

The SAMEX Vector Magnetograph: A Design Study for a Space-Based Solar Vector Magnetograph

NASA Technical Reports Server (NTRS)

Hagyard, M. J.; Gary, G. A.; West, E. A.

1988-01-01

This report presents the results of a pre-phase A study performed by the Marshall Space Flight Center (MSFC) for the Air Force Geophysics Laboratory (AFGL) to develop a design concept for a space-based solar vector magnetograph and hydrogen-alpha telescope. These are two of the core instruments for a proposed Air Force mission, the Solar Activities Measurement Experiments (SAMEX). This mission is designed to study the processes which give rise to activity in the solar atmosphere and to develop techniques for predicting solar activity and its effects on the terrestrial environment.

Solar Surface Velocity in the Large Scale estimated by Magnetic Element Tracking Method

NASA Astrophysics Data System (ADS)

Fujiyama, M.; Imada, S.; Iijima, H.; Machida, S.

2017-12-01

The 11years variation in the solar activity is one of the important sources of decadal variation in the solar-terrestrial environment. Therefore, predicting the solar cycle activity is crucial for the space weather. To build the prediction schemes for the next solar cycle is a key for the long-term space weather study. Recently, the relationship between polar magnetic field at the solar minimum and next solar cycle activity is intensively discussed. Nowadays, many people believe that the polar magnetic field at the solar minimum is one of the best predictor for the next solar cycle. To estimate polar magnetic field, Surface Flux Transport (SFT) model have been often used. On the other hand, SFT model needs several parameters, for example Meridional circulation, differential rotation, turbulent diffusion etc.. So far, those parameters have not been fully understood, and their uncertainties may affect the accuracy of the prediction. In this study, we try to discuss the parameters which are used in SFT model. We focus on two kinds of the solar surface motions, Differential rotation and Meridional circulation. First, we have developed Magnetic Element Tracking (MET) module, which is able to obtain the surface velocity by using the magnetic field data. We have used SOHO/MDI and SDO/HMI for the magnetic field data. By using MET, we study the solar surface motion over 2 cycle (nearly 24 years), and we found that the velocity variation is related to the active region belt. This result is consistent with [Hathaway et al., 2011]. Further, we apply our module to the Hinode/SOT data which spatial resolution is high. Because of its high resolution, we can discuss the surface motion close to the pole which has not been discussed enough. Further, we discuss the relationship between the surface motion and the magnetic field strength and the location of longitude.

Actual versus predicted performance of an active solar heating system - A comparison using FCHART 4.0

NASA Astrophysics Data System (ADS)

Wetzel, P. E.

1981-11-01

The performance of an active solar heating system added to a house in Denver, CO was compared with predictions made by the FCHART 4.0 computer program. The house featured 43.23 sq m of collectors with an ethylene-glycol/water heat transfer fluid, and a 3.23 cu m storage tank. The house hot water was preheated in the storage tank, and home space heat was furnished whenever the storage water was above 32 C. Actual meteorological and heating demand data were used for the comparison, rather than long-term averages. Although monthly predictions by the FCHART program were found to diverge from measured data, the annual demand and supply predictions provided a good fit, i.e. within 9%, and were within 1% of the measured solar energy contributed to storage.

Active optimal control strategies for increasing the efficiency of photovoltaic cells

NASA Astrophysics Data System (ADS)

Aljoaba, Sharif Zidan Ahmad

Energy consumption has increased drastically during the last century. Currently, the worldwide energy consumption is about 17.4 TW and is predicted to reach 25 TW by 2035. Solar energy has emerged as one of the potential renewable energy sources. Since its first physical recognition in 1887 by Adams and Day till nowadays, research in solar energy is continuously developing. This has lead to many achievements and milestones that introduced it as one of the most reliable and sustainable energy sources. Recently, the International Energy Agency declared that solar energy is predicted to be one of the major electricity production energy sources by 2035. Enhancing the efficiency and lifecycle of photovoltaic (PV) modules leads to significant cost reduction. Reducing the temperature of the PV module improves its efficiency and enhances its lifecycle. To better understand the PV module performance, it is important to study the interaction between the output power and the temperature. A model that is capable of predicting the PV module temperature and its effects on the output power considering the individual contribution of the solar spectrum wavelengths significantly advances the PV module edsigns toward higher efficiency. In this work, a thermoelectrical model is developed to predict the effects of the solar spectrum wavelengths on the PV module performance. The model is characterized and validated under real meteorological conditions where experimental temperature and output power of the PV module measurements are shown to agree with the predicted results. The model is used to validate the concept of active optical filtering. Since this model is wavelength-based, it is used to design an active optical filter for PV applications. Applying this filter to the PV module is expected to increase the output power of the module by filtering the spectrum wavelengths. The active filter performance is optimized, where different cutoff wavelengths are used to maximize the module output power. It is predicted that if the optimized active optical filter is applied to the PV module, the module efficiency is predicted to increase by about 1%. Different technologies are considered for physical implementation of the active optical filter.

