Precipitation of relativistic electrons into the Earth's atmosphere regulates the outer radiation belt fluxes and contributes to magnetosphere-atmosphere coupling. One of the main drivers of such precipitation is electron scattering by whistler-mode waves. Such waves typically originate at the equator, where they can resonate with and scatter sub-relativistic (tens to a few hundred keV) electrons. However, they can occasionally propagate far away from the equator along field lines, reaching middle latitudes, where they can resonate with and scatter relativistic (>500 keV) electrons. Such a propagation is typical for the dayside, but statistically has not been found on the nightside where the waves are quickly damped along their propagation due to Landau damping. Here we explore two events of relativistic electron precipitation from low-altitude observations on the nightside. Combining measurements of whistler-mode waves from ground observatories, relativistic electron precipitation from low-altitude satellites, total electron content maps from GPS receivers, and magnetic field and electron flux from equatorial satellites, we show wave ducting by plasma density gradients is the possible channel that allows the waves to reach middle latitudes and scatter relativistic electrons. We suggest that both whistler-mode wave generation and ducting can be driven by equatorial mesoscale (with spatial scales of about one Earth radius) transient structures during nightside injections. We also compare these nightside events with observations of ducted waves and relativistic electron precipitation at the dayside, where wave generation and ducting are driven by ultra-low-frequency waves. This study demonstrates the potential importance of mesoscale transients in relativistic electron precipitation, but does not however unequivocally establish that ducted whistler-mode waves are the primary cause of the observed electron precipitation.

AbstractGlobal ionospheric total electron content (TEC) maps exhibit TEC intensifications and depletions of various sizes and shapes. Characterizing key features on TEC maps and understanding their dynamic coupling with external drivers can significantly benefit space weather forecasting. However, comprehensive analysis of ionospheric structuring over decades of TEC maps is currently lacking due to large data volume. We develop feature extraction software based on image processing techniques to extract TEC intensification regions, that is, contiguous regions with sufficiently elevated TEC values than surrounding areas, from global TEC maps. Applying the software to the Jet Propulsion Laboratory Global Ionospheric Map data, we generate a TEC intensification data set for years 2003–2022 and carry out a statistical study on the number and strength of TEC intensifications. We find that the majority of the TEC maps (about 86%) are characterized with one or two intensification(s), while the rest of the TEC maps have three or more intensifications. Both the number and strength of TEC intensifications exhibit semi‐annual variation that peaks near equinoxes and dips near solstices, as well as an annual asymmetry with larger values around December solstice compared to June solstice. The number and strength of intensifications increase with enhanced solar extreme‐violet irradiance. The strength of intensifications also increases with elevated geomagnetic activity, but the number of intensifications does not. In addition, the number of intensifications is not correlated with the strength of intensifications.

The International Muon Collider Collaboration (IMCC) [1] was established in 2020 following the recommendations of the European Strategy for Particle Physics (ESPP) and the implementation of the European Strategy for Particle Physics-Accelerator R D Roadmap by the Laboratory Directors Group [2], hereinafter referred to as the the European LDG roadmap. The Muon Collider Study (MuC) covers the accelerator complex, detectors and physics for a future muon collider. In 2023, European Commission support was obtained for a design study of a muon collider (MuCol) [3]. This project started on 1st March 2023, with work-packages aligned with the overall muon collider studies. In preparation of and during the 2021-22 U.S. Snowmass process, the muon collider project parameters, technical studies and physics performance studies were performed and presented in great detail. Recently, the P5 panel [4] in the U.S. recommended a muon collider R D, proposed to join the IMCC and envisages that the U.S. should prepare to host a muon collider, calling this their "muon shot". In the past, the U.S. Muon Accelerator Programme (MAP) [5] has been instrumental in studies of concepts and technologies for a muon collider.

The database of Global Ionospheric Maps (GIMs) produced at Jet Propulsion Laboratory is analyzed. We define high density total electron content (TEC) regions (HDRs) in a map, following certain selection criteria. For the first time, we trained four convolutional neural networks (CNNs) corresponding to four phases of a solar cycle to classify the GIMs by the number of HDRs in each map with similar to 80% accuracy on average. We compared HDR counts for GIMs across ten years to draw conclusions on how the number of HDRs in the GIMs changes throughout the solar cycle. Occurrence of HDRs during different geomagnetic activity conditions is discussed. Catalog of selected HDRs for ten years and four CNN-based models that can be used to extend classification to other years are provided for the community to use.

