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At least 1,639 records · Page 91
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NASA NTRS2013

Impacts of Atmospheric Ultrafast Kelvin Waves on Radio Scintillations in the Equatorial Ionosphere

We present a statistical analysis of the amplitudes of GPS scintillations (S4 index) observed throughout 2008–2010 using the satellite radio occultation measurements of the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC). Here, for the first time, periodic variability in the occurrence of S4 is investigated using these data. Significant variations of S4 with periods of 2.5-4 days (quasi-3 days) are identified from the observations during postsunset hours (1900-2400 local time) between 15°S-15°N magnetic latitude during this 3-year interval. Coherence analyses of these variations with the geomagnetic Ap index, solar EUV irradiance, and atmospheric wind measurements from an equatorial mesosphere meteor radar at Thumba, India (8.5°N, 77°E) are performed, providing a measure of the relationship between variations in the scintillations and potential drivers. The quasi-3-day variations in S4 are found to covary with the variations of the three drivers examined. In particular, the S4 signatures are found to be coherent with the atmospheric ultrafast Kelvin (UFK) planetary waves characterized by the zonal wind measurements of the radar. This study shows that these UFK waves are as important as the solar and geomagnetic drivers in forcing the day-to-day variations of the occurrence of equatorial spread F.

Ultra Fast Kelvin planetary wave↗
NASA NTRS2022

Determining Latitudinal Extent of Energetic Electron Precipitation Using MEPED On-Board NOAA/POES

Energetic Electron Precipitation (EEP) from the plasma sheet and the radiation belts ionizes the polar lower thermosphere and mesosphere. EEP increases the production of NO x and HO x , which will catalytically destroy ozone, an important element of atmospheric dynamics. Therefore, measurement of the latitudinal extent of the precipitation boundaries is important in quantifying the atmospheric effects of the Sun-Earth interaction. This study uses measurements by the Medium Energy Proton Electron Detector (MEPED) of six NOAA/POES and EUMETSAT/METOP satellites from 2004 to 2014 to determine the latitudinal boundaries of EEP and their variability with geomagnetic activity and solar wind drivers. Variation of the boundaries for different electron energies and Magnetic Local Time (MLT) is studied. Regression analyses are applied to determine the best predictor variable based on solar wind parameters and geomagnetic indices. The highest correlation was found for the pressure-corrected Dst index when applying a linear regression model. A model of the equatorward EEP boundary is developed separately for three different energy channels, >43, >114, and >292 keV, and for 3 hour MLT sectors. For >43 keV EEP, 80% of the equatorward boundaries predicted by the model are within ±2.2° cgmlat. The model exhibits a solar cycle bias where it systematically exaggerates the equatorward movement of the EEP region during solar minimum. The highest accuracy of the model is found in periods dominated by corotating interaction regions/high speed solar wind streams. The result will be a key element for constructing a model of EEP variability to be applied in atmosphere climate models.

E. M. Babu↗
NASA NTRS2021

Applying Machine Learning to Predict Alaskan Ionospheric Irregularities

In this work several machine-learning (ML) techniques for predicting ionospheric irregularities in the northern auroral zone were tested. The techniques include Ridge Regression, Long Short-Term Memory Neural Network (LSTM), Classification Neural Network (CNN), Autoencoder Classification Neural Network (ACNN), and LSTM Autoencoder Classification Neural Network (LACNN). These techniques were tested with the rate of total electron content (TEC) index (ROTI) data collected during 2008 and 2009 from a geodetic station in Fairbanks, Alaska (64.98°N, 147.50°W), which is in the auroral zone. Using ROTI data with the ML techniques, experiments were conducted to reach two goals: (1) examine what space weather measurements present good correlation with ROTI so that they may be helpful in ML-based prediction of ionospheric irregularities in the polar region; (2) predict ROTI hours and days ahead by training the neural network models with historical ROTI data alone. The Ridge Regression experiments indicate that a combination of measurements of local geomagnetic horizontal components, geomagnetic SYM-H index, 3-hour Kp and ap indices, and F10.7 solar flux index appears to be more correlated to the single-site ROTI measurements than other parameters. The neural network (NN) experiments show that although the LACNN model allows for predictions of non-irregularity and irregularity conditions defined by ROTI levels up to 3 hours in advance, with an overall accuracy ≥ 92%, a number of irregularity events can still be missed. Hence, further development is needed to reduce the number of missed events. In this paper, the models, data processing, model performance, prediction results, and potential applications are presented.