An early solar dynamo prediction: Cycle 23 is approximately cycle 22

NASA Technical Reports Server (NTRS)

Schatten, Kenneth H.; Pesnell, W. Dean

1993-01-01

In this paper, we briefly review the 'dynamo' and 'geomagnetic precursor' methods of long-term solar activity forecasting. These methods depend upon the most basic aspect of dynamo theory to predict future activity, future magnetic field arises directly from the magnification of pre-existing magnetic field. We then generalize the dynamo technique, allowing the method to be used at any phase of the solar cycle, through the development of the 'Solar Dynamo Amplitude' (SODA) index. This index is sensitive to the magnetic flux trapped within the Sun's convection zone but insensitive to the phase of the solar cycle. Since magnetic fields inside the Sun can become buoyant, one may think of the acronym SODA as describing the amount of buoyant flux. Using the present value of the SODA index, we estimate that the next cycle's smoothed peak activity will be about 210 +/- 30 solar flux units for the 10.7 cm radio flux and a sunspot number of 170 +/- 25. This suggests that solar cycle #23 will be large, comparable to cycle #22. The estimated peak is expected to occur near 1999.7 +/- 1 year. Since the current approach is novel (using data prior to solar minimum), these estimates may improve when the upcoming solar minimum is reached.

Solar Flares and Their Prediction

NASA Technical Reports Server (NTRS)

Adams, Mitzi L.

1999-01-01

Solar flares and coronal mass ejection's (CMES) can strongly affect the local environment at the Earth. A major challenge for solar physics is to understand the physical mechanisms responsible for the onset of solar flares. Flares, characterized by a sudden release of energy (approx. 10(exp 32) ergs for the largest events) within the solar atmosphere, result in the acceleration of electrons, protons, and heavier ions as well as the production of electromagnetic radiation from hard X-rays to km radio waves (wavelengths approx. = 10(exp -9) cm to 10(exp 6) cm). Observations suggest that solar flares and sunspots are strongly linked. For example, a study of data from 1956-1969, reveals that approx. 93 percent of major flares originate in active regions with spots. Furthermore, the global structure of the sunspot magnetic field can be correlated with flare activity. This talk will review what we know about flare causes and effects and will discuss techniques for quantifying parameters, which may lead to a prediction of solar flares.

High resolution solar observations in the context of space weather prediction

NASA Astrophysics Data System (ADS)

Yang, Guo

Space weather has a great impact on the Earth and human life. It is important to study and monitor active regions on the solar surface and ultimately to predict space weather based on the Sun's activity. In this study, a system that uses the full power of speckle masking imaging by parallel processing to obtain high-spatial resolution images of the solar surface in near real-time has been developed and built. The application of this system greatly improves the ability to monitor the evolution of solar active regions and to predict the adverse effects of space weather. The data obtained by this system have also been used to study fine structures on the solar surface and their effects on the upper solar atmosphere. A solar active region has been studied using high resolution data obtained by speckle masking imaging. Evolution of a pore in an active region presented. Formation of a rudimentary penumbra is studied. The effects of the change of the magnetic fields on the upper level atmosphere is discussed. Coronal Mass Ejections (CMEs) have a great impact on space weather. To study the relationship between CMEs and filament disappearance, a list of 431 filament and prominence disappearance events has been compiled. Comparison of this list with CME data obtained by satellite has shown that most filament disappearances seem to have no corresponding CME events. Even for the limb events, only thirty percent of filament disappearances are associated with CMEs. A CME event that was observed on March 20, 2000 has been studied in detail. This event did not show the three-parts structure of typical CMEs. The kinematical and morphological properties of this event were examined.