Global circulation models (GCMs) for the ionosphere‐thermosphere system traditionally use empirical models to specify upper boundary conditions to represent solar wind and magnetospheric drivers. However, the magnetosphere, ionosphere, and thermosphere systems are coupled on different spatial and temporal scales. During increased levels of geomagnetic activity, these empirical models can't resolve dynamic electric field variability ( 500 km, 15 min) because of their statistical nature and/or low spatial and temporal resolutions. This results in an underestimation of energy input to the ionosphere, causing disagreements between model results and observations. This paper introduces a new framework to incorporate dynamic electric fields into GCMs: High‐latitude Input for Mesoscale Electrodynamics (HIME). As a demonstration HIME uses the Poker Flat Incoherent Scatter Radar (PFISR) electric field estimates during an experiment on 2 March 2017. The electric potentials were calculated using the PFISR estimates and merged with a global empirical model of electric potential. A set of high‐latitude electric potential drivers were used to drive the University of Michigan Global Ionosphere Thermosphere Model (GITM) to understand the effects of driving at different scales. Data versus model comparisons for ion temperature, electron temperature, and electron density are provided along the PFISR beams. The ion convection velocities and neutral winds at the PFISR location are compared with the PFISR and Scanning Doppler Imager data. The effects of different multiscale drivers are investigated. The results showed that energy deposited by HIME‐driven simulations was locally larger by approximately an order of magnitude compared to the empirical model‐driven results.

AbstractPrediction of ionospheric state is a critical space weather problem. We expand on our previous research of medium‐range ionospheric forecasts and present new results on evaluating prediction capabilities of three physics‐based ionosphere‐thermosphere models (Thermosphere Ionosphere Electrodynamics General Circulation Model, TIE‐GCM; Coupled Thermosphere Ionosphere Plasmasphere Electrodynamics Model, CTIPe; and Global Ionosphere Thermosphere Model, GITM). The focus of our study is understanding how current modeling approaches may predict the global ionosphere for geomagnetic storms (as studied through 35 storms during 2000–2016). Prediction approach uses physics‐based modeling without any manual model adjustment, quality control, or selection of the results. Our goal is to understand to what extent current physics‐based modeling can be used in total electron content (TEC) prediction and explore uncertainties of these prediction efforts with multiday lead times. The ionosphere‐thermosphere model runs are driven by actual interplanetary conditions, whether those data come from real‐time measurements or predicted values themselves. These model runs were performed by the Community Coordinated Modeling Center (CCMC). Jet Propulsion Laboratory (JPL)‐produced global ionospheric maps (GIMs) were used to validate model TEC estimates. We utilize the True Skill Statistic (TSS) metric for the TEC prediction evaluation, noting that this is but one metric to assess predictive skill and that complete evaluations require combinations of such metrics. The meanings of contingency table elements for the prediction performance are analyzed in the context of ionosphere modeling. Prediction success is between about 0.2 and 0.5 for weak ionospheric disturbances and decreases for strong disturbances. We evaluate the prediction of TEC decreases and increases. Our results indicate that physics‐based modeling during storms shows promise in TEC prediction with multiday lead time.

The production cross-sections for W +/- and Z bosons are measured using ATLAS data corresponding to an integrated luminosity of 4.0 pb-1 collected at a centre-ofmass energy v s = 2.76 TeV. The deca ...

