L1 and off Sun-Earth line visible-light imaging of Earth-directed CMEs: An analysis of inconsistent observations

The efficacy of coronal mass ejection (CME) observations as a key input to space weather forecasting is explored by comparing on and off Sun-Earth line observations from the ESA/NASA SOHO and NASA STEREO spacecraft. A comparison is made of CME catalogues based on L1 coronagraph imagery and off Sun-Earth line coronagraph and heliospheric imager (HI) observations, for the year 2011. Analysis reveals inconsistencies in the identification of a number of potentially Earth-directed CMEs. The catalogues reflect our ability to identify and characterise CMEs, so any discrepancies can impact our prediction of Earth-directed CMEs. We show that 15 CMEs, which were observed by STEREO, that had estimated directions compatible with Earth-directed events, had no identified halo/partial halo counterpart listed in the L1 coronagraph CME catalogue. In-situ data confirms that for 9 of these there was a consistent L1 Interplanetary CME (ICME). The number of such "discrepant" events is significant compared to the number of ICMEs recorded at L1 in 2011, stressing the need to address space weather monitoring capabilities, particularly with the inclusion of off Sun-Earth line observation. While the study provides evidence that some halo CMEs are simply not visible in near-Earth coronagraph imagery, there is evidence that some halo CMEs viewed from L1 are compromised by preceding CME remnants or the presence of multiple-CMEs. This underlines (1) the value of multiple vantage point CME observation, and (2) the benefit of off Sun-Earth line platform heliospheric imaging, and coronagraph imaging, for the efficient identification and tracking of Earth-directed events.Comment: 36 pages, 6 figures, in press at AGU Space Weather, 202

Reconstructing CMEs with Coordinated Imaging and In Situ Observations: Global Structure, Kinematics, and Implications for Space Weather Forecasting

See the pdf for detailsComment: 45 pages, 16 figures, ApJ, in pres

On Sun-to-Earth Propagation of Coronal Mass Ejections

We investigate how coronal mass ejections (CMEs) propagate through, and interact with, the inner heliosphere between the Sun and Earth, a key question in CME research and space weather forecasting. CME Sun-to-Earth kinematics are constrained by combining wide-angle heliospheric imaging observations, interplanetary radio type II bursts and in situ measurements from multiple vantage points. We select three events for this study, the 2012 January 19, 23, and March 7 CMEs. Different from previous event studies, this work attempts to create a general picture for CME Sun-to-Earth propagation and compare different techniques for determining CME interplanetary kinematics. Key results are obtained concerning CME Sun-to-Earth propagation. Our comparison between different techniques (and data sets) also has important implications for CME observations and their interpretations. Future CME observations and space weather forecasting are discussed based on these results. See detail in the PDF.Comment: ApJ, in pres

Optimising use of electronic health records to describe the presentation of rheumatoid arthritis in primary care: a strategy for developing code lists

Background Research using electronic health records (EHRs) relies heavily on coded clinical data. Due to variation in coding practices, it can be difficult to aggregate the codes for a condition in order to define cases. This paper describes a methodology to develop ‘indicator markers’ found in patients with early rheumatoid arthritis (RA); these are a broader range of codes which may allow a probabilistic case definition to use in cases where no diagnostic code is yet recorded. Methods We examined EHRs of 5,843 patients in the General Practice Research Database, aged ≥30y, with a first coded diagnosis of RA between 2005 and 2008. Lists of indicator markers for RA were developed initially by panels of clinicians drawing up code-lists and then modified based on scrutiny of available data. The prevalence of indicator markers, and their temporal relationship to RA codes, was examined in patients from 3y before to 14d after recorded RA diagnosis. Findings Indicator markers were common throughout EHRs of RA patients, with 83.5% having 2 or more markers. 34% of patients received a disease-specific prescription before RA was coded; 42% had a referral to rheumatology, and 63% had a test for rheumatoid factor. 65% had at least one joint symptom or sign recorded and in 44% this was at least 6-months before recorded RA diagnosis. Conclusion Indicator markers of RA may be valuable for case definition in cases which do not yet have a diagnostic code. The clinical diagnosis of RA is likely to occur some months before it is coded, shown by markers frequently occurring ≥6 months before recorded diagnosis. It is difficult to differentiate delay in diagnosis from delay in recording. Information concealed in free text may be required for the accurate identification of patients and to assess the quality of care in general practice