The Solar Wind and Geomagnetic Activity as a Function of Time Relative to Corotating Interaction Regions

NASA Technical Reports Server (NTRS)

McPherron, Robert L.; Weygand, James

2006-01-01

Corotating interaction regions during the declining phase of the solar cycle are the cause of recurrent geomagnetic storms and are responsible for the generation of high fluxes of relativistic electrons. These regions are produced by the collision of a high-speed stream of solar wind with a slow-speed stream. The interface between the two streams is easily identified with plasma and field data from a solar wind monitor upstream of the Earth. The properties of the solar wind and interplanetary magnetic field are systematic functions of time relative to the stream interface. Consequently the coupling of the solar wind to the Earth's magnetosphere produces a predictable sequence of events. Because the streams persist for many solar rotations it should be possible to use terrestrial observations of past magnetic activity to predict future activity. Also the high-speed streams are produced by large unipolar magnetic regions on the Sun so that empirical models can be used to predict the velocity profile of a stream expected at the Earth. In either case knowledge of the statistical properties of the solar wind and geomagnetic activity as a function of time relative to a stream interface provides the basis for medium term forecasting of geomagnetic activity. In this report we use lists of stream interfaces identified in solar wind data during the years 1995 and 2004 to develop probability distribution functions for a variety of different variables as a function of time relative to the interface. The results are presented as temporal profiles of the quartiles of the cumulative probability distributions of these variables. We demonstrate that the storms produced by these interaction regions are generally very weak. Despite this the fluxes of relativistic electrons produced during those storms are the highest seen in the solar cycle. We attribute this to the specific sequence of events produced by the organization of the solar wind relative to the stream interfaces. We also show that there are large quantitative differences in various parameters between the two cycles.

COMPARISON OF CHAOTIC AND FRACTAL PROPERTIES OF POLAR FACULAE WITH SUNSPOT ACTIVITY

DOE Office of Scientific and Technical Information (OSTI.GOV)

Deng, L. H.; Xiang, Y. Y.; Dun, G. T.

The solar magnetic activity is governed by a complex dynamo mechanism and exhibits a nonlinear dissipation behavior in nature. The chaotic and fractal properties of solar time series are of great importance to understanding the solar dynamo actions, especially with regard to the nonlinear dynamo theories. In the present work, several nonlinear analysis approaches are proposed to investigate the nonlinear dynamical behavior of the polar faculae and sunspot activity for the time interval from 1951 August to 1998 December. The following prominent results are found: (1) both the high- and the low-latitude solar activity are governed by a three-dimensional chaoticmore » attractor, and the chaotic behavior of polar faculae is the most complex, followed by that of the sunspot areas, and then the sunspot numbers; (2) both the high- and low-latitude solar activity exhibit a high degree of persistent behavior, and their fractal nature is due to such long-range correlation; (3) the solar magnetic activity cycle is predictable in nature, but the high-accuracy prediction should only be done for short- to mid-term due to its intrinsically dynamical complexity. With the help of the Babcock–Leighton dynamo model, we suggest that the nonlinear coupling of the polar magnetic fields with strong active-region fields exhibits a complex manner, causing the statistical similarities and differences between the polar faculae and the sunspot-related indicators.« less

Physics-based Space Weather Forecasting in the Project for Solar-Terrestrial Environment Prediction (PSTEP) in Japan

NASA Astrophysics Data System (ADS)

Kusano, K.

2016-12-01

Project for Solar-Terrestrial Environment Prediction (PSTEP) is a Japanese nation-wide research collaboration, which was recently launched. PSTEP aims to develop a synergistic interaction between predictive and scientific studies of the solar-terrestrial environment and to establish the basis for next-generation space weather forecasting using the state-of-the-art observation systems and the physics-based models. For this project, we coordinate the four research groups, which develop (1) the integration of space weather forecast system, (2) the physics-based solar storm prediction, (3) the predictive models of magnetosphere and ionosphere dynamics, and (4) the model of solar cycle activity and its impact on climate, respectively. In this project, we will build the coordinated physics-based model to answer the fundamental questions concerning the onset of solar eruptions and the mechanism for radiation belt dynamics in the Earth's magnetosphere. In this paper, we will show the strategy of PSTEP, and discuss about the role and prospect of the physics-based space weather forecasting system being developed by PSTEP.

The MSFC Solar Activity Future Estimation (MSAFE) Model

NASA Technical Reports Server (NTRS)

Suggs, Ronnie J.

