The SPoCA-suite: Software for extraction, characterization, and tracking of active regions and coronal holes on EUV images

Verbeeck, C.; Delouille, Véronique; Mampaey, Benjamin; Visscher, Ruben De

doi:10.1051/0004-6361/201321243

Cited by 121 publications

(124 citation statements)

References 58 publications

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“…We show coronal holes falsely detected by our algorithm (left top outlined with a blue circle) for the SDO/AIA-193Å image taken on the 5th of August, 2012. In comparison, the Spatial Possibilistic Clustering Algorithm SPoCA-Suite (Delouille et al, 2012;Verbeeck et al, 2014) did not identify the filament as coronal hole (Image taken from helioviewer (http://www.helioviewer.org/). In the bottom right panel an Hα image recorded at Kanzelhoehe observatory (http://cesar.kso.ac.at/synoptic/ha years.php ;Pötzi, Hirtenfellner-Polanec, and Temmer, 2013), clearly outlines the filament giving evidence of the current weakness of the algorithm to distinct between coronal holes and filaments, as they are both characterized by low intensities in the EUV images (see also de Toma, Arge, and Riley, 2005).…”

Section: Discussionmentioning

confidence: 99%

Real-Time Solar Wind Prediction Based on SDO/AIA Coronal Hole Data

et al. 2015

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We present an empirical model based on the visible area covered by coronal holes close to the central meridian in order to predict the solar wind speed at 1 AU with a lead time up to four days in advance with a 1-h time resolution. Linear prediction functions are used to relate coronal hole areas to solar wind speed. The function parameters are automatically adapted by using the information from the previous 3 Carrington Rotations. Thus the algorithm automatically reacts on the changes of the solar wind speed during different phases of the solar cycle. The adaptive algorithm has been applied to and tested on SDO/AIA-193Å observations and ACE measurements during the years 2011 − 2013, covering 41 Carrington Rotations. The solar wind speed arrival time is delayed and needs on average 4.02 ± 0.5 days to reach Earth. The algorithm produces good predictions for the 156 solar wind high speed streams peak amplitudes with correlation coefficients of cc ≈ 0.60. For 80% of the peaks, the predicted arrival matches within a time window of 0.5 days of the ACE in situ measurements. The same algorithm, using linear predictions, was also applied to predict the magnetic field strength from coronal hole areas but did not give reliable predictions (cc ≈ 0.2).

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Section: Discussionmentioning

confidence: 99%

Real-Time Solar Wind Prediction Based on SDO/AIA Coronal Hole Data

et al. 2015

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“…The SPoCA-suite is a set of multichannel fuzzy clustering algorithms that automatically segment solar EUV images into a set of features (see Barra et al 2009;Verbeeck et al 2013b for a complete presentation). We have chosen SPoCA because of the maturity and flexibility of the program.…”

Section: Image Segmentation With the Spoca-suitementioning

confidence: 99%

Segmentation of coronal features to understand the solar EUV and UV irradiance variability

