New techniques for the characterization of dynamical phenomena in solar coronal images.

This dissertation describes two aspects of solar coronal dynamics: waves in coronal loops (Part I) and coronal mass ejections (Part II). The central theme is the development of new techniques used to extract crucialinformation out of the observations. We investigate the influence of (semi-) automated techniques on solar coronal research. This is a timely discussion since the observation of solar phenomena is transitioning frommanual detection to `Solar Image Processing'. Our results are mainly based on images from the Extreme UV Imaging Telescope (EIT) and the Large Angle and Spectrometric Coronagraph (LASCO), two instruments onboard thesatellite SOHO (Solar and Heliospheric Observatory) of which we recently celebrated its 11th anniversary. The high quality of the images together with the long timespan created a valuable database for solar physics research.Part I reports on the first detection of slow magnetoacoustic waves in coronal loops observed in high cadence image sequences simultaneously produced by EIT and TRACE (Transition Region And Coronal Explorer). These two instruments observe the hot corona in extreme UV emission lines. TRACE achieved a 25 sec image cadence in the Fe ix (17.1 nm) bandpass whileEIT achieved a 15 sec cadence in the Fe xii (19.5 nm) bandpass. These multi-wavelength observations have revealed the existence of weak transients in extended coronal loop systems. The disturbances originate from small scale brightenings at the footpoints of the loops and propagate along the loops. By explaining the difference of the measured speeds with the sound speed as a projection effect we suggest that these weak transients are magnetoacoustic waves. The appearance of waves traveling at different speeds in the same loop, possibly suggests that these loops have sharp temperature gradients.Part II addresses the question of detecting coronal mass ejections in coronagraphic white light data. Coronal mass ejections (CMEs) are sudden expulsions of mass and magnetic field from the solar corona into the interplanetary medium. A classical CME carries away some 1023 to 10****status: publishe

Verification of space weather forecasting at the Regional Warning Center in Belgium

The Solar Influences Data analysis Center (SIDC) in Brussels at the Royal Observatory of Belgium (ROB) has been providing daily space weather forecasts for more than a decade. A verification analysis was applied to evaluate the performance of the SIDC forecasts of fundamental space weather parameters such as the F10.7 radio flux, solar flare activity, and local geomagnetic index. Strengths and weaknesses are determined compared to common numerical models. Descriptive model statistics, common verification measures, error analysis and conditional plots related to forecasts and observations are presented. The verification analysis methods have been designed such that future improvements and additions can easily be included, for example with new forecasting models. The SIDC forecast (together with the persistence model) achieves the best performance for forecasting F10.7 on day 1, but has potential for improvement for a larger lead time mainly by applying estimates from the persistence and corrected recurrence models. The persistence model is superior for the forecast of flares, though corrected recurrence models are slightly better in foreseeing M- and X-class flares and the SIDC forecast estimates B- and C-class flares very well. The SIDC forecast scores better than all models in forecasting the local K-index. It best reproduces observations in the range of K = 2–4, but underestimates larger K values. The SIDC forecast provides a distribution that best matches the observations of the K-index. The analysis presented here demonstrates the influence of solar activity on the confidence level of the forecasts, as well as the hinted influence of the forecaster on duty due to the subjective nature of forecasting. The output aids to identify the strong and weak points of the SIDC forecast as well as those of the models considered. Though the presented analysis needs further extension, it already illustrates the opportunity to regularly reevaluate space weather forecasts and to stimulate ideas for improvement and increase the reliability of space weather forecasting

Solar activity : nowcasting and forecasting at the SIDC

The Solar Influences Data analysis Center (SIDC) is the World Data Center for the production and the distribution of the International Sunspot Index, coordinating a network of about 80 stations worldwide. From this core activity, the SIDC has grown in recent years to a European center for nowcasting and forecasting of solar activity on all timescales. This paper reviews the services (data, forecasts, alerts, software) that the SIDC currently offers to the scientific community. The SIDC operates instruments both on the ground and in space. The USET telescope in Brussels produces daily white light and Hα images. Several members of the SIDC are co-investigators of the EIT instrument onboard SOHO and are involved in the development of the next generation of Europe's solar weather monitoring capabilities. While the SIDC is staffed only during day-time (7 days/week), the monitoring service is a 24 h activity thanks to the implementation of autonomous software for data handling and analysis and the sending of automated alerts. We will give an overview of recently developed techniques for visualization and automated analysis of solar images and detection of events significant for space weather (e.g. CMEs or EIT waves). As part of the involvement of the SIDC in the ESA Pilot Project for Space Weather Applications we have developed services dedicated to the users of the Global Positioning System (GPS). As a Regional Warning Center (RWC) of the International Space Environment Service (ISES), the SIDC produces daily forecasts of flaring probability, geomagnetic activity and 10.7 cm radio flux. The accuracy of these forecasts will be investigated through an in-depth quality analysis

Shared polygenic risk and causal inferences in amyotrophic lateral sclerosis

International audienceTo identify shared polygenic risk and causal associations in amyotrophic lateral sclerosis (ALS).Methods: Linkage disequilibrium score regression and Mendelian randomization were applied in a large-scale, data-driven manner to explore genetic correlations and causal relationships between >700 phenotypic traits and ALS. Exposures consisted of publicly available genome-wide association studies (GWASes) summary statistics from MR Base and LD-hub. The outcome data came from the recently published ALS GWAS involving 20,806 cases and 59,804 controls. Multivariate analyses, genetic risk profiling, and Bayesian colocalization analyses were also performed.Results: We have shown, by linkage disequilibrium score regression, that ALS shares polygenic risk genetic factors with a number of traits and conditions, including positive correlations with smoking status and moderate levels of physical activity, and negative correlations with higher cognitive performance, higher educational attainment, and light levels of physical activity. Using Mendelian randomization, we found evidence that hyperlipidemia is a causal risk factor for ALS and localized putative functional signals within loci of interest.Interpretation: Here, we have developed a public resource (https://lng-nia.shinyapps.io/mrshiny) which we hope will become a valuable tool for the ALS community, and that will be expanded and updated as new data become available. Shared polygenic risk exists between ALS and educational attainment, physical activity, smoking, and tenseness/restlessness. We also found evidence that elevated low-desnity lipoprotein cholesterol is a causal risk factor for ALS. Future randomized controlled trials should be considered as a proof of causality. Ann Neurol 2019;85:470-481