Composition of Coronal Hole Boundary Layers at Low Heliographic Latitudes

Author(s):  
K. Delano ◽  
H. A. Elliott ◽  
S. T. Lepri ◽  
S. A. Fuselier
Author(s):  
Xinping Zhou ◽  
Yuandeng Shen ◽  
Zehao Tang ◽  
Chengrui zhou ◽  
Yadan Duan ◽  
...  

1998 ◽  
Vol 103 (A2) ◽  
pp. 1955-1967 ◽  
Author(s):  
D. J. McComas ◽  
P. Riley ◽  
J. T. Gosling ◽  
A. Balogh ◽  
R. Forsyth

1999 ◽  
Vol 513 (2) ◽  
pp. 961-968 ◽  
Author(s):  
Richard Woo ◽  
Shadia Rifai Habbal ◽  
Russell A. Howard ◽  
Clarence M. Korendyke

Solar Physics ◽  
2019 ◽  
Vol 294 (10) ◽  
Author(s):  
Stephan G. Heinemann ◽  
Manuela Temmer ◽  
Niko Heinemann ◽  
Karin Dissauer ◽  
Evangelia Samara ◽  
...  

Abstract Coronal holes are usually defined as dark structures seen in the extreme ultraviolet and X-ray spectrum which are generally associated with open magnetic fields. Deriving reliably the coronal hole boundary is of high interest, as its area, underlying magnetic field, and other properties give important hints as regards high speed solar wind acceleration processes and compression regions arriving at Earth. In this study we present a new threshold-based extraction method, which incorporates the intensity gradient along the coronal hole boundary, which is implemented as a user-friendly SSW-IDL GUI. The Collection of Analysis Tools for Coronal Holes (CATCH) enables the user to download data, perform guided coronal hole extraction and analyze the underlying photospheric magnetic field. We use CATCH to analyze non-polar coronal holes during the SDO-era, based on 193 Å filtergrams taken by the Atmospheric Imaging Assembly (AIA) and magnetograms taken by the Heliospheric and Magnetic Imager (HMI), both on board the Solar Dynamics Observatory (SDO). Between 2010 and 2019 we investigate 707 coronal holes that are located close to the central meridian. We find coronal holes distributed across latitudes of about ${\pm}\, 60^{\circ}$±60∘, for which we derive sizes between $1.6 \times 10^{9}$1.6×109 and $1.8 \times 10^{11}\mbox{ km}^{2}$1.8×1011 km2. The absolute value of the mean signed magnetic field strength tends towards an average of $2.9\pm 1.9$2.9±1.9 G. As far as the abundance and size of coronal holes is concerned, we find no distinct trend towards the northern or southern hemisphere. We find that variations in local and global conditions may significantly change the threshold needed for reliable coronal hole extraction and thus, we can highlight the importance of individually assessing and extracting coronal holes.


1999 ◽  
Vol 104 (A5) ◽  
pp. 9735-9751 ◽  
Author(s):  
X. P. Zhao ◽  
J. T. Hoeksema ◽  
P. H. Scherrer

1998 ◽  
Vol 103 (A7) ◽  
pp. 14655-14655 ◽  
Author(s):  
D. J. McComas ◽  
P. Riley ◽  
J. T. Gosling ◽  
A. Balogh ◽  
R. Forsyth

Solar Physics ◽  
1976 ◽  
Vol 46 (2) ◽  
pp. 291-301 ◽  
Author(s):  
J. T. Nolte ◽  
A. S. Krieger ◽  
A. F. Timothy ◽  
G. S. Vaiana ◽  
M. V. Zombeck
Keyword(s):  

Solar Physics ◽  
1979 ◽  
Vol 62 (2) ◽  
pp. 343-346
Author(s):  
M. P. Nakada
Keyword(s):  

2006 ◽  
Vol 446 (1) ◽  
pp. 327-331 ◽  
Author(s):  
J. G. Doyle ◽  
M. D. Popescu ◽  
Y. Taroyan

2021 ◽  
Author(s):  
Robert Jarolim ◽  
Astrid Veronig ◽  
Stefan Hofmeister ◽  
Stephan Heinemann ◽  
Manuela Temmer ◽  
...  

<p>Being the source region of fast solar wind streams, coronal holes are one of the key components which impact space weather. The precise detection of the coronal hole boundary is an important criterion for forecasting and solar wind modeling, but also challenges our current understanding of the magnetic structure of the Sun. We use deep-learning to provide new methods for the detection of coronal holes, based on the multi-band EUV filtergrams and LOS magnetogram from the AIA and HMI instruments onboard the Solar Dynamics Observatory. The proposed neural network is capable to simultaneously identify full-disk correlations as well as small-scale structures and efficiently combines the multi-channel information into a single detection. From the comparison with an independent manually curated test set, the model provides a more stable extraction of coronal holes than the samples considered for training. Our method operates in real-time and provides reliable coronal hole extractions throughout the solar cycle, without any additional adjustments. We further investigate the importance of the individual channels and show that our neural network can identify coronal holes solely from magnetic field data.</p>


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