2017-01-01

The MSAFE model provides forecasts for the solar indices SSN, F10.7, and Ap. These solar indices are used as inputs to many space environment models used in orbital spacecraft operations and space mission analysis. Forecasts from the MSAFE model are provided on the MSFC Natural Environments Branch's solar webpage and are updated as new monthly observations come available. The MSAFE prediction routine employs a statistical technique that calculates deviations of past solar cycles from the mean cycle and performs a regression analysis to predict the deviation from the mean cycle of the solar index at the next future time interval. The prediction algorithm is applied recursively to produce monthly smoothed solar index values for the remaining of the cycle. The forecasts are initiated for a given cycle after about 8 to 12 months of observations are collected. A forecast made at the beginning of cycle 24 using the MSAFE program captured the cycle fairly well with some difficulty in discerning the double peak that occurred at solar cycle maximum.

A model for solar constant secular changes

NASA Technical Reports Server (NTRS)

Schatten, Kenneth H.

1988-01-01

In this paper, contrast models for solar active region and global photospheric features are used to reproduce the observed Active Cavity Radiometer and Earth Radiation Budget secular trends in reasonably good fashion. A prediction for the next decade of solar constant variations is made using the model. Secular trends in the solar constant obtained from the present model support the view that the Maunder Minimum may be related to the Little Ice Age of the 17th century.

A comparison of solar irradiances measured by SBUV, SME, and rockets

NASA Technical Reports Server (NTRS)

Schlesinger, Barry M.; Heath, Donald F.

1988-01-01

In this paper, Solar Backscatter Ultraviolet (SBUV) measurements of solar irradiance and predictions from the Mg 280-nm index are compared with each other and with coincident Solar Mesosphere Explorer (SME) and rocket measurements. The SBUV irradiances show a systematic decrease with time not seen in the rocket measurements; a correction for this decrease is introduced. The scatter and overall structure in the SME spectra is 3-5 percent, of the order of or larger than most of the changes predicted by the Mg index. The corrected SBUV ratio and the Mg index prediction for it agree to within 1 percent. Such agreement supports a common origin for variations between solar maximum and minimum and those for individual rotations: the degree to which active regions cover the visible hemisphere of the sun.

Forecasting the peak of the present solar activity cycle 24

NASA Astrophysics Data System (ADS)

Hamid, R. H.; Marzouk, B. A.

2018-06-01

Solar forecasting of the level of sun Activity is very important subject for all space programs. Most predictions are based on the physical conditions prevailing at or before the solar cycle minimum preceding the maximum in question. Our aim is to predict the maximum peak of cycle 24 using precursor techniques in particular those using spotless event, geomagnetic aamin. index and solar flux F10.7. Also prediction of exact date of the maximum (Tr) is taken in consideration. A study of variation over previous spotless event for cycles 7-23 and that for even cycles (8-22) are carried out for the prediction. Linear correlation between maximum of solar cycles (RM) and spotless event around the preceding minimum gives R24t = 88.4 with rise time Tr = 4.6 years. For the even cycles R24E = 77.9 with rise time Tr = 4.5 y's. Based on the average aamin. index for cycles (12-23), we estimate the expected amplitude for cycle 24 to be Raamin = 99.4 and 98.1 with time rise of Traamin = 4.04 & 4.3 years for both the total and even cycles in consecutive. The application of the data of solar flux F10.7 which cover only cycles (19-23) was taken in consideration and gives predicted maximum amplitude R24 10.7 = 126 with rise time Tr107 = 3.7 years, which are over estimation. Our result indicating to somewhat weaker of cycle 24 as compared to cycles 21-23.

Prediction of Geomagnetic Activity and Key Parameters in High-Latitude Ionosphere-Basic Elements

NASA Technical Reports Server (NTRS)

Lyatsky, W.; Khazanov, G. V.

2007-01-01

Prediction of geomagnetic activity and related events in the Earth's magnetosphere and ionosphere is an important task of the Space Weather program. Prediction reliability is dependent on the prediction method and elements included in the prediction scheme. Two main elements are a suitable geomagnetic activity index and coupling function -- the combination of solar wind parameters providing the best correlation between upstream solar wind data and geomagnetic activity. The appropriate choice of these two elements is imperative for any reliable prediction model. The purpose of this work was to elaborate on these two elements -- the appropriate geomagnetic activity index and the coupling function -- and investigate the opportunity to improve the reliability of the prediction of geomagnetic activity and other events in the Earth's magnetosphere. The new polar magnetic index of geomagnetic activity and the new version of the coupling function lead to a significant increase in the reliability of predicting the geomagnetic activity and some key parameters, such as cross-polar cap voltage and total Joule heating in high-latitude ionosphere, which play a very important role in the development of geomagnetic and other activity in the Earth s magnetosphere, and are widely used as key input parameters in modeling magnetospheric, ionospheric, and thermospheric processes.

