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Innovative Information Technology for Space Weather Research. Haimin Wang, Ming Qu, Carsten Denker, Frank Shih, Alex Gerbessiotis, Mats Lofdahl, David Rees and Christoph Keller. ITR Project.
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Innovative Information Technology for Space Weather Research Haimin Wang, Ming Qu, Carsten Denker, Frank Shih, Alex Gerbessiotis, Mats Lofdahl, David Rees and Christoph Keller
ITR Project • Use the innovative computation and information technologies (IT) for real time space weather monitoring and forecasting.
The Team • Wang and Denker, BBSO/NJIT • Shi and Gerbessiotis, CIS/NJIT • Christoph Keller, NSO • Mats Lofdahl, RSAS • David Rees, Sydney University • 5 Ph.D. and 1 undergraduate students
Task 1 • We use the technologies of image processing and pattern recognition, such as image morphology segmentation, Support Vector Machines (SVMs), and neural networks to detect and characterize important solar activities in real-time: filament eruptions, flares, CMEs and emerging flux regions (EFRs).
MLP, RBF, SVM • A neural network is a massively parallel distributed processor that has a natural propensity for storing experiential knowledge and making it available for classification • MLP: multi-layer perceptron neural network • RBF: Radial Basis Function neural network • SVM: Support Vector Machine. It is a new generation learning system based on advances in statistical learning theory
Region Growing • The first step is to determine the initial seed points. We use the key pixel that is proposed in the classification step. • The second step is to choose criteria for the region growing. 1) The gray-level of any pixels are greater than or equal to 75% of gray-level of the initial points. 2) To be included in one of the regions, the pixel had to be 8-connected to at least one pixel on that region. 3) When a pixel was found to be connected to more than one region, the regions were merged.
There are two terms used in Edge detection. Edge Detection • The first-order derivatives are computed using the gradient • The second-order derivatives are obtained using the Laplacian
Modified Adaptive Gradient Method • For each 10 by 10 small window, pick 8 neighboring 10 by 10 windows. • Calculate the mean brightness and standard deviation for the small window and neighboring windows. • Use the second largest mean brightness and standard deviation among all these windows to be the criteria for picking threshold T. • When the mean brightness and standard deviation are large, use a larger threshold; otherwise, a small threshold. • This method will remove small regions close to bright objects.
Experimental Results Region growing Using light side pixels as seeds Original image Adaptive gradient Global gradient After expanding
Motion of a Two-ribbon Flare • Our goal is to track the separation motion of two-ribbon flares and measure their moving direction and speed in the magnetic fields. • From these measurements, with certain assumptions, we can infer the reconnection of the electric field as a measure of the rate of the magnetic reconnection in the corona. The automatic procedure is a valuable tool for real-time monitoring of flare evolution.
Experimental Results V Build a model Characterization result Differences between the models
Experimental Results The Green is the result of our method, The pink is the result of previous method. E is electric field, Vr is the velocity of flare ribbons, Bn is the value of magnetic field from MDI image. Compared with the previous method which tracks the moving front of ribbons manually, our method has the following advantages: 1) it avoids the difficulty in determining the positions of the moving front which usually do not have a regular and clear shape, and 2) an accurate average is obtained by considering all the value of magnetic fields in the newly brightened area. However, the results of automatic method also contain errors due to noise effect. The confidence intervals of both methods are estimated by computing the standard deviation of parameters in the background level, which are shown in Figure with error bars. In general, the deviations between two sets of results are acceptable. Figure b shows that the automatic method can obtain a better result than the standard method in Qiu et al. since the peak of the $E$ field from the automatic result is closer to the peak of the light curve of the flare as observed in the radio.
Task 2 • We combine state-of-art parallel computing techniques with phase diversity speckle imaging techniques, to yield near real-time diffraction limited images with a cadence of approximately 10 sec.
Application • Real-time diffraction limited data of solar active regions • Real-time flow maps of active regions • Real-time Stokes inversion of imaging vector magnetograms
Task 3 • We develop Web based software tools to post our processed data, events and forecasting in real time, and to be integrated with current solar activity and space weather prediction Web pages at BBSO. This will also be a part of Virtual Solar Observatory (VSO) We also develop a high speed server and advanced user interface and data visualization, so the real-time data and forecasting of solar activity and geomagnetic storm can be available to the community efficiently.
Cartoon to Demonstrate two Events(top, 2/17/2000, bottom, 7/14/2000)