The Compact Linear Collider (CLIC) is a TeV-scale high-luminosity linear $e^+e^-$ collider under development at CERN. Following the CLIC conceptual design published in 2012, this report provides an overview of the CLIC project, its current status, and future developments. It presents the CLIC physics potential and reports on design, technology, and implementation aspects of the accelerator and the detector. CLIC is foreseen to be built and operated in stages, at centre-of-mass energies of 380 GeV, 1.5 TeV and 3 TeV, respectively. CLIC uses a two-beam acceleration scheme, in which 12 GHz accelerating structures are powered via a high-current drive beam. For the first stage, an alternative with X-band klystron powering is also considered. CLIC accelerator optimisation, technical developments and system tests have resulted in an increased energy efficiency (power around 170 MW) for the 380 GeV stage, together with a reduced cost estimate at the level of 6 billion CHF. The detector concept has been refined using improved software tools. Significant progress has been made on detector technology developments for the tracking and calorimetry systems. A wide range of CLIC physics studies has been conducted, both through full detector simulations and parametric studies, together providing a broad overview of the CLIC physics potential. Each of the three energy stages adds cornerstones of the full CLIC physics programme, such as Higgs width and couplings, top-quark properties, Higgs self-coupling, direct searches, and many precision electroweak measurements. The interpretation of the combined results gives crucial and accurate insight into new physics, largely complementary to LHC and HL-LHC. The construction of the first CLIC energy stage could start by 2026. First beams would be available by 2035, marking the beginning of a broad CLIC physics programme spanning 25-30 years.

To numerically simulate earthquake-induced ionospheric disturbances, we extend the Wave Perturbation-Global Ionosphere-Thermosphere Model, which was originally developed for tsunami-ionosphere coupling via gravity waves, to the case of earthquake-ionosphere coupling via acoustic-gravity waves. The new Wave Perturbation-Global Ionosphere-Thermosphere Model represents epicentral crustal movements by a point source specified with the ground motion data from seismic measurements. The model then solves for the neutral atmospheric perturbations generated by spherical acoustic-gravity waves and the resulting ionospheric plasma perturbations over the epicentral area. We apply the model to simulate the near-field ionospheric disturbances during two major earthquake events: the 2011 Tohoku-Oki, Japan, and the 2015 Illapel, Chile, events. To validate the results, we extract receiver-to-satellite total electron content perturbations from the simulations and compare them to the corresponding slant total electron content perturbations from Global Positioning System observations. We find good agreement on magnitudes and arrival times between the simulations and observations.

The ionosphere-thermosphere (IT) energy partitioning for the interplanetary coronal mass ejection (ICME) storms of 16-19 March 2013 and 2015 is estimated with the Global Ionosphere-Thermosphere Model (GITM), empirical models and proxies derived from in situ measurements. We focus on auroral heating, Joule heating, and thermospheric cooling. Solar wind data, F-10.7, OVATION Prime model and the Weimer 2005 model are used to drive GITM from above. Thermospheric nitric oxide and carbon dioxide cooling emission powers and fluxes are estimated from TIMED/SABER measurements. Assimilative mapping of ionospheric electrodynamics (AMIE) estimations of hemispheric power and Joule heating are presented, based on data from global magnetometers, the AMPERE magnetic field data, SSUSI auroral images, and the SuperDARN radar network. Modeled Joule heating and auroral heating of the IT system are mostly controlled by external driving in the March 2013 and 2015 storms, while NO cooling persists into the storm recovery phase. The total heating in the model is about 1000 GW to 3000 GW. Additionally, we intercompare contributions in selected energy channels for five coronal mass ejection-type storms modeled with GITM. Modeled auroral heating shows reasonable agreement with AMIE hemispheric power and is higher than other observational proxies. Joule heating and infrared cooling are likely underestimated in GITM. We discuss challenges and discrepancies in estimating and global modeling of the IT energy partitioning, especially Joule heating, during geomagnetic storms. Plain Language Summary Energy budget is an important characteristic of the ionosphere-thermosphere system. We utilize Global Ionosphere-Thermosphere Model runs for two strong recent geomagnetic storms, March 2013 and March 2015. In the paper we intercompare and evaluate different available sources for estimation of energy terms, including global modeling, empirical models, and direct satellite measurements. We discuss challenges and discrepancies in estimating and global modeling of the ionosphere-thermosphere energy partitioning, especially Joule heating, during geomagnetic storms. Understanding energy budget and improving its estimates are important for the space weather forecasting efforts.