Heartbeat of the Sun from Principal Component Analysis and prediction of solar activity on a millenium timescale

PubMed Central

Zharkova, V. V.; Shepherd, S. J.; Popova, E.; Zharkov, S. I.

2015-01-01

We derive two principal components (PCs) of temporal magnetic field variations over the solar cycles 21–24 from full disk magnetograms covering about 39% of data variance, with σ = 0.67. These PCs are attributed to two main magnetic waves travelling from the opposite hemispheres with close frequencies and increasing phase shift. Using symbolic regeression analysis we also derive mathematical formulae for these waves and calculate their summary curve which we show is linked to solar activity index. Extrapolation of the PCs backward for 800 years reveals the two 350-year grand cycles superimposed on 22 year-cycles with the features showing a remarkable resemblance to sunspot activity reported in the past including the Maunder and Dalton minimum. The summary curve calculated for the next millennium predicts further three grand cycles with the closest grand minimum occurring in the forthcoming cycles 26–27 with the two magnetic field waves separating into the opposite hemispheres leading to strongly reduced solar activity. These grand cycle variations are probed by α − Ω dynamo model with meridional circulation. Dynamo waves are found generated with close frequencies whose interaction leads to beating effects responsible for the grand cycles (350–400 years) superimposed on a standard 22 year cycle. This approach opens a new era in investigation and confident prediction of solar activity on a millenium timescale. PMID:26511513

Solar activity prediction

NASA Technical Reports Server (NTRS)

Slutz, R. J.; Gray, T. B.; West, M. L.; Stewart, F. G.; Leftin, M.

1971-01-01

A statistical study of formulas for predicting the sunspot number several years in advance is reported. By using a data lineup with cycle maxima coinciding, and by using multiple and nonlinear predictors, a new formula which gives better error estimates than former formulas derived from the work of McNish and Lincoln is obtained. A statistical analysis is conducted to determine which of several mathematical expressions best describes the relationship between 10.7 cm solar flux and Zurich sunspot numbers. Attention is given to the autocorrelation of the observations, and confidence intervals for the derived relationships are presented. The accuracy of predicting a value of 10.7 cm solar flux from a predicted sunspot number is dicussed.

Prediction of solar activity from solar background magnetic field variations in cycles 21-23

DOE Office of Scientific and Technical Information (OSTI.GOV)

Shepherd, Simon J.; Zharkov, Sergei I.; Zharkova, Valentina V., E-mail: s.j.shepherd@brad.ac.uk, E-mail: s.zharkov@hull.ac.uk, E-mail: valentina.zharkova@northumbria.ac.uk

2014-11-01

A comprehensive spectral analysis of both the solar background magnetic field (SBMF) in cycles 21-23 and the sunspot magnetic field in cycle 23 reported in our recent paper showed the presence of two principal components (PCs) of SBMF having opposite polarity, e.g., originating in the northern and southern hemispheres, respectively. Over a duration of one solar cycle, both waves are found to travel with an increasing phase shift toward the northern hemisphere in odd cycles 21 and 23 and to the southern hemisphere in even cycle 22. These waves were linked to solar dynamo waves assumed to form in differentmore » layers of the solar interior. In this paper, for the first time, the PCs of SBMF in cycles 21-23 are analyzed with the symbolic regression technique using Hamiltonian principles, allowing us to uncover the underlying mathematical laws governing these complex waves in the SBMF presented by PCs and to extrapolate these PCs to cycles 24-26. The PCs predicted for cycle 24 very closely fit (with an accuracy better than 98%) the PCs derived from the SBMF observations in this cycle. This approach also predicts a strong reduction of the SBMF in cycles 25 and 26 and, thus, a reduction of the resulting solar activity. This decrease is accompanied by an increasing phase shift between the two predicted PCs (magnetic waves) in cycle 25 leading to their full separation into the opposite hemispheres in cycle 26. The variations of the modulus summary of the two PCs in SBMF reveals a remarkable resemblance to the average number of sunspots in cycles 21-24 and to predictions of reduced sunspot numbers compared to cycle 24: 80% in cycle 25 and 40% in cycle 26.« less

Solar cosmic ray hazard to interplanetary and earth-orbital space travel

NASA Technical Reports Server (NTRS)

Yucker, W. R.