Pi, Xiaoqing↗
NASA NTRS2023

Probabilistic Forecasting of Ground Magnetic Perturbation Spikes at Mid-Latitude Stations

The prediction of large fluctuations in the ground magnetic field (dB/dt) is essential for preventing damage from Geomagnetically Induced Currents. Directly forecasting these fluctuations has proven difficult, but accurately determining the risk of extreme events can allow for the worst of the damage to be prevented. Here we trained Convolutional Neural Network models for eight mid-latitude magnetometers to predict the probability that dB/dt will exceed the 99th percentile threshold 30–60 min in the future. Two model frameworks were compared, a model trained using solar wind data from the Advanced Composition Explorer (ACE) satellite, and another model trained on both ACE and SuperMAG ground magnetometer data. The models were compared to examine if the addition of current ground magnetometer data significantly improved the forecasts of dB/dt in the future prediction window. A bootstrapping method was employed using a random split of the training and validation data to provide a measure of uncertainty in model predictions. The models were evaluated on the ground truth data during eight geomagnetic storms and a suite of evaluation metrics are presented. The models were also compared to a persistence model to ensure that the model using both datasets did not over-rely on dB/dt values in making its predictions. Overall, we find that the models using both the solar wind and ground magnetometer data had better metric scores than the solar wind only and persistence models, and was able to capture more spatially localized variations in the dB/dt threshold crossings.

Michael Coughlan↗
NASA NTRSDate not supplied

Temporal and Spatial Dynamics of Nitric Oxide Production at High Latitudes During an ICME-driven Storm on Dec. 14, 2006

During geomagnetic storms driven by interplanetary coronal mass ejections (ICMEs), Earth’s upper atmosphere neutral mass density is directly driven by the geomagnetic storm cycle from intense driving and subsequent recovery. The neutral density heating and cooling times are shorter for stronger storms and longer for weaker storms. The influx of energy (Joule Heating and precipitation) into Earth’s upper atmosphere during the storm main phase allows for the enhanced production of nitric oxide (NO) at high latitudes, which in turn cools the thermosphere by radiating away excess energy. As a result, greater NO production results in quicker thermospheric cooling. Yet while the production of NO on a global scale has been examined in detail, the space and time evolution of NO is not well-known. In this study, we investigate the effects of a particular ICME-driven storm, which occurred on Dec. 14th, 2006, on the production of NO at high latitudes. We compare NO measurements from the Thermosphere, Ionosphere, Mesosphere Dynamics (TIMED) spacecraft to neutral mass density measurements from the Challenging Minisatellite Payload (CHAMP) spacecraft and find that the impact of the shock prior to the storm, not just the onset of the storm itself, is responsible for an increase in NO production. We also observe a dawn-dusk asymmetry in high-latitude NO production and discuss how the geometry of the solar wind driver may play a role in creating this asymmetry. Finally, we discuss how the improved hemispheric and local time coverage provided by the GDC and DYNAMIC missions will enable more robust studies of the spatiotemporal evolution of NO during storms. Since NO is an important factor in regulating the neutral density of the thermosphere, such studies will be relevant for more accurate orbital drag calculations.

Kevin Delano↗
NASA NTRSDate not supplied

Impact of Solar Cycle Variation on Space Weather

We report on the solar cycle variation of solar eruptions and their space weather consequences during solar cycles 23-25. While the solar activity cycle is primarily expressed in terms of the Sunspot number (SSN), coronal mass ejections (CMEs) provide a different perspective on the solar cycle owing to their origin in sunspot regions as well as other non-spot magnetic regions such as quiescent filament regions. Weak solar activity observed in solar cycle 24 has been found to result in a weak heliospheric state, which backreacted on CME properties. The cycle resulted in mild space weather in that the number of large solar energetic particle events and geomagnetic storms decreased significantly. The occurrence of intense geomagnetic storms and high-energy SEP events was drastically reduced, much more than the reduction in the solar activity. The current status of solar cycle 25 indicates that it will be similar to solar cycle 24 or slightly stronger, which means a mild space weather in this cycle as well. We illustrate this using the data obtained during the rise phase of solar cycle 25 in comparison with the corresponding phases in cycles 23 and 24.

