Study of Global Photospheric and Chromospheric Flows Using Local Correlation Tracking and Machine Learning Methods I: Methodology and Uncertainty Estimates

Keyphrases

• Chromosphere 100%
• Cyclic Variation 20%
• Differential Rotation 80%
• Dipole Field 20%
• Equator 20%
• Field Emergence 20%
• Field Formation 20%
• Flow Field 20%
• Full Disk 20%
• Fundamental Causes 20%
• Local Correlation Tracking 100%
• Low Latitude 20%
• Machine Learning Based 20%
• Machine Learning Techniques 100%
• Magnetogram 40%
• Measurement Uncertainty 20%
• Meridional Flow 80%
• Method Handles 20%
• Optical Flow Method 40%
• Photosphere 20%
• Photospheric Flow 20%
• Polar Regions 20%
• Rotating Flows 40%
• Solar Activity 20%
• Solar Cycle 20%
• Solar Dynamo 20%
• Solar Magnetic Fields 40%
• Space Weather Research 20%
• Strong Correlation 20%
• Strong Dynamics 20%
• Surface Components 20%
• Surface Flow 20%
• Uncertainty Estimation 100%

Engineering

• Component Surface 50%
• Dipole Field 50%
• Flow Field 50%
• Global Scale 50%
• Local Method 50%
• Machine Learning Method 100%
• Magnetic Field 100%
• Measurement Uncertainty 50%
• Modeling Study 50%

Earth and Planetary Sciences

• Flow Distribution 25%
• Machine Learning 100%
• Magnetic Signature 50%
• Meridional Flow 100%
• Photosphere 25%
• Polar Regions 25%
• Solar Activity 25%
• Solar Cycle 25%
• Solar Magnetic Field 50%
• Space Weather 25%
• Tropics 25%