The floor in the interplanetary magnetic field: Estimation on the basis of relative duration of ICME observations in solar wind during 1976-2000

To measure the floor in interplanetary magnetic field and estimate the time- invariant open magnetic flux of Sun, it is necessary to know a part of magnetic field of Sun carried away by CMEs. In contrast with previous papers, we did not use global solar parameters: we identified different large-scale types of solar wind for 1976-2000 interval, obtained a fraction of interplanetary CMEs (ICMEs) and calculated magnitude of interplanetary magnetic field B averaged over 2 Carrington rotations. The floor of magnetic field is estimated as B value at solar cycle minimum when the ICMEs were not observed and it was calculated to be 4,65 \pm 6,0 nT. Obtained value is in a good agreement with previous results.Comment: 10 pages, 2 figures, submitted in GR

Where the wild things are! Do urban green spaces with greater avian biodiversity promote more positive emotions in humans?

Urban green space can help mitigate the negative impacts of urban living and provide positive effects on citizens’ mood, health and well-being. Questions remain, however, as to whether all types of green space are equally beneficial, and if not, what landscape forms or key features optimise the desired benefits. For example, it has been cited that urban landscapes rich in wildlife (high biodiversity) may promote more positive emotions and enhance well-being. This research utilised a mobile phone App, employed to assess people’s emotions when they entered any one of 945 green spaces within the city of Sheffield, UK. Emotional responses were correlated to key traits of the individual green spaces, including levels of biodiversity the participant perceived around them. For a subsample of these green spaces, actual levels of biodiversity were assessed through avian and habitat surveys. Results demonstrated strong correlations between levels of avian biodiversity within a green space and human emotional response to that space. Respondents reported being happier in sites with greater avian biodiversity (p < 0.01, r = 0.78) and a greater variety of habitats (p < 0.02, r = 0.72). Relationships were strengthened when emotions were linked to perceptions of overall biodiversity (p < 0.001, r = 0.89). So, when participants thought the site was wildlife rich, they reported more positive emotions, even when actual avian biodiversity levels were not necessarily enhanced. The data strengthens the arguments that nature enhances well-being through positive affect, and that increased ‘engagement with nature’ may help support human health within urban environments. The results have strong implications for city planning with respect to the design, management and use of city green spaces

The variation of geomagnetic storm duration with intensity

Variability in the near-Earth solar wind conditions can adversely affect a number of ground- and space-based technologies. Such space-weather impacts on ground infrastructure are expected to increase primarily with geomagnetic storm intensity, but also storm duration, through time-integrated effects. Forecasting storm duration is also necessary for scheduling the resumption of safe operating of affected infrastructure. It is therefore important to understand the degree to which storm intensity and duration are correlated. The long-running, global geomagnetic disturbance index, aa , has recently been recalibrated to account for the geographic distribution of the component stations. We use this aaH index to analyse the relationship between geomagnetic storm intensity and storm duration over the past 150 years, further adding to our understanding of the climatology of geomagnetic activity. Defining storms using a peak-above-threshold approach, we find that more intense storms have longer durations, as expected, though the relationship is nonlinear. The distribution of durations for a given intensity is found to be approximately log-normal. On this basis, we provide a method to probabilistically predict storm duration given peak intensity, and test this against the aaH dataset. By considering the average profile of storms with a superposed-epoch analysis, we show that activity becomes less recurrent on the 27-day timescale with increasing intensity. This change in the dominant physical driver, and hence average profile, of geomagnetic activity with increasing threshold is likely the reason for the nonlinear behaviour of storm duration

Embodied Knowledge: Writing Researchers’ Bodies Into Qualitative Health Research

After more than a decade of postpositivist health care research and an increase in narrative writing practices, social scientific, qualitative health research remains largely disembodied. The erasure of researchers’ bodies from conventional accounts of research obscures the complexities of knowledge production and yields deceptively tidy accounts of research. Qualitative health research could benefit significantly from embodied writing that explores the discursive relationship between the body and the self and the semantic challenges of writing the body by incorporating bodily details and experiences into research accounts. Researchers can represent their bodies by incorporating autoethnographic narratives, drawing on all of their senses, interrogating the connections between their bodily signifiers and research processes, and experimenting with the semantics of self and body. The author illustrates opportunities for embodiment with excerpts from an ethnography of a geriatric oncology team and explores implications of embodied writing for the practice of qualitative health research

Metrics for solar wind prediction models: Comparison of empirical, hybrid, and physics-based schemes with 8 years of L1 observations

Space weather effects on technological systems originate with energy carried from the Sun to the terrestrial environment by the solar wind. In this study, we present results of modeling of solar corona-heliosphere processes to predict solar wind conditions at the L1 Lagrangian point upstream of Earth. In particular we calculate performance metrics for (1) empirical, (2) hybrid empirical/physics-based, and (3) full physics-based coupled corona-heliosphere models over an 8-year period (1995–2002). L1 measurements of the radial solar wind speed are the primary basis for validation of the coronal and heliosphere models studied, though other solar wind parameters are also considered. The models are from the Center for Integrated Space-Weather Modeling (CISM) which has developed a coupled model of the whole Sun-to-Earth system, from the solar photosphere to the terrestrial thermosphere. Simple point-by-point analysis techniques, such as mean-square-error and correlation coefficients, indicate that the empirical coronal-heliosphere model currently gives the best forecast of solar wind speed at 1 AU. A more detailed analysis shows that errors in the physics-based models are predominately the result of small timing offsets to solar wind structures and that the large-scale features of the solar wind are actually well modeled. We suggest that additional “tuning” of the coupling between the coronal and heliosphere models could lead to a significant improvement of their accuracy. Furthermore, we note that the physics-based models accurately capture dynamic effects at solar wind stream interaction regions, such as magnetic field compression, flow deflection, and density buildup, which the empirical scheme cannot