Nat Gopalswamy↗
NASA NTRSDate not supplied

Challenges in Understanding Radiation Belt Dynamics: Insights from Two Storm Periods

The periods of May 27 - June 5, 2017 and Oct 24 — 29, 2016 are 'unusual' in terms of radiation belt dynamics and their solar wind driving conditions. The first period was under the influence of a slow CME-led major geomagnetic storm with Dstmin = -125 nT and the second period was under high speed solar wind streams. Observations from Van Allen Probes show great variabilities in different electron energy channels for both periods. During the second period of Oct 24 - 29, 2016, electron fluxes are found to be near the highest upper limit among various storms during 2013–2018 (Hua, Bortnik and Ma, 2022). In this paper, we provide solar wind sources and geomagnetic conditions for these two storm periods and point out challenges in understanding, modeling, and forecasting radiation belt dynamics. In-depth analysis of modeling results utilizing radiation belt models available at the Community Coordinated Modeling Center such as VERB and CIMI will be performed. Initial modeling results indicate rather large discrepancies with the observations. Model validation using different metrics introduced in Zheng et al. (2019) will be carried out to gain a deeper understanding of the physical processes involved and to identity potential causes of modeling inadequacies.

Yihua Zheng↗
NASA NTRS2023

Editorial: Understanding the Causes of Asymmetries in Earth’S Magnetosphere-Ionosphere System

Geomagnetic activity observed in geospace, the upper atmosphere, and on the ground results from solar-terrestrial interactions. Such interactions correspond to the coupling between the solar wind, magnetosphere, and the thermosphere-ionosphere (MIT) system (Khazanov, 2016). However, given the complexity of the whole system and its large spatial scale and long-term solar variability, effects resulting from this coupling can be asymmetric. For example, inter-hemispherical asymmetric responses can arise when a hemisphere receives more energy than the other (e.g., Knipp et al., 2021; Pakhotin et al., 2021), local time effects can take place due to the occurrence of intense dawn-dusk interplanetary electric fields (e.g.,Haaland et al., 2017), and asymmetric geomagnetic field and mapping are caused by the Earth’s dipole offset and tilt (e.g., Laundal et al., 2017). The drivers that generate asymmetric MIT coupling response are generally recognized as long term: solar activity (Zhang et al., 2022) and dipole offset and tilt (Laundal et al., 2017); middle term: seasons (Lu et al., 2010); and short term: the y and z components of the interplanetary magnetic field (IMF) (Cowley, 1981; Li et al., 2011; Knipp et al., 2021). Thermospheric neutral mass density can present local time asymmetries associated with IMF By (Forster et al., 2017), and inter-hemispheric asymmetries can be generated by cross-hemispheric propagation of large-scale gravity waves (Bruinsma and Forbes, 2007). In addition, forcing from the mesosphere and lower thermosphere can generate inter-hemispheric neutral wind asymmetric patterns that can in turn asymmetrically impact neutral density in different hemispheres (Stober et al., 2021).

magnetosphere-ionophere coupling↗
NASA NTRS2024

A Floor in the Sun's Photospheric Magnetic Field: Implications for an Independent Small-scale Dynamo

Clette recently showed that F 10.7 systematically approaches a quiet Sun daily value of 67 solar flux units (sfu) at solar minima as the number of spotless days on the Sun increases. Previously, a floor of ∼2.8 nT had been proposed for the solar wind (SW) magnetic field strength (B). F 10.7 , which closely tracks the Sun's unsigned photospheric magnetic flux, and SW B exhibit different relationships to their floors at 11 yr solar minima during the last ∼50 yr. While F 10.7 approaches 67 sfu at each minimum, the corresponding SW B is offset above ∼2.8 nT by an amount approximately proportional to the solar polar field strength—which varied by a factor of ∼2.5 during this interval. This difference is substantiated by ∼130 yr of reconstructed F 10.7 (via the range of the diurnal variation of the East-component (rY) of the geomagnetic field) and SW B (based on the interdiurnal variability geomagnetic activity index). For the last ∼60 yr, the contribution of the slow SW to SW B has exhibited a floor-like behavior at ∼2 nT, in contrast to the contributions of coronal mass ejections and high-speed streams that vary with the solar cycle. These observations, as well as recent SW studies based on Parker Solar Probe and Solar Dynamics Observatory data, suggest that (1) the Sun has a small-scale turbulent dynamo that is independent of the 11 yr sunspot cycle; and (2) the small-scale magnetic fields generated by this nonvarying turbulent dynamo maintain a constant open flux carried to the heliosphere by the Sun's floor-like slow SW.