1972-01-01

A statistical treatment of the radiation hazards to astronauts due to solar cosmic ray protons is reported to determine shielding requirements for solar proton events. More recent data are incorporated into the present analysis in order to improve the accuracy of the predicted mission fluence and dose. The effects of the finite data sample are discussed. Mission fluence and dose versus shield thickness data are presented for mission lengths up to 3 years during periods of maximum and minimum solar activity; these correspond to various levels of confidence that the predicted hazard will not be exceeded.

A new ionospheric index MF2

NASA Astrophysics Data System (ADS)

Mikhailov, A. V.; Mikhailov, V. V.

1995-02-01

A new ionospheric index MF2 to improve monthly median foF2 regression and prediction accuracy is proposed. The interhemispheric magnetic conjunction of the F2-region was used to derive this index for the northern hemisphere. Since the monthly MF2 index varies in regular way with the season and in the course of solar cycle this allows an easy long-term prediction. Using MF2 instead of direct solar R12 index considerably improves the quality of the foF2 versus solar activity level regression (by 30% for middle, and by 10% for high latitudes.) For the rising phase of solar cycle 22, MF2 yields much better foF2 prediction accuracy than Consultative Committee on International Radiopropagation (CCIR) numerical maps can achieve.

An Early Prediction of Sunspot Cycle 25

NASA Astrophysics Data System (ADS)

Nandy, D.; Bhowmik, P.

2017-12-01

The Sun's magnetic activity governs our space environment, creates space weather and impacts our technologies and climate. With increasing reliance on space- and ground-based technologies that are subject to space weather, the need to be able to forecast the future activity of the Sun has assumed increasing importance. However, such long-range, decadal-scale space weather prediction has remained a great challenge as evident in the diverging forecasts for solar cycle 24. Based on recently acquired understanding of the physics of solar cycle predictability, we have devised a scheme to extend the forecasting window of solar cycles. Utilizing this we present an early forecast for sunspot cycle 25 which would be of use for space mission planning, satellite life-time estimates, and assessment of the long-term impacts of space weather on technological assets and planetary atmospheres.

Prediction Methods in Solar Sunspots Cycles

PubMed Central

Ng, Kim Kwee

2016-01-01

An understanding of the Ohl’s Precursor Method, which is used to predict the upcoming sunspots activity, is presented by employing a simplified movable divided-blocks diagram. Using a new approach, the total number of sunspots in a solar cycle and the maximum averaged monthly sunspots number Rz(max) are both shown to be statistically related to the geomagnetic activity index in the prior solar cycle. The correlation factors are significant and they are respectively found to be 0.91 ± 0.13 and 0.85 ± 0.17. The projected result is consistent with the current observation of solar cycle 24 which appears to have attained at least Rz(max) at 78.7 ± 11.7 in March 2014. Moreover, in a statistical study of the time-delayed solar events, the average time between the peak in the monthly geomagnetic index and the peak in the monthly sunspots numbers in the succeeding ascending phase of the sunspot activity is found to be 57.6 ± 3.1 months. The statistically determined time-delayed interval confirms earlier observational results by others that the Sun’s electromagnetic dipole is moving toward the Sun’s Equator during a solar cycle. PMID:26868269

Gravity wave control on ESF day-to-day variability: An empirical approach

NASA Astrophysics Data System (ADS)

Aswathy, R. P.; Manju, G.

2017-06-01

The gravity wave control on the daily variation in nighttime ionization irregularity occurrence is studied using ionosonde data for the period 2002-2007 at magnetic equatorial location Trivandrum. Recent studies during low solar activity period have revealed that the seed perturbations should have the threshold amplitude required to trigger equatorial spread F (ESF), at a particular altitude and that this threshold amplitude undergoes seasonal and solar cycle changes. In the present study, the altitude variation of the threshold seed perturbations is examined for autumnal equinox of different years. Thereafter, a unique empirical model, incorporating the electrodynamical effects and the gravity wave modulation, is developed. Using the model the threshold curve for autumnal equinox season of any year may be delineated if the solar flux index (F10.7) is known. The empirical model is validated using the data for high, moderate, and low solar epochs in 2001, 2004, and 1995, respectively. This model has the potential to be developed further, to forecast ESF incidence, if the base height of ionosphere is in the altitude region where electrodynamics controls the occurrence of ESF. ESF irregularities are harmful for communication and navigation systems, and therefore, research is ongoing globally to predict them. In this context, this study is crucial for evolving a methodology to predict communication as well as navigation outages.