OMNIWEB NASA DST INDEX SERIES

Morozov, Pattern Recognition Methods in Analysis of Complex Physical Phenomena (IZMIRAN, Russia, Moscow, 1990) Morozov, Geomagnetic storm as an object of pattern recognition. Palmadesso, Predicting solar wind structures and their geoeffectiveness. Lin, Influence of the solar wind dynamic pressure on the decay and injection of the ring current. Li, A new model for the prediction of Dst on the basis of the solar wind. McPherron, An empirical phase space analysis of ring current dynamics: solar wind control of injection and decay. Luhmann, Geomagnetic response to magnetic clouds of different polarity. Russell, An empirical relationship between interplanetary conditions and Dst. Lee, Comparison of Dst forecast models for intense geomagnetic storms. Alleyne, Multi-input data derived Dst model. Balikhin, Prediction of the Dst index using multiresolution wavelet models. Camporeale, Multiple-hour-ahead forecast of the Dst index using a combination of long short-term memory neural network and Gaussian process. Amariutei, Data derived NARMAX Dst model. Jiang, Prediction of the Dst index with bagging ensemble-learning algorithm. Wing, Probabilistic forecasting of the disturbance storm time index: an autoregressive Gaussian process approach. Arge, Probabilistic Forecasting of the Dst Index, in The Inner Magnetosphere: Physics and Modeling.

Singh, Geomagnetically induced currents in Europe: characteristics based on a local power grid model. Reay, Quantifying extreme behavior in geomagnetic activity. Thomson, Generation of 100-year geomagnetically induced current scenarios. A Workshop Report, Washington, DC, The National Academies Press (2008)Ī. National Research Council, Severe Space Weather Events, Understanding Societal and Economic Impacts. Lanzerotti, Space Weather Effects on Technologies, in Space Weather. Space Environment Center, Boulder, Colorado, June 2004 (2003) NOAA technical memorandum OAR SEC‐88, Halloween space weather storms of 2003. Retrieved 2010‐10‐2 (1989)ĬENTRA Technology Inc., Geomagnetic Storms Report Prepared for the Office of Risk Management and Analysis United States Department of Homeland Security (2011) Radasky, Geomagnetic storms can threaten electric power grid. Showalter, High-resolution global storm index: Dst versus SYM-H. Annals of the International Geophysical Year, vol. Sugiura, Hourly values of equatorial Dst for IGY. Kamei, Equatorial DST index 1957–1986, in IAGA Bull. Interestingly, the technique only requires the Dst index data as an input of the probabilistic model and is found to be very effective for the entire 24th solar cycle. Eventually, the simulated output of the probabilistic model exhibits a high value of correlation coefficient with the real-time Dst index.

OMNIWEB NASA DST INDEX SERIES

Also, the series is a combination of multiple distinguished repeating and non-repeating patterns, denoting a high degree of predictability of the Mth data of the series from its previous ( M-1) binary data. The exact dimension M of the optimum state space for the Dst index is found to be at M = 10. The study reveals some significant insights. In this paper, we introduce a new probabilistic model based on the concept of adaptive delta modulation and optimum state space to analyze, identify and characterize the patterns enfolded in the layers of the 1-h Dst index and then predict Dst data using these patterns. As the adverse effect of a powerful geomagnetic event has become a serious threat to modern human society, extracting meaningful information concealed in the complex structures of this time series can form the ground of a successful prediction algorithm. The Disturbance storm time (Dst) index is the global estimation of the intensity of the terrestrial geomagnetic activities as well as the primary indicator of the geomagnetic storm.

Omniweb nasa dst index