Using coordinated observations in polarized white light and Faraday rotation to probe the spatial position and magnetic field of an interplanetary sheath

Coronal mass ejections (CMEs) can be continuously tracked through a large portion of the inner heliosphere by direct imaging in visible and radio wavebands. White light (WL) signatures of solar wind transients, such as CMEs, result from Thomson scattering of sunlight by free electrons and therefore depend on both viewing geometry and electron density. The Faraday rotation (FR) of radio waves from extragalactic pulsars and quasars, which arises due to the presence of such solar wind features, depends on the line-of-sight magnetic field component B ∥ and the electron density. To understand coordinated WL and FR observations of CMEs, we perform forward magnetohydrodynamic modeling of an Earth-directed shock and synthesize the signatures that would be remotely sensed at a number of widely distributed vantage points in the inner heliosphere. Removal of the background solar wind contribution reveals the shock-associated enhancements in WL and FR. While the efficiency of Thomson scattering depends on scattering angle, WL radiance I decreases with heliocentric distance r roughly according to the expression Ir –3. The sheath region downstream of the Earth-directed shock is well viewed from the L4 and L5 Lagrangian points, demonstrating the benefits of these points in terms of space weather forecasting. The spatial position of the main scattering site r sheath and the mass of plasma at that position M sheath can be inferred from the polarization of the shock-associated enhancement in WL radiance. From the FR measurements, the local B ∥sheath at r sheath can then be estimated. Simultaneous observations in polarized WL and FR can not only be used to detect CMEs, but also to diagnose their plasma and magnetic field properties

Characteristics of Kinematics of a Coronal Mass Ejection during the 2010 August 1 CME-CME Interaction Event

We study the interaction of two successive coronal mass ejections (CMEs) during the 2010 August 1 events using STEREO/SECCHI COR and HI data. We obtain the direction of motion for both CMEs by applying several independent reconstruction methods and find that the CMEs head in similar directions. This provides evidence that a full interaction takes place between the two CMEs that can be observed in the HI1 field-of-view. The full de-projected kinematics of the faster CME from Sun to Earth is derived by combining remote observations with in situ measurements of the CME at 1 AU. The speed profile of the faster CME (CME2; ~1200 km/s) shows a strong deceleration over the distance range at which it reaches the slower, preceding CME (CME1; ~700 km/s). By applying a drag-based model we are able to reproduce the kinematical profile of CME2 suggesting that CME1 represents a magnetohydrodynamic obstacle for CME2 and that, after the interaction, the merged entity propagates as a single structure in an ambient flow of speed and density typical for quiet solar wind conditions. Observational facts show that magnetic forces may contribute to the enhanced deceleration of CME2. We speculate that the increase in magnetic tension and pressure, when CME2 bends and compresses the magnetic field lines of CME1, increases the efficiency of drag.Comment: accepted for Ap

Validation of a priori CME arrival predictions made using real-time heliospheric imager observations