Temperature anisotropy has been frequently observed in the solar corona and the solar wind, yet poorly represented in computational models of the solar wind. Therefore, we have included proton temperature anisotropy in our Alfven wave solar model (AWSoM). This model solves the magnetohydrodynamic equations augmented with low-frequency Alfven wave turbulence. The wave reflection due to Alfven speed gradient and field-aligned vorticity results in turbulent cascade. At the gyroradius scales, the apportioning of the turbulence dissipation into coronal heating of the protons and electrons is through stochastic heating. This paper focuses on the impacts of the proton temperature anisotropy on the solar wind. We apply AWSoM to simulate the steady solar wind from the corona to 1AU using synoptic magnetograms. The Alfven wave energy density at the inner boundary is prescribed with a uniform Poynting flux per field strength. We present the proton temperature anisotropy distribution, and investigate the firehose instability in the heliosphere from our simulations. In particular, the comparisons between the simulated and observed solar wind properties at 1AU during the ramping-up phase and the maximum of solar cycle 24 imply the importance of addressing the proton temperature anisotropy in solar wind modelling to capture the fast solar wind speed.

Scientific knowledge is acquired in geophysics generally without the benefit of controlled experiments. In this paper, we discuss how scientific inference based on observations occurs in geophysical contexts. We develop a specific approach that uses approximate simultaneity of proposed cause and effect phenomena to infer causality. The approach applies equally well to effect phenomena that follow the cause with a known time delay. We find that, in general, establishing a causal relationship between two phenomena based on simultaneity requires knowledge of how often simultaneity of these phenomena occurs in the absence of causality. We then extend the discussion to using numerical simulations in the scientific inference process. Numerical simulations of physical processes, because they can simulate the values of observations, are often used to infer what physical processes are occurring in nature. We discuss agreement between model output and observations as a basis for inferring the physical processes underlying the observations. We find that an important factor to consider, which we here call the “confusion factor,” is how often it may occur that insufficient model representations of the physical processes nevertheless lead to agreement between model computations and observations. We suggest that models of intermediate or low complexity may have a significant role to play when using geophysical simulations to reach scientific conclusions.