Probabilistic solar wind and geomagnetic forecasting using an analogue ensemble or "Similar Day" approach

Effective space-weather prediction and mitigation requires accurate forecasting of near-Earth solar-wind conditions. Numerical magnetohydrodynamic models of the solar wind, driven by remote solar observations, are gaining skill at forecasting the large-scale solar-wind features that give rise to near-Earth variations over days and weeks. There remains a need for accurate short-term (hours to days) solar-wind forecasts, however. In this study we investigate the analogue ensemble (AnEn), or “similar day”, approach that was developed for atmospheric weather forecasting. The central premise of the AnEn is that past variations that are analogous or similar to current conditions can be used to provide a good estimate of future variations. By considering an ensemble of past analogues, the AnEn forecast is inherently probabilistic and provides a measure of the forecast uncertainty. We show that forecasts of solar-wind speed can be improved by considering both speed and density when determining past analogues, whereas forecasts of the out-of-ecliptic magnetic field [ BNBN ] are improved by also considering the in-ecliptic magnetic-field components. In general, the best forecasts are found by considering only the previous 6 – 12 hours of observations. Using these parameters, the AnEn provides a valuable probabilistic forecast for solar-wind speed, density, and in-ecliptic magnetic field over lead times from a few hours to around four days. For BNBN , which is central to space-weather disturbance, the AnEn only provides a valuable forecast out to around six to seven hours. As the inherent predictability of this parameter is low, this is still likely a marked improvement over other forecast methods. We also investigate the use of the AnEn in forecasting geomagnetic indices Dst and Kp. The AnEn provides a valuable probabilistic forecast of both indices out to around four days. We outline a number of future improvements to AnEn forecasts of near-Earth solar-wind and geomagnetic conditions

Effect of Solar Wind Drag on the Determination of the Properties of Coronal Mass Ejections from Heliospheric Images

The Fixed-\Phi (F\Phi) and Harmonic Mean (HM) fitting methods are two methods to determine the average direction and velocity of coronal mass ejections (CMEs) from time-elongation tracks produced by Heliospheric Imagers (HIs), such as the HIs onboard the STEREO spacecraft. Both methods assume a constant velocity in their descriptions of the time-elongation profiles of CMEs, which are used to fit the observed time-elongation data. Here, we analyze the effect of aerodynamic drag on CMEs propagating through interplanetary space, and how this drag affects the result of the F\Phi and HM fitting methods. A simple drag model is used to analytically construct time-elongation profiles which are then fitted with the two methods. It is found that higher angles and velocities give rise to greater error in both methods, reaching errors in the direction of propagation of up to 15 deg and 30 deg for the F\Phi and HM fitting methods, respectively. This is due to the physical accelerations of the CMEs being interpreted as geometrical accelerations by the fitting methods. Because of the geometrical definition of the HM fitting method, it is affected by the acceleration more greatly than the F\Phi fitting method. Overall, we find that both techniques overestimate the initial (and final) velocity and direction for fast CMEs propagating beyond 90 deg from the Sun-spacecraft line, meaning that arrival times at 1 AU would be predicted early (by up to 12 hours). We also find that the direction and arrival time of a wide and decelerating CME can be better reproduced by the F\Phi due to the cancellation of two errors: neglecting the CME width and neglecting the CME deceleration. Overall, the inaccuracies of the two fitting methods are expected to play an important role in the prediction of CME hit and arrival times as we head towards solar maximum and the STEREO spacecraft further move behind the Sun.Comment: Solar Physics, Online First, 17 page

Ensemble downscaling in coupled solar wind-magnetosphere modeling for space weather forecasting

Advanced forecasting of space weather requires simulation of the whole Sun-to-Earth system, which necessitates driving magnetospheric models with the outputs from solar wind models. This presents a fundamental difficulty, as the magnetosphere is sensitive to both large-scale solar wind structures, which can be captured by solar wind models, and small-scale solar wind “noise,” which is far below typical solar wind model resolution and results primarily from stochastic processes. Following similar approaches in terrestrial climate modeling, we propose statistical “downscaling” of solar wind model results prior to their use as input to a magnetospheric model. As magnetospheric response can be highly nonlinear, this is preferable to downscaling the results of magnetospheric modeling. To demonstrate the benefit of this approach, we first approximate solar wind model output by smoothing solar wind observations with an 8 h filter, then add small-scale structure back in through the addition of random noise with the observed spectral characteristics. Here we use a very simple parameterization of noise based upon the observed probability distribution functions of solar wind parameters, but more sophisticated methods will be developed in the future. An ensemble of results from the simple downscaling scheme are tested using a model-independent method and shown to add value to the magnetospheric forecast, both improving the best estimate and quantifying the uncertainty. We suggest a number of features desirable in an operational solar wind downscaling scheme