Kumara

Kariyappa

Zender

et al. 2013

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Context. The study of solar irradiance variability is of great importance in heliophysics, the Earth's climate, and space weather applications. These studies require careful identifying, tracking and monitoring of active regions (ARs), coronal holes (CHs), and the quiet Sun (QS). Aims. We studied the variability of solar irradiance for a period of two years (January 2011-December 2012) using the Large Yield Radiometer (LYRA), the Sun Watcher using APS and image Processing (SWAP) on board PROBA2, and the Atmospheric Imaging Assembly (AIA) on board the Solar Dynamics Observatory (SDO). Methods. We used the spatial possibilistic clustering algorithm (SPoCA) to identify and segment coronal features from the EUV observations of AIA. The AIA segmentation maps were then applied on SWAP images, and parameters such as the intensity, fractional area, and contribution of ARs/CHs/QS features were computed and compared with the full-disk integrated intensity and LYRA irradiance measurements. Results. We report the results obtained from SDO/AIA and PROBA2/SWAP images taken from January 2011 to December 2012 and compare the resulting integrated full-disk intensity with PROBA2/LYRA irradiance. We determine the contributions of the segmented features to EUV and UV irradiance variations. The variations of the parameters resulting from the segmentation, namely the area, integrated intensity, and relative contribution to the solar irradiance, are compared with LYRA irradiance. We find that the active regions have a great impact on the irradiance fluctuations. In the EUV passbands considered in this study, the QS is the greatest contributor to the solar irradiance, with up to 63% of total intensity values. Active regions, on the other hand, contribute to about 10%, and off-limb structures to about 24%. We also find that the area of the features is highly variable suggesting that their area has to be taken into account in irradiance models, in addition to their intensity variations. Conclusions. We successfully show that the feature extraction allows us to use EUV telescopes to measure irradiance fluctuations and to quantify the contribution of each part to the EUV spectral solar irradiance observed with a calibrated radiometer. This study also shows that SPoCA is viable, and that the segmentation of images can be a useful tool. We also provide the measurement correlation between SWAP and AIA during this analysis.

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“…Second, we are interested in a segmentation up to 1.4 R , where R is the solar radius. A limb brightness correction is therefore needed and is applied on the eit-prepped images as described in Verbeeck et al (2014). Note that the spectral synthesis code SOLMOD accounts for the limb brightening naturally due to the lineof-sight integration carried out in spherical symmetry.…”

Section: Selection Of Eit Datasetmentioning

confidence: 99%

“…We recall here the formula to be used in the present context. The reader is referred to Verbeeck et al (2014) for a complete description. In fuzzy clustering, every pixel obtains a membership value to each of the C classes via the minimization of an intra-cluster variance.…”

Section: Spoca2 Fuzzy Clustering Algorithmmentioning

confidence: 99%

“…Our analysis is based on the segmentation of images taken in the 171 Å and 195 Å channels with the Extreme UV Imaging Telescope (EIT; Delaboudinière et al 1995). Using the Spatial Possibilistic Clustering Algorithm (SPoCA2) tool described in Verbeeck et al (2014), these images are segmented into six different features. This dataset of EIT images covers approximately one and a half solar cycles, including two solar minima.…”

Section: Introductionmentioning

confidence: 99%

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Reconstruction of the solar EUV irradiance from 1996 to 2010 based on SOHO/EIT images

Haberreiter

Delouille

Mampaey

et al. 2014

J. Space Weather Space Clim.

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The solar Extreme UltraViolet (EUV) spectrum has important effects on the Earth's upper atmosphere. For a detailed investigation of these effects it is important to have a consistent data series of the EUV spectral irradiance available. We present a reconstruction of the solar EUV irradiance based on SOHO/EIT images, along with synthetic spectra calculated using different coronal features which represent the brightness variation of the solar atmosphere. The EIT images are segmented with the SPoCA2 tool which separates the features based on a fixed brightness classification scheme. With the SOLMOD code we then calculate intensity spectra for the 10-100 nm wavelength range and each of the coronal features. Weighting the intensity spectra with the area covered by each of the features yields the temporal variation of the EUV spectrum. The reconstructed spectrum is then validated against the spectral irradiance as observed with SOHO/SEM. Our approach leads to good agreement between the reconstructed and the observed spectral irradiance. This study is an important step toward understanding variations in the solar EUV spectrum and ultimately its effect on the Earth's upper atmosphere.

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The SPoCA-suite: Software for extraction, characterization, and tracking of active regions and coronal holes on EUV images

Cited by 121 publications

References 58 publications

Real-Time Solar Wind Prediction Based on SDO/AIA Coronal Hole Data

Real-Time Solar Wind Prediction Based on SDO/AIA Coronal Hole Data

Segmentation of coronal features to understand the solar EUV and UV irradiance variability

Reconstruction of the solar EUV irradiance from 1996 to 2010 based on SOHO/EIT images

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