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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS1989

Ionospheric effects of the extreme solar activity of February 1986

During February 1986, near the minimum of the 11 year Solar sunspot cycle, after a long period of totally quiet solar activity (R sub z = 0 on most days in January) a period of a suddenly enhanced solar activity occurred in the minimum between solar cycles 21 and 22. Two proton flares were observed during this period. A few other flares, various phenomena accompanying proton flares, an extremely severe geomagnetic storm and strong disturbances in the Earth's ionosphere were observed in this period of enhanced solar activity. Two active regions appeared on the solar disc. The flares in both active regions were associated with enhancement of solar high energy proton flux which started on 4 February of 0900 UT. Associated with the flares, the magnetic storm with sudden commencement had its onset on 6 February 1312 UT and attained its maximum on 8 February (Kp = 9). The sudden enhancement in solar activity in February 1986 was accompanied by strong disturbances in the Earth's ionosphere, SIDs and ionospheric storm. These events and their effects on the ionosphere are discussed.

Boska, J.↗
NASA NTRS1979

A new method of very long-term prediction of solar activity

A method of solar activity prediction based on the statistical relationship between the minimum value of geomagnetic activity in each 11 year cycle and the height of the next sunspot cycle is proposed. The method is applied to predict sunspot cycle 21.

Ohl, A. I.↗
NASA NTRS1988

Dynamo theory prediction of solar activity

The dynamo theory technique to predict decadal time scale solar activity variations is introduced. The technique was developed following puzzling correlations involved with geomagnetic precursors of solar activity. Based upon this, a dynamo theory method was developed to predict solar activity. The method was used successfully in solar cycle 21 by Schatten, Scherrer, Svalgaard, and Wilcox, after testing with 8 prior solar cycles. Schatten and Sofia used the technique to predict an exceptionally large cycle, peaking early (in 1990) with a sunspot value near 170, likely the second largest on record. Sunspot numbers are increasing, suggesting that: (1) a large cycle is developing, and (2) that the cycle may even surpass the largest cycle (19). A Sporer Butterfly method shows that the cycle can now be expected to peak in the latter half of 1989, consistent with an amplitude comparable to the value predicted near the last solar minimum.

Schatten, Kenneth H.↗
NASA NTRS1985

Simultaneous Solar Maximum Mission and Very Large Array (VLA) observations of solar active regions

Simultaneous observations of solar active regions with the Solar Maximum Mission (SMM) Satellite and the Very Large Array (VLA) have been obtained and analyzed. Combined results enhance the scientific return for beyond that expeted from using either SMM or VLA alone. A total of two weeks of simultaneous SMM/VLA data were obtained. The multiple wavelength VLA observations were used to determine the temperature and magnetic structure at different heights within coronal loops. These data are compared with simultaneous SMM observations. Several papers on the subject are in progress. They include VLA observations of compact, transient sources in the transition region; simultaneous SMM/VLA observations of the coronal loops in one active region and the evolution of another one; and sampling of the coronal plasma using thermal cyclotron lines (magnetic field - VLA) and soft X ray spectral lines (electron density and electron temperaure-SMM).

Lang, K. R.↗
NASA NTRS2019

Solar Activity Modeling: From Subgranular Dynamical Scales to the Solar Cycles

Dynamical effects of solar magnetoconvection span a wide range spatial and temporal scales that extends from the interior to the corona and from fast turbulent motions to the global-Sun magnetic activity. To study the solar activity on short temporal scales (from minutes to hours), we use 3D radiative MHD simulations that allow us to investigate complex turbulent interactions that drive various phenomena, such as plasma eruptions, spontaneous formation of magnetic structures, funnel-like structures and magnetic loops in the corona, and others. In particular, we focus on multi-scale processes of energy exchange across the different layers, which contribute to the corona heating and eruptive dynamics, as well as interlinks between different layers of the solar interior and atmosphere. For modeling the global-scale activity we use the data assimilation approach that has demonstrated great potential for building reliable long-term forecasts of solar activity. In particular, it has been shown that the Ensemble Kalman Filter (EnKF) method applied to the Parker-Kleeorin-Ruzmakin dynamo model is capable of predicting solar activity up to one sunspot cycle ahead in time, as well as estimating the properties of the next cycle a few years before it begins. In this presentation, using the available magnetogram data, we discuss development of the methodology and forecast quality criteria (including forecast uncertainties and sources of errors). We demonstrate the influence of observational limitation on the prediction accuracy. We present the EnKF predictions of the upcoming Solar Cycle 25 based on both the sunspot number series and observed magnetic fields, and discuss the uncertainties and potential of the data assimilation approach for modeling and forecasting the solar activity.

