Solar Image Analysis and Visualization (eBook)

Jack Ireland, C. Alex Young (Herausgeber)

eBook Download: PDF

2009 | 2009
IV, 289 Seiten
Springer New York (Verlag)
978-0-387-98154-3 (ISBN)

Infos

The SECCHI A and B instrument suites (Howard et al. , 2006) onboard the two STEREO mission spacecraft (Kaiser, 2005) are each composed of: one Extreme Ultra-Violet Imager (EUVI), two white-light coronagraphs (COR1 and COR2), and two wide-angle heliospheric imagers (HI1 and HI2). Technical descriptions of EUVI, COR1 and the HIs can be found in Wuelser et al. (2004), Thompson et al. (2003), and De?se et al. (2003), respectively. The images produced by SECCHI represent a data visualization challenge: i) the images are 2048×2048 pixels (except for the HIs, which are usually binned onboard 2×2), thus the vast majority of computer displays are not able to display them at full frame and full r- olution, and ii) more importantly, the ?ve instruments of SECCHI A and B were designed to be able to track Coronal Mass Ejections from their onset (with EUVI) to their pro- gation in the heliosphere (with the HIs), which implies that a set of SECCHI images that covers the propagation of a CME from its initiation site to the Earth is composed of im- ?1 ages with very different spatial resolutions - from 1. 7 arcsecondspixel for EUVI to 2. 15 ?1 arcminutespixel for HI2, i. e. 75 times larger. A similar situation exists with the angular scales of the physical objects, since the size of a CME varies by orders of magnitude as it expands in the heliosphere.