Space WeatherVolume 13, Issue 3 p. 125-129 Feature ArticleFree Access Medium-Range Thermosphere-Ionosphere Storm Forecasts A. J. Mannucci, Corresponding Author A. J. Mannucci orcid.org/0000-0003-2391-8490 Search for more papers by this authorO. P. Verkhoglyadova, O. P. VerkhoglyadovaSearch for more papers by this authorB. T. Tsurutani, B. T. TsurutaniSearch for more papers by this authorX. Meng, X. MengSearch for more papers by this authorX. Pi, X. PiSearch for more papers by this authorC. Wang, C. WangSearch for more papers by this authorG. Rosen, G. RosenSearch for more papers by this authorE. Lynch, E. LynchSearch for more papers by this authorS. Sharma, S. SharmaSearch for more papers by this authorA. Ridley, A. RidleySearch for more papers by this authorW. Manchester, W. ManchesterSearch for more papers by this authorB. Van Der Holst, B. Van Der HolstSearch for more papers by this authorE. Echer, E. EcherSearch for more papers by this authorR. Hajra, R. HajraSearch for more papers by this author A. J. Mannucci, Corresponding Author A. J. Mannucci orcid.org/0000-0003-2391-8490 Search for more papers by this authorO. P. Verkhoglyadova, O. P. VerkhoglyadovaSearch for more papers by this authorB. T. Tsurutani, B. T. TsurutaniSearch for more papers by this authorX. Meng, X. MengSearch for more papers by this authorX. Pi, X. PiSearch for more papers by this authorC. Wang, C. WangSearch for more papers by this authorG. Rosen, G. RosenSearch for more papers by this authorE. Lynch, E. LynchSearch for more papers by this authorS. Sharma, S. SharmaSearch for more papers by this authorA. Ridley, A. RidleySearch for more papers by this authorW. Manchester, W. ManchesterSearch for more papers by this authorB. Van Der Holst, B. Van Der HolstSearch for more papers by this authorE. Echer, E. EcherSearch for more papers by this authorR. Hajra, R. HajraSearch for more papers by this author First published: 03 February 2015 https://doi.org/10.1002/2014SW001125Citations: 18AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Introduction The development of quantitative models that describe physical processes from the solar corona to the Earth's upper atmosphere creates the possibility of numerical space weather forecasting with a lead time of a few days [Merkin et al., 2007; Tóth et al., 2007]. Developing such a capability for the thermosphere and ionosphere is the objective of an effort described here sponsored by the NASA/National Science Foundation (NSF) Partnership for Collaborative Space Weather Modeling [Schunk, 2014]. Despite significant scientific progress in Sun-to-Earth modeling over the last few years, there is currently no system in place that relies on the physics-based model development of the past 10 years, to forecast moderate to intense upper atmosphere storms caused by solar wind disturbances. Mannucci [2012] suggests that a physics-based approach to forecasting upper atmospheric space weather has scientific as well as practical benefits. Despite the initiation of operational approaches to predicting space weather disturbances due to coronal mass ejections (CMEs) [Tobiska et al., 2013], predicting storm intensity with a few days' lead time remains a significant challenge. Predicting the magnitude and orientation of the interplanetary magnetic field component Bz at Earth, which determines storm intensity, is an active research topic [Zheng et al., 2013]. Geospace storms are also caused by high-speed streams (HSSs) in the solar wind, emanating from coronal holes. The arrival of HSSs and their associated corotating interaction regions (CIRs), where the high-speed flow meets the slower ambient flow, can be forecast also with lead times of 2–4 days and possibly longer [Norquist, 2013]. For HSSs, there is no clear "eruptive" signature as there is for CMEs [Tsurutani et al., 2006]. However, the persistence of coronal holes and the associated fast wind allows their impact to be predicted with the solar rotation period, which provides a more straightforward predictive capability compared to CMEs. Challenges in forecasting the solar wind speed and the magnitude and orientation of the interplanetary magnetic field remain for HSS, but the heliospheric modeling for HSS is more straightforward than for CMEs [van der Holst et al., 2010]. Space Weather and the Global Thermosphere-Ionosphere Storm Global thermosphere-ionosphere storms are highly disruptive to numerous technologies, as well as being important subjects of scientific study in their own right [Mendillo, 2006]. Storm impacts include increased satellite drag and impairment to communications, navigation, and radar systems due to ionospheric perturbations [Datta-Barua et al., 2014; Lechtenberg et al., 2013; Tobiska et al., 2013]. To illustrate an early effort in this Collaborative Space Weather Modeling Partnership, we focus on ionospheric total electron content (TEC) during HSS-driven storms and differences from the preceding quiet period in response to solar wind driving. The profound upper atmosphere changes that occur during storms are due to several factors that act simultaneously and affect each other: (1) momentum input from the solar wind creates large-scale changes to high-latitude ionospheric plasma convection, redefining the boundary between quiescent middle latitudes and the strongly convecting higher latitudes; (2) ionospheric electric fields of magnetospheric origin cause global-scale changes to plasma transport processes and plasma structure; (3) frictional heating between convecting ions and neutrals and (4) heating from magnetospheric particle precipitation create large-scale changes to the thermosphere composition, density, and circulation (winds). Ionization from precipitating particles locally affects the high-latitude electron density profile. Thermosphere circulation changes also have electrodynamic impact via the neutral wind dynamo mechanism. These upper atmospheric consequences are represented in first-principles coupled models of the magnetosphere-thermosphere-ionosphere, which is why forecasting these phenomena can be attempted. Examples of the steps required for an upper atmosphere forecast can be found in Merkin et al. [2007] and Tóth et al. [2007]. Such forecasts begin with solar observations that provide information on the solar origin of the resulting storm, for example, the detection of a coronal mass ejection that hurls hot magnetized plasma earthward, for eventual interaction with Earth's magnetosphere if the CME path intersects Earth. Such data are useful to specify the CME's speed and direction of propagation within a model of the Sun's corona [e.g., Manchester et al., 2014], which then provides inputs to a model that calculates resulting changes to the magnetized plasma in the heliosphere. Propagation time of the disturbance through the heliosphere from Sun to Earth typically lasts 1–4 days for CMEs. Solar wind conditions at Earth, as predicted by the heliospheric model, are used to drive physics-based models of near-Earth space, consisting of a global magnetosphere model and models of solar wind-magnetosphere-ionosphere interaction. This provides drivers for models of the upper atmosphere, such as the global ionosphere thermosphere model (GITM) [Ridley et al., 2006], which are then used to estimate upper atmospheric conditions. Storm impacts are calculated from the model output, such as increased satellite drag and impairments to communications, navigation, and radar systems, due to ionospheric perturbations. Similar considerations apply for storms caused by high-speed streams in the solar wind. However, forecasting the onset times of upper atmospheric disturbances due to high-speed streams could reach higher levels of accuracy sooner than for CMEs. Physics-based heliospheric modeling shows promise for reproducing the impacts of coronal holes where HSSs originate [van der Holst et al., 2010]. Predictable empirical relationships between heliospheric and upper atmosphere conditions for HSS are being realized in recent research [McGranaghan et al., 2014; Sojka et al., 2014]. Using Data-Driven Models to Improve Forecasts Where Needed Models that span distances from Sun to Earth (~1.5 × 108 km) do not have the spatial resolution to resolve structures in the solar wind causing hourly variability at Earth. Fluctuations in the solar wind on subhourly time scales are relevant for the response of the thermosphere and ionosphere [Solomon et al., 2012]. We are investigating the use of data-driven models based on dynamical systems theory, for which forecasts are generated based on time series of heliophysical variables such as solar wind parameters or geomagnetic indices that correlate with driving the upper atmosphere. These time series capture subhourly variability that is under resolved in the large-scale heliosphere and magnetosphere physics-based models. We are investigating techniques that reconstruct the dynamical behavior of the system from time series data alone, independent of modeling assumptions, yielding predictions. State space reconstruction techniques [Sharma, 1995] yield multidimensional data from a single time series. An integrative approach that uses these techniques with the physics-based modeling will be adapted for the thermosphere-ionosphere system. Using dynamical systems theory in upper atmosphere forecasts is a new approach that is distinct from approaches based on empirical models [e.g., Tobiska et al., 2013] or downscaling using additive noise [Owens et al., 2014]. Two High-Speed Stream Storms: Similarities and Differences The initial focus of the project is forecasting thermosphere-ionosphere disturbances due to solar wind high-speed streams (HSSs) and associated corotating interaction regions (CIRs) [Verkhoglyadova et al., 2013]. HSS is a persistent global-scale feature of the quiet Sun that emanates continuously from coronal holes. These fast wind patterns can be predicted with a variety of techniques that rely upon synoptic magnetograms to model the global structure of the coronal magnetic field [van der Holst et al., 2010]. Models of the evolving heliospheric structure predict the periodic impact of HSSs at Earth (modulated by solar rotation), which permits a predictive capability with a lead time of a few days. Due to the persistence of coronal holes, which can last for weeks, lead times for HSSs can be longer than for CMEs. Nature has provided two useful events occurring in 2011 and 2012 that highlight the upper atmosphere forecasting challenge. Solar wind parameters for these two events, both occurring in the April–May season, are shown in Figure 1 derived from the ACE or WIND spacecraft and time shifted to account for travel time to Earth's magnetopause (data available at http://cdaweb.gsfc.nasa.gov). The solar wind time history of these two interactions is largely similar, at least as recorded at the spacecraft. The 2012 period is associated with more solar active regions, a more highly distorted heliospheric current sheet, and somewhat higher solar flux than 2011 (F10.7 of 130 versus 111 for 2012 and 2011, respectively). Figure 1Open in figure viewerPowerPoint Two high-speed streams in the solar wind occurring during the ascending phase of solar cycle 24. The following question is relevant to forecasting space weather in the ionosphere-thermosphere system: is the response of the upper atmosphere similar, given that these storms occurred at similar times of year and at similar (though not identical) phases of the solar cycle? We address this question using ground-based TEC data from the GPS global network [Mannucci et al., 1998]. These data are combined into global ionospheric maps (GIMs), which are space and time interpolations of the ground-based TEC data. Using GIM, in Figure 2 we plot global TEC fractional differences between the HSS period and an average of five quiet days preceding each event. The fractional TEC difference at each location is the TEC difference between storm time and quiet time, divided by the corresponding quiet time TEC. These difference maps are meant to approximate the global ionospheric response to the evolving geomagnetic storm. A snapshot of fractional TEC differences at similar storm phases is shown in Figure 2. At the latitudes north of 30°S, similarities are found: in both storms, fractional TEC increases are observed in broad midlatitude bands (orange to red). The largest increases occur in the North American sector. It is also clear that the fractional response in 2011 is larger, particularly over North America. The southern high-latitude increase is quite significant and largely similar between the two events. We note that the fractional difference method can cause an apparent extreme amplification of differences in regions where the background values are low during quiet times (e.g., the white regions in the Southern Hemisphere in Figure 2). Figure 2Open in figure viewerPowerPoint Percent differences in global TEC estimates at comparable phases of the two storms (2011 and 2012). White areas indicate percentage differences above 100%. Global coupled models, such as the global ionosphere thermosphere model (GITM) [Ridley et al., 2006], available at http://csem.engin.umich.edu/tools/swmf, and the thermosphere ionosphere electrodynamics general circulation model, available at http://www.hao.ucar.edu [Roble et al., 1988; Richmond et al., 1992], are being run for these periods to assess how they capture these ionospheric "weather patterns," driven primarily by the solar wind parameters shown in Figure 2. The models will also be run using solar wind forecasts to assess how solar wind forecast errors affect the thermosphere-ionosphere response. Subgrid-scale fluctuations will be added to drivers of the response based on the dynamical systems theory approach we are developing. Conclusions Building on nearly two decades of focused space weather research, the scientific community is on the threshold of a new era in space weather forecasting, and scientific understanding of the connected Sun-Earth system, enabled in part by the development of physics-based modeling capabilities that span the solar corona to the Earth's upper atmosphere (thermosphere and ionosphere). Combining comprehensive observations and global models, a system to forecast thermosphere and ionosphere space weather is being developed under the NASA/NSF Partnership for Collaborative Space Weather Modeling [Schunk, 2014]. The focus of this project is on ionospheric total electron content during storms and differences from the preceding quiet period in response to solar wind driving. This requires detailed understanding of the physical processes that cause changes in the thermosphere and ionosphere and how these processes are represented in global models. Achieving forecast lead times of a few days will require accurate solar wind forecasts. This appears to be tractable for high-speed streams and the corotating interaction regions that accompany them, using synoptic maps of the coronal magnetic fields as input to magnetohydrodynamic models of the solar wind. Upper atmosphere research shows that solar wind variability that is subgrid scale with respect to heliospheric models can have significant impacts. Therefore, a portion of this modeling project is devoted to dynamical systems theory as a means of developing data-driven methods that accurately forecast time series of geospace parameters that are overly smoothed by global models. Acknowledgments Portions of the research for this paper were performed at the Jet Propulsion Laboratory, California Institute of Technology under contract with NASA. 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Tsunamis can generate gravity waves propagating upward through the atmosphere, inducing total electron content (TEC) disturbances in the ionosphere. To capture this process, we have implemented tsunami-generated gravity waves into the Global Ionosphere-Thermosphere Model (GITM) to construct a three-dimensional physics-based model WP (Wave Perturbation)-GITM. WP-GITM takes tsunami wave properties, including the wave height, wave period, wavelength, and propagation direction, as inputs and time-dependently characterizes the responses of the upper atmosphere between 100km and 600km altitudes. We apply WP-GITM to simulate the ionosphere above the West Coast of the United States around the time when the tsunami associated with the March 2011 Tohuku-Oki earthquke arrived. The simulated TEC perturbations agree with Global Positioning System observations reasonably well. For the first time, a fully self-consistent and physics-based model has reproduced the GPS-observed traveling ionospheric signatures of an actual tsunami event.