Between December 2010 and March 2013, volunteers for the Solar Stormwatch (SSW) Citizen Science project have identified and analyzed coronal mass ejections (CMEs) in the near real-time Solar Terrestrial Relations Observatory Heliospheric Imager observations, in order to make “Fearless Forecasts” of CME arrival times and speeds at Earth. Of the 60 predictions of Earth-directed CMEs, 20 resulted in an identifiable Interplanetary CME (ICME) at Earth within 1.5–6 days, with an average error in predicted transit time of 22 h, and average transit time of 82.3 h. The average error in predicting arrival speed is 151 km s−1, with an average arrival speed of 425km s−1. In the same time period, there were 44 CMEs for which there are no corresponding SSW predictions, and there were 600 days on which there was neither a CME predicted nor observed. A number of metrics show that the SSW predictions do have useful forecast skill; however, there is still much room for improvement. We investigate potential improvements by using SSW inputs in three models of ICME propagation: two of constant acceleration and one of aerodynamic drag. We find that taking account of interplanetary acceleration can improve the average errors of transit time to 19 h and arrival speed to 77 km s−1

Why are ELEvoHI CME arrival predictions different if based on STEREO‐A or STEREO‐B heliospheric imager observations?

Accurate forecasting of the arrival time and arrival speed of coronal mass ejections (CMEs) is a unsolved problem in space weather research. In this study, a comparison of the predicted arrival times and speeds for each CME based, independently, on the inputs from the two STEREO vantage points is carried out. We perform hindcasts using ELlipse Evolution model based on Heliospheric Imager observations (ELEvoHI) ensemble modelling. An estimate of the ambient solar wind conditions is obtained by the Wang‐Sheeley‐Arge/Heliospheric Upwind eXtrapolation (WSA/HUX) model combination that serves as input to ELEvoHI. We carefully select 12 CMEs between February 2010 and July 2012 that show clear signatures in both STEREO‐A and STEREO‐B HI time‐elongation maps, that propagate close to the ecliptic plane, and that have corresponding in situ signatures at Earth. We find a mean arrival time difference of 6.5 hrs between predictions from the two different viewpoints, which can reach up to 9.5 hrs for individual CMEs, while the mean arrival speed difference is 63 km s−1. An ambient solar wind with a large speed variance leads to larger differences in the STEREO‐A and STEREO‐B CME arrival time predictions (cc = 0.92). Additionally, we compare the predicted arrivals, from both spacecraft, to the actual in situ arrivals at Earth and find a mean absolute error of 7.5 ± 9.5 hrs for the arrival time and 87 ± 111 km s−1 for the arrival speed. There is no tendency for one spacecraft to provide more accurate arrival predictions than the other

Predicting dementia from primary care records: a systematic review and meta-analysis

Introduction Possible dementia is usually identified in primary care by general practitioners (GPs) who refer to specialists for diagnosis. Only two-thirds of dementia cases are currently recorded in primary care, so increasing the proportion of cases diagnosed is a strategic priority for the UK and internationally. Clinical entities in the primary care record may indicate risk of developing dementia, and could be combined in a predictive model to help find patients who are missing a diagnosis. We conducted a meta-analysis to identify clinical entities with potential for use in such a predictive model for dementia in primary care. Methods and Findings We conducted a systematic search in PubMed, Web of Science and primary care database bibliographies. We included cohort or case-control studies which used routinely collected primary care data, to measure the association between any clinical entity and dementia. Meta-analyses were performed to pool odds ratios. A sensitivity analysis assessed the impact of non-independence of cases between studies. From a sift of 3836 papers, 20 studies, all European, were eligible for inclusion, comprising >1 million patients. 75 clinical entities were assessed as risk factors for all cause dementia, Alzheimer’s (AD) and Vascular dementia (VaD). Data included were unexpectedly heterogeneous, and assumptions were made about definitions of clinical entities and timing as these were not all well described. Meta-analysis showed that neuropsychiatric symptoms including depression, anxiety, and seizures, cognitive symptoms, and history of stroke, were positively associated with dementia. Cardiovascular risk factors such as hypertension, heart disease, dyslipidaemia and diabetes were positively associated with VaD and negatively with AD. Sensitivity analyses showed similar results. Conclusions These findings are of potential value in guiding feature selection for a risk prediction tool for dementia in primary care. Limitations include findings being UK-focussed. Further predictive entities ascertainable from primary care data, such as changes in consulting patterns, were absent from the literature and should be explored in future studies