Inhalt

Preface: A Topical Issue on Solar Image Analysis and Visualization 6
FESTIVAL: A Multiscale Visualization Tool for Solar Imaging Data 7
Abstract 7
Introduction 8
FESTIVAL Basics 8
Layers 8
Systems of Coordinates and Projections 9
Data Handling 9
Coalignment Calibration 10
The Interface 14
Navigation in Time 14
Filters and Enhancements 15
Graphical Output 16
Images 16
Movies 16
3D Visualization 16
Perspectives 17
Acknowledgements 18
References 18
Visualization of Distributed Solar Data and Metadata with the Solar Weather Browser 19
Abstract 19
Introduction 19
SWB Concept 21
Server Design 23
Client Interface 24
Conclusions 25
Appendix 26
Acknowledgements 26
References 26
Widespread Occurrence of Trenching Patterns in the Granulation Field: Evidence for Roll Convection? 27
Abstract 27
Introduction 27
The Data 29
Image Processing 30
Results 31
Conclusion 37
Acknowledgements 38
References 39
Principal Components and Independent Component Analysis of Solar and Space Data 40
Abstract 40
Introduction 40
Principal Components Analysis 41
Mathematical Background 41
Application of PCA to Heliospheric and Solar Data 43
Solar Wind IMF Longitude 43
Wilcox Solar Observatory Coronal 3.25R Magnetic Carrington Maps 44
Independent Component Analysis 46
Mathematical Background 46
Application to Search for Active Longitudes 48
Data Preparation and PCA 48
Independent Components 51
Summary and Conclusions 52
Acknowledgements 53
References 53
Automatic Recognition and Characterisation of Supergranular Cells from Photospheric Velocity Fields 55
Abstract 55
Introduction 55
Cell Analysis Method 56
Data Preparation 56
Overview 57
Method in Detail 58
Application to Test Data 59
Application to Real Data 61
Cell Sizes 62
Cell Internal Speeds 63
Tracking Supergranular Cells over Time 65
Cautionary Notes 65
Conclusions 66
Acknowledgements 67
References 67
Automated McIntosh-Based Classification of Sunspot Groups Using MDI Images 68
Abstract 68
Introduction 68
Data Description 69
Sunspot Detection and Grouping 70
Preprocessing of MDI Images 70
Initial Detection of Solar Features 73
Deciding Active Regions and Grouping of Sunspots 73
McIntosh Classification of Sunspot Regions 77
Computing the Modified Zürich Class - Z 78
Determining the Type of the Largest Spot - p 78
Determining the Sunspot Distribution - c 80
Implementation and Evaluation 80
Practical Implementation of the System 80
The Evaluation of the ASC 81
Discussions, Conclusions and Future Work 83
Discussions and Concluding Remarks 83
Future Work 85
Acknowledgements 86
Appendix: Neural Networks 86
References 87
Multifractal Properties of Evolving Active Regions 88
Abstract 88
Introduction 89
Observations and Data Reduction 90
Fractals and Multifractals 90
Results 92
Theoretical fractals 92
NOAA 10488 92
NOAA 10798 94
NOAA 10763 96
NOAA 10727 96
Discussion and Conclusions 97
Acknowledgements 100
References 100
Multiscale Analysis of Active Region Evolution 101
Abstract 101
Introduction 102
Observations and Data Reduction 103
A Wavelet Method for Energy Spectrum Extraction 103
Results 105
Application to a Simple 1D Signal 105
Application to Simulated Magnetograms 105
Application to MDI Magnetograms 105
NOAA 09077: A Comparison 105
NOAA 10488: Evidence of an Inverse Cascade 106
Conclusions 112
Acknowledgements 112
References 112
A Comparison of Feature Classification Methods for Modeling Solar Irradiance Variation 113
Abstract 113
Introduction 114
Observations and Feature Identification Methods 115
Comparison of Feature Labelings 119
Discussion 124
Acknowledgements 125
References 126
The Observed Long- and Short-Term Phase Relation between the Toroidal and Poloidal Magnetic Fields in Cycle 23 128
Abstract 128
Introduction 129
Data Description and Statistical Tool 130
Solar Feature Catalogue 130
Wilcox Solar Observatory Data 131
Some Elements of Statistics 131
Results and Discussion 132
Latitudinal Distributions of Sunspot and Active Region Areas and Excess Magnetic Flux 132
Quasi-3D Butterfly Diagrams of the Sunspot Areas and Excess Magnetic Field 132
The North-South Asymmetry in the Areas and Excess Magnetic Flux 134
Correlation with the Background Solar Magnetic Field 136
Latitudinal Variations of the Background SMF 137
The Long-Term Phase Relation between the Sunspot Excess Magnetic Flux and the SMF 138
The Short-Term Phase Relation between the SMF and the Sunspot EMF 139
Conclusions 145
Acknowledgements 146
References 146
Comparison of Five Numerical Codes for Automated Tracing of Coronal Loops 148
Abstract 148
Introduction 149
Automated Loop-Tracing Codes 149
The Oriented-Connectivity Method 150
The Dynamic Aperture-based Loop Segmentation Method 150
Unbiased Detection of Curvilinear Structures Method (UDM) 151
Oriented-Directivity Loop Tracing Method 151
Ridge Detection by Automated Scaling 152
Test Comparisons of Loop-Tracing Codes 152
Test Image and High-Pass Filtering 153
