Name
Title | ||
|---|---|---|
An Application Of Spatio-Temporal Co-Occurrence Analyses For Integrating Solar Active Region Data From Multiple Reporting Modules |
Abstract | ||
|---|---|---|
Spatio-temporal co-occurrence analysis captures the spatial and temporal relations between events that occur at the same time and location. In this paper. we utilize spatio-temporal co-occurrence relations to integrate solar active region (AR) data detected and reported by three feature recognition methods, namely, human labeling by forecasters at National Oceanic and Atmospheric Administration (NOAA), Spaceweather HMI Active Region Patch (SHARP) detection pipeline, and Spatial Possibilistic Clustering Algorithm (SPoCA). We determine the associations between individual reports by identifying the spatiotemporal co-occurrences among the reports from these modules. We compare our findings with the data from the Joint Science Operations Center (JSOC), analyzing the discrepancies in different circumstances. We found 105 SHARP series not properly associated with the NOAA-labeled ARs. In the end, we provide detailed movement analyses for the AR trajectories, create an updated SHARP-to-NOAA AR associations, that is crucial for space weather predictions utilizing magnetic field information, and make the ternary associations between SHARP, NOAA ARs, and SPoCA ARs available to the public. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/BigData47090.2019.9006185 | 2019 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA) |
Field | DocType | ISSN |
Data mining,Computer science,Feature recognition,Co-occurrence,Cluster analysis,Space weather | Conference | 2639-1589 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xumin Cai | 1 | 0 | 0.68 |
| Berkay Aydin | 2 | 40 | 10.75 |
| Manolis K. Georgoulis | 3 | 0 | 0.68 |
| Rafal A. Angryk | 4 | 271 | 45.56 |