Solar magnetic fields↗
NASA NTRSDate not supplied

Evaluation of the Recent Improvements of the Nowcast of Aerospace Ionizing Radiation System (NAIRAS)

The Nowcast of Aerospace Ionizing Radiation System (NAIRAS) model is a real-time, global, physics-based model used to assess radiation exposure now running in real-time and in run on request (RoR) mode at NASA Goddard’s Community Coordinated Modeling Center. NAIRAS was recently updated to extend the galactic cosmic ray (GCR) model to include ultra-heavy nuclei (Z=29-92, A=64-238) for single event effects assessment from high linear energy transfer processes, to expand the geomagnetic cutoff rigidity model to use the either the TS05 (Tsyganenko and Sitnov, 2005), T89 (Tsyganenko, 1989), or the International Geomagnetic Reference Field model (IGRF) magnetic field models, and to improve the solar energetic particle (SEP) proton spectral fitting to better represent relativist protons during ground level enhancements. Here, we evaluate the recent NAIRAS improvements for a range of conditions. First, we demonstrate the effect of choice in magnetic field model to the NAIRAS computed dosimetric quantities for a United States domestic flight and a transatlantic flight during the May 11, 2024 SEP event. Second, the effect of ultra-heavy ions on SEP dose rate is examined for two different flight trajectories during the top SEP events. Lastly, the effect of ultra-heavy ions on NAIRAS computed GCR dose rates at solar minimum and maximum is demonstrated.

Daniel B Phoenix↗
NASA NTRS2024

The Global Mapping of Electron Precipitation and Ionospheric Conductance From Whistler-Mode Chorus Waves

Auroral precipitation is the second major energy source after solar irradiation that ionizes the Earth’s upper atmosphere. Diffuse electron aurora caused by wave-particle interaction in the inner magnetosphere (L < 8) takes over 60% of total auroral energy flux, strongly contributing to the ionospheric conductance and thus to the ionosphere-thermosphere dynamics. This paper quantifies the impact of chorus waves on the diffuse aurora and the ionospheric conductance during quiet, medium, and strong geomagnetic activities, parameterized by AE <100, 100 < AE < 300, and AE > 300, respectively. Using chorus wave statistics and inner-magnetosphere plasma conditions from Timed History Events and Macroscale Interactions during Substorms (THEMIS) observations, we directly derive the energy spectrum of diffuse electron precipitation under quasi-linear theory. We then calculate the height-integrated conductance from the wave-driven aurora spectrum using the electron impact ionization model of Fang et al. (Geophys. Res. Lett., 2010, 37) and the MSIS atmosphere model. By utilizing Fang’s ionization model, the US Naval Research Laboratory Mass Spectrometer and Incoherent Scattar Radar (NRLMSISE-00) model from 2000s for the neutral atmosphere components, and the University of California, Los Angeles (UCLA) Full Diffusion Code, we improve upon the standard generalization of Maxwellian diffuse electron precipitation patterns and their resulting ionosphere conductance. Our study of global auroral precipitation and ionospheric conductance from chorus wave statistics is the first statistical model of its kind. We show that the total electron flux and conductance pattern from our results agree with those of Ovation Prime model over the pre-midnight to post-dawn sector as geomagnetic activity increases. Our study examines the relative contributions of upper band chorus (UBC) and lower band chorus wave (LBC) driven conductance in the ionosphere. We found LBC waves drove diffuse electron precipitation significantly more than UBC waves, however it is possible that THEMIS data may have underestimated the upper chorus band wave observations for magnetic latitudes below 65 degrees. ° .

diffuse auroral precipitation↗
NASA NTRS1966

Models of the Earth's magnetic field.

Geomagnetic field model based on definition of core field, crustal field, ionospheric currents and plasma pressures, examining trapped particle behavior

GEOMAGNETIC FIELD↗
NASA NTRS1966

Magnetic coordinates.

Definitions and properties of magnetic coordinates B, L, K, R, lambda and A, discussing role of these parameters in study of geomagnetically trapped particles

GEOMAGNETICALLY TRAPPED PARTICLE↗
DOE OSTI2015

Penetration of magnetosonic waves into the plasmasphere observed by the Van Allen Probes

During the small storm on 14–15 April 2014, Van Allen Probe A measured a continuously distinct proton ring distribution and enhanced magnetosonic (MS) waves along its orbit outside the plasmapause. Inside the plasmasphere, strong MS waves were still present but the distinct proton ring distribution was falling steeply with distance. We adopt a sum of subtracted bi-Maxwellian components to model the observed proton ring distribution and simulate the wave trajectory and growth. MS waves at first propagate toward lower L shells outside the plasmasphere, with rapidly increasing path gains related to the continuous proton ring distribution. The waves then gradually cross the plasmapause into the deep plasmasphere, with almost unchanged path gains due to the falling proton ring distribution and higher ambient density. These results present the first report on how MS waves penetrate into the plasmasphere with the aid of the continuous proton ring distributions during weak geomagnetic activities.

58 GEOSCIENCES↗