A5033998769- Solar and Space Plasma Dynamics
- Solar Radiation and Photovoltaics
- Image and Signal Denoising Methods
- Stellar, planetary, and galactic studies
- Statistical and numerical algorithms
- Photovoltaic System Optimization Techniques
- Ionosphere and magnetosphere dynamics
- Geophysics and Gravity Measurements
- CCD and CMOS Imaging Sensors
- Statistical Methods and Inference
- Solar Thermal and Photovoltaic Systems
- Spectroscopy and Chemometric Analyses
- Advanced Image Fusion Techniques
- Complex Systems and Time Series Analysis
- Spacecraft Design and Technology
- Astro and Planetary Science
- Adaptive optics and wavefront sensing
- Atmospheric Ozone and Climate
- Blind Source Separation Techniques
- Remote-Sensing Image Classification
- Industrial Vision Systems and Defect Detection
- Fault Detection and Control Systems
- Sparse and Compressive Sensing Techniques
- Energy Efficient Wireless Sensor Networks
- Geological and Geophysical Studies
Royal Observatory of Belgium
2015-2024
Rice University
2003-2004
Fund for Scientific Research
2004
KU Leuven
2004
UCLouvain
2004
Context. The Extreme Ultraviolet Imager (EUI) is part of the remote sensing instrument package ESA/NASA Solar Orbiter mission that will explore inner heliosphere and observe Sun from vantage points close to out ecliptic. advance “connection science” between solar activity heliosphere. Aims. With EUI we aim improve our understanding structure dynamics atmosphere, globally as well at high resolution, latitude perspectives. Methods. consists three telescopes, Full two High Resolution Imagers,...
Abstract Accurate forecasting of the properties coronal mass ejections (CMEs) as they approach Earth is now recognized an important strategic objective for both NOAA and NASA. The time arrival such events a key parameter, one that had been anticipated to be relatively straightforward constrain. In this study, we analyze forecasts submitted Community Coordinated Modeling Center at NASA's Goddard Space Flight over last 6 years answer following questions: (1) How well do these models forecast...
In Fall 2008 NASA selected a large international consortium to produce comprehensive automated feature-recognition system for the Solar Dynamics Observatory (SDO). The SDO data that we consider are all of Atmospheric Imaging Assembly (AIA) images plus surface magnetic-field from Helioseismic and Magnetic Imager (HMI). We robust, very efficient, professionally coded software modules can keep up with stream detect, trace, analyze numerous phenomena, including flares, sigmoids, filaments,...
Context. Precise localization and characterization of active regions (AR) coronal holes (CH) as observed by extreme ultra violet (EUV) imagers are crucial for a wide range solar helio-physics studies.
Many scientists use coronal hole (CH) detections to infer open magnetic flux. Detection techniques differ in the areas that they assign as open, and may obtain different values for We characterize uncertainties of these methods, by applying six detection methods deduce area flux a near-disk center CH observed on 9/19/2010, single method five EUV filtergrams this CH. Open was calculated using maps. The standard deviation (interpreted uncertainty) estimate about 26%. However, including...
Abstract Automated detection schemes are nowadays the standard approach for locating coronal holes in extreme-UV images from Solar Dynamics Observatory (SDO). However, factors such as noisy nature of solar imagery, instrumental effects, and others make it challenging to identify using these automated schemes. While discrepancies between have been noted literature, a comprehensive assessment is still lacking. The contribution Coronal Hole Boundary Working Team COSPAR ISWAT initiative close...
In December 2019, the Space Weather Prediction Center (SWPC) started using GOES -16 satellite as its primary input for solar x-ray flux monitoring. As such, it stopped applying a scaling factor that had been applied since GOES-8 came in operation. This has an important impact on number of flares can be expected, and flare rates associated with McIntosh classifications, often used to help forecast flaring activity. To quantify effects, intensities period covering 1976-2019 have all...
<i>Context. <i/>The study of the variability solar corona and monitoring coronal holes, quiet sun active regions are great importance in astrophysics as well for space weather climate applications.<i>Aims. <i/>In a previous work, we presented spatial possibilistic clustering algorithm (SPoCA). This is multi-channel unsupervised spatially-constrained fuzzy method that automatically segments extreme ultraviolet (EUV) images into interest. The results reported on SoHO-EIT taken from February...