Kitiashvili, I. N.↗
NASA NTRS2019

Solar Activity Modeling: From Subgranular Dynamical Scales to the Solar Cycles

The dynamical effects of solar magnetoconvection span a wide range spatial and temporal scales that extend from the interior to the corona and from fast turbulent motions to global magnetic activity. To study the solar activity on short temporal scales (from minutes to hours), we use 3D radiative MHD simulations that allow us to investigate complex turbulent interactions that drive various phenomena, such as plasma eruptions, spontaneous formation of magnetic structures, funnel-like structures and magnetic loops in the corona, and others. In particular, we focus on multi-scale processes of energy exchange across layers of the solar interior and atmosphere, which contribute to coronal heating and eruptive dynamics. For modeling global-scale activity, we use a data assimilation approach that has demonstrated great potential for building reliable long-term forecasts of solar activity. In particular, it has been shown that the Ensemble Kalman Filter (EnKF) method applied to the Parker-Kleeorin-Ruzmakin dynamo model is capable of predicting solar activity up to one sunspot cycle ahead in time, as well as estimating the properties of the next cycle a few years before it begins. In this presentation, using the available magnetogram data, we discuss development of the methodology and forecast quality criteria (including forecast uncertainties and sources of errors). We demonstrate the influence of observational limitations on prediction accuracy, and we present the EnKF predictions of the upcoming Solar Cycle (25) based on both the sunspot number series and observed magnetic fields and discuss the uncertainties and potential of the data assimilation approach for modeling and forecasting solar activity.

Kitiashvili, I. N.↗
NASA NTRS2017

Using Data Assimilation Methods for Prediction of Solar Activity

The variability of solar magnetic activity known as the 11-­‐year solar cycles has the longest history of observations. These solar 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 the 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.

Solar Activity↗
NASA NTRS1990

Some Results from Studies on Relationships Between the Optical-meteorological Parameters and Solar Activity. Part 2: Development of the Problem of Solar Forcing

A set of complex spectral, actinometric and meteorological data obtained in the periods of heightened solar activity (1981 and 1988) has been considered in order to reveal the atmospheric component affected by solar emissions in the troposphere and lower stratosphere. For the first time, it has been found out that water vapor molecules can be transformed, under the impact of corpuscular and microwave solar emissions, from the free state to the bound one (association into clusters), and vice versa. The transition of water vapor molecules into the bound state results in a decrease of spectral optical thickness in the visible, near IR and IR spectral regions, and an appearance and deepening of the cluster absorption bands at wavelengths 330 to 340, 365, 380 to 390, and 480 nm.

Kondratyev, K. Y.↗
NASA NTRS1971

Models of the quiet and active solar atmosphere from Harvard OSO data.

Review of some Harvard Observatory programs aimed at defining the physical conditions in quiet and active solar regions on the basis of data obtained from the OSO-IV and OSO-VI spacecraft. The spectral range covered is from 300 A to 1400 A. This spectral range consists of emission lines and continua from abundant elements such as hydrogen, helium, carbon, nitrogen, oxygen, silicon, magnesium, aluminum, neon, iron, and calcium in various ionization states ranging from neutral to 15 times ionized. The structure is discussed of the quiet solar atmosphere as deduced from center-to-limb behavior of spectral lines and continua formed in the chromosphere and corona. In reviewing investigations of solar active regions, it is shown that the structure of these regions varies in a complicated manner from point to point. The local structure is influenced by factors such as the magnetic field configuration within the active region and the age or evolutionary state of the region.

Noyes, R. W.↗
NASA NTRS2001

Solar Activity Forecasting for use in Orbit Prediction

Orbital prediction for satellites in low Earth orbit (LEO) or low planetary orbit depends strongly on exospheric densities. Solar activity forecasting is important in orbital prediction, as the solar UV and EUV inflate the upper atmospheric layers of the Earth and planets, forming the exosphere in which satellites orbit. Geomagnetic effects also relate to solar activity. Because of the complex and ephemeral nature of solar activity, with different cycles varying in strength by more than 100%, many different forecasting techniques have been utilized. The methods range from purely numerical techniques (essentially curve fitting) to numerous oddball schemes, as well as a small subset, called 'Precursor techniques.' The situation can be puzzling, owing to the numerous methodologies involved, somewhat akin to the numerous ether theories near the turn of the last century. Nevertheless, the Precursor techniques alone have a physical basis, namely dynamo theory, which provides a physical explanation for why this subset seems to work. I discuss this solar cycle's predictions, as well as the Sun's observed activity. I also discuss the SODA (Solar Dynamo Amplitude) index, which provides the user with the ability to track the Sun's hidden, interior dynamo magnetic fields. As a result, one may then update solar activity predictions continuously, by monitoring the solar magnetic fields as they change throughout the solar cycle. This paper ends by providing a glimpse into what the next solar cycle (#24) portends.

Schatten, Kenneth↗
NASA NTRSDate not supplied

Global Solar Activity Data Portal for Studying 3D Dynamics and Activity of the Sun

The main problems with understanding and predicting solar activity are tightly linked to limitations in describing the global evolution of the Sun from the deep interior to the corona. Because of the complexity of interactions in a wide range of dynamical, turbulent, and spatial scales and dramatic changes in thermodynamic and magnetic field conditions, only physics-based models can provide essential background to generate reliable solar activity forecasts. However, performing accurate model calibration and estimating uncertainties is often challenging due to the unavailability of a long time series of observations. To mitigate these limitations, we have developed the Global Solar Activity (GSA) Data Portal, which enables convenient access to a variety of modern and historical data. The portal supports a dynamic visualization for 1D time series (such as the sunspot number and solar irradiance) and quick-look visualization for 2D datasets (e.g., synoptic magnetograms, the solar internal rotation, and flows). The GSA portal includes a search engine that enables data retrieval from user-specified data sources and time intervals. In this presentation, we will discuss the current and upcoming capabilities of the data portal and its potential applications for space weather studies.

SMD↗