Manual Tracing of Loops 154
Automated Tracing of Loops 154
Automated Tracing with the OCM Code 156
Automated Tracing with the DAM Code 156
Automated Tracing with the UDM Code 157
Automated Tracing with the ODM Code 157
Automated Tracing with the RAS Code 158
Quantitative Comparison of Automated Tracing Codes 159
Cumulative Distribution of Loop Lengths 159
Maximum Detected Loop Lengths 160
Completeness of Loop Detection 161
Accuracy and Sensitivity of Loop Detection 161
Computation Speed of Automated Tracing Codes 163
Discussion and Conclusions 164
Acknowledgements 165
References 165
Segmentation of Loops from Coronal EUV Images 167
Abstract 167
Introduction 167
Method 168
Ridgel Location and Orientation 169
Ridgel Connection to Chains 172
Curve Fits to the Ridgel Chains 175
Application 177
Discussion 180
Acknowledgements 180
References 181
The Pixelised Wavelet Filtering Method to Study Waves and Oscillations in Time Sequences of Solar Atmospheric Images 182
Abstract 182
Introduction 182
Test Signals 184
The Scheme of the Pixelised Wavelet Filtering Method 185
Application to Sunspot Oscillations 189
Application to Coronal Loop Oscillations 191
Conclusions 192
Acknowledgements 194
References 194
A Time-Evolving 3D Method Dedicated to the Reconstruction of Solar Plumes and Results Using Extreme Ultraviolet Data 196
Abstract 196
Introduction 197
Method 198
Direct Problem 198
Modeling of the Temporal Evolution 199
Inverse Problem 200
Criterion Minimization 201
Descent Direction Definition and Stop Threshold 201
Method Validation 202
Simulation Generation Process 202
Analysis of Results 202
Choice of Evolution Areas 204
Reconstruction of SOHO/EIT Data 205
Data Preprocessing 205
Analysis of Results 205
Discussion 207
Conclusion 208
Acknowledgements 209
Appendix A: Pseudo-Inverse Minimization 209
Appendix B: Gradient-Like Method 209
References 210
Automatic Detection and Classification of Coronal Holes and Filaments Based on EUV and Magnetogram Observations of the Solar Disk 211
Abstract 211
Introduction 212
Overview of Approach and Data Selection 213
Data Preparation and Image Processing 214
Candidate Features: Detection and Classification Method 215
Candidate Features: Final Product 220
Discussion and Conclusion 224
Acknowledgements 225
References 225
Spatial and Temporal Noise in Solar EUV Observations 226
Abstract 226
Introduction 227
EIT Data Set Analysis 227
Sources of Noise in EIT Images 228
Poisson Noise 228
Blurring from the PSF 228
Flat Field 229
Read-out Noise 229
Method 229
Regularity Analysis Using Pointwise Hölder Exponents 230
Signal-to-Noise Ratio and Relationship between Mean and Standard Deviation 231
Forward Modeling Approach 233
Generation of the Data Sequence 234
Phenomenological Model 236
Results of the Simulation Study 237
Discussion and Conclusion 238
Acknowledgements 239
Appendix: Pointwise Hölder Exponent 239
References 239
Multiscale Edge Detection in the Corona 241
Abstract 241
Introduction 241
A System for Automatic CME Detection 242
Observations 243
Methodology 244
Edge Detection 244
Multiscale Edge Detection 245
Edge Selection and Error Estimation 246
Results 248
Conclusions and Future Work 252
Acknowledgements 252
References 252
Automated Prediction of CMEs Using Machine Learning of CME-Flare Associations 254
Abstract 254
Introduction 254
CMEs and their Associations with Solar Activities and Features 256
The Computer Platform Design for CME Predictions 256
Associating Flares and CMEs 257
Creating the Associated Numerical Data Set 257
Practical Implementation and Results 258
The Learning Algorithms and Techniques 258
Optimising the Learning Algorithms 259
Optimising the CCNN 259
Optimising the SVM 261
Comparing the Prediction Performances 262
Further Investigation of Catalogue Data 264
Conclusions and Future Research 264
Acknowledgements 265
References 266
Automatic Detection and Tracking of Coronal Mass Ejections in Coronagraph Time Series 267
Abstract 267
Introduction 268
Methodology 269
Preprocessing 269
Initial Detection 271
Tracking 273
Leading-Edge Determination and Visualization 275
Example Case 275
Results and Validation 276
Results 276
Validation and Comparison 277
Discussion and Conclusion 280
Acknowledgements 281
References 281

Produkt-Details

• Erscheint lt. Verlag: 27.8.2009
• Zusatzinfo: IV, 289 p.
• Verlagsort: New York
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Informatik ► Grafik / Design
• Themenwelt: Naturwissenschaften ► Geowissenschaften ► Geologie
• Themenwelt: Naturwissenschaften ► Physik / Astronomie ► Astronomie / Astrophysik
• Themenwelt: Technik ► Elektrotechnik / Energietechnik
• Schlagworte: automated feature detection • Corona • image enhancement • machine learning and solar physics • Solar • solar image processing • Sun • SunSPOT • three dimensional reconstruction of CMEs
• ISBN-10: 0-387-98154-3 / 0387981543
• ISBN-13: 978-0-387-98154-3 / 9780387981543