Natural hazards and solid Earth events, such as earthquakes, tsunamis and volcanic eruptions are actual sources that may trigger acoustic and gravity waves resulting in traveling ionospheric disturbances (TIDs) in the upper atmosphere. Trans-ionospheric radio wave measurements sense the total electron content (TEC) along the signal propagation path. In this research, we introduce a novel GPS-based detection and estimation technique for remote sensing of atmospheric wave-induced TIDs including space weather phenomena induced by major natural hazard events, using TEC time series collected from worldwide ground-based dual-frequency GNSS (including GPS) receiver networks.We demonstrate the ability of using ground-and space-based dual-frequency GPS measurements to detect and monitor tsunami wave propagation from the 2011 Tohoku-Oki earthquake and tsunami. Major wave trains with different propagation speeds and wavelengths were identified through analysis of the GPS remote sensing observations. Dominant physical characteristics of atmospheric wave-induced TIDs are found to be associated with specific tsunami propagations and oceanic Rayleigh waves. In this research, we focus on the analysis of tsunami-induced TIDs observed from the U.S. west coast. We compared GPS-based observations, corresponding model simulations and tsunami wave propagation. Results are shown to lead to a better understanding of the tsunami-induced ionosphere responses. Based on current distribution of Plate Boundary Observatory GPS stations, the results indicate that tsunami-induced TIDs may be detected about 60 minutes prior to tsunamis arriving at the U.S. west coast. It is expected that this GNSS-based technology will become an integral part of future early-warning systems.