Solar flares are extremely energetic phenomena in our System. Their impulsive, often drastic radiative increases, particular at short wavelengths, bring immediate impacts that motivate solar physics and space weather research to understand the point of being able forecast them. As data algorithms improve dramatically, questions must be asked concerning how well forecasting performs; crucially, we ask rigorously measure performance order critically gauge any improvements. Building upon...
We propose a new iterative distributed algorithm for linear minimum mean-squared-error (LMMSE) estimation in sensor networks whose measurements follow Gaussian hidden Markov graphical model with cycles. The embedded polygons decomposes loopy into number of linked and then applies parallel block Gauss-Seidel iteration comprising local LMMSE on each polygon (involving inversion small matrix) followed by an information exchange between neighboring nodes polygons. is robust to temporary...
Wavelet-based distributed data processing holds much promise for sensor networks; however, irregular node placement precludes the direct application of standard wavelet techniques. We develop a new transform based on lifting that takes into account sampling and provides piecewise-planar multiresolution representation sensed data. theory; outline how to implement it in multi-hop, wireless network; illustrate with several simulations. The performs par conventional methods head-to-head...
We propose a new iterative, distributed approach for linear minimum mean-square-error (LMMSE) estimation in graphical models with cycles. The embedded subgraphs algorithm (ESA) decomposes loopy model into number of linked and applies the classical parallel block Jacobi iteration comprising local LMMSE each subgraph (involving inversion small matrix) followed by an information exchange between neighboring nodes subgraphs. Our primary application is sensor networks, where encodes correlation...
This paper aims at providing an overview of latest advances in space weather modeling operational environment Europe, including both the introduction new models and improvements to existing codes algorithms that address broad range weather's prediction requirements from Sun Earth. For each case, we consider model's input data, output parameters, products or services, its status, whether it is supported by validation results, order build a solid basis for future developments. work Sub Group...
We demonstrate the use of machine learning algorithms in combination with segmentation techniques order to distinguish coronal holes and filaments SDO/AIA EUV images Sun. Based on two hole detection (intensity-based thresholding, SPoCA), we prepared datasets manually labeled filament channel regions present Sun during time range 2011–2013. By mapping extracted from observations onto HMI line-of-sight magnetograms also include their magnetic characteristics. computed shape measures segmented...
Coronal holes are the observational manifestation of solar magnetic field open to heliosphere and pivotal importance for our understanding origin acceleration wind. Observations from space missions such as Solar Dynamics Observatory now allow us study coronal in unprecedented detail. Instrumental effects other factors, however, pose a challenge automatically detect imagery. The science community addresses these challenges with different detection schemes. Until now, little attention has been...
We treat nonparametric stochastic regression using smooth design-adapted wavelets built by means of the lifting scheme. The proposed method automatically adapts to nature problem, that is, irregularity design, data on interval, and arbitrary sample sizes (which do not need be a power 2). As such, this provides uniform solution usual criticisms first-generation wavelet estimators. More precisely, starting from unbalanced Haar basis orthogonal with respect empirical design measure, we use...
Abstract Current magnetohydrodynamics (MHD) models largely rely on synoptic magnetograms, such as the ones produced by Global Oscillation Network Group (GONG). Magnetograms are currently available mostly from front side of Sun, which significantly reduces accuracy MHD modeling. Extreme Ultraviolet (EUV) images can instead be obtained other vantage points. To investigate potential, we explore possibility using EUV information Atmospheric Imaging Assembly (AIA) to directly generate input for...
Abstract A workshop was recently held at Nagoya University (2017 October 31–November 2), sponsored by the Center for International Collaborative Research, Institute Space-Earth Environmental University, Japan, to quantitatively compare performance of today’s operational solar flare forecasting facilities. Building upon Paper I this series, in II we described participating methods latest comparison effort, evaluation methodology, and presented quantitative comparisons. In paper, focus on...
A crucial challenge to successful flare prediction is forecasting periods that transition between "flare-quiet" and "flare-active". Building on earlier studies in this series (Barnes et al. 2016; Leka 2019a,b) which we describe methodology, details, results of comparison efforts, focus here patterns forecast outcomes (success failure) over multi-day periods. novel analysis developed evaluate success the context catching first event flare-active periods, conversely, correctly predicting...