The jet fragmentation function of inclusive jets with transverse momentum ${p}_{\mathrm{T}}$ above $100\phantom{\rule{0.28em}{0ex}}\text{GeV}/c$ in PbPb collisions has been measured using reconstructed charged particles with ${p}_{\mathrm{T}}$ above $1\phantom{\rule{0.28em}{0ex}}\text{GeV}/c$ in a cone of radius 0.3 around the jet axis. A data sample of PbPb collisions collected in 2011 at a nucleon-nucleon center-of-mass energy of $\sqrt{{s}_{{}_{\mathit{NN}}}}=2.76\phantom{\rule{0.28em}{0ex}}\text{TeV}$ corresponding to an integrated luminosity of $150\phantom{\rule{0.28em}{0ex}}\ensuremath{\mu}{\mathrm{b}}^{\ensuremath{-}1}$ is used. The results for PbPb collisions as a function of collision centrality and jet transverse momentum are compared to reference distributions based on $\mathit{pp}$ data collected at the same center-of-mass energy in 2013, with an integrated luminosity of $5.3\phantom{\rule{0.28em}{0ex}}{\text{pb}}^{\ensuremath{-}\text{1}}$. A centrality-dependent modification of the fragmentation function is found. For the most central collisions, a significant enhancement is observed in the PbPb/$\mathit{pp}$ fragmentation function ratio for charged particles with ${p}_{\mathrm{T}}$ less than $3\phantom{\rule{0.28em}{0ex}}\text{GeV}/c$. This enhancement is observed for all jet ${p}_{\mathrm{T}}$ bins studied.

We present a new version of the Alfven Wave Solar Model (AWSoM), a global model from the upper chromosphere to the corona and the heliosphere. The coronal heating and solar wind acceleration are addressed with low-frequency Alfven wave turbulence. The injection of Alfven wave energy at the inner boundary is such that the Poynting flux is proportional to the magnetic field strength. The three-dimensional magnetic field topology is simulated using data from photospheric magnetic field measurements. This model does not impose open-closed magnetic field boundaries; those develop self-consistently. The physics includes: (1) The model employs three different temperatures, namely the isotropic electron temperature and the parallel and perpendicular ion temperatures. The firehose, mirror, and ion-cyclotron instabilities due to the developing ion temperature anisotropy are accounted for. (2) The Alfven waves are partially reflected by the Alfven speed gradient and the vorticity along the field lines. The resulting counter-propagating waves are responsible for the nonlinear turbulent cascade. The balanced turbulence due to uncorrelated waves near the apex of the closed field lines and the resulting elevated temperatures are addressed. (3) To apportion the wave dissipation to the three temperatures, we employ the results of the theories of linear wave damping and nonlinear stochastic heating. (4) We have incorporated the collisional and collisionless electron heat conduction. We compare the simulated multi-wavelength EUV images of CR2107 with the observations from STEREO/EUVI and SDO/AIA instruments. We demonstrate that the reflection due to strong magnetic fields in proximity of active regions intensifies the dissipation and observable emission sufficiently.