Mathematics – Probability
Scientific paper
May 2002
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2002agusmsh52a..06a&link_type=abstract
American Geophysical Union, Spring Meeting 2002, abstract #SH52A-06
Mathematics
Probability
2722 Forecasting, 2784 Solar Wind/Magnetosphere Interactions, 7513 Coronal Mass Ejections, 2111 Ejecta, Driver Gases, And Magnetic Clouds, 2134 Interplanetary Magnetic Fields
Scientific paper
The Chen prediction technique [Chen et al., 1996; 1997] is a feature-based pattern recognition scheme designed to identify and predict accurately the occurrence, duration, and strength of moderately large to large geomagnetic storms using real-time solar wind data. It does this by estimating the interplanetary magnetic field (IMF) of the solar wind upstream of a monitor and then by calculating the probability of its geoeffectiveness using Bayesian statistics. The model identifies physical features of solar wind structures that cause large storms: long durations of southward IMF. It is designed to identify moderately large to large storms (defined as a sustained Dst of less than -80 nT for more than 2-hours) as they have the greatest potential for causing disruption and/or damage to power grids, satellites, and communications systems. An operational version of the model is online (http://solar.sec.noaa.gov/~narge/cloud/cloud.cgi) at NOAA/SEC and routinely provides hourly estimates of the probability that a storm will ensue using real-time solar wind data available from the Advanced Composition Explorer (ACE) spacecraft. A recent 3-year (i.e., years 1998-2000) historical verification study conducted on the model [Arge et al., 2002] shows that it can successfully predict approximately 80% of large nonrecurrent storms with an average advance warning time of 2.1 hours. The number of false alarms is also relatively small (25%). These ACE results are similar to the results of the companion tests based on the daily analysis of the WIND MFI data, which has been ongoing since 1996 (http://wwwppd.nrl.navy.mil/prediction/). Arge et al., submitted, Adv. Space Res., 2002. Chen et al., GRL, 23, 625, 1996. Chen et al., JGR, 102, 14701, 1997. Work supported by ONR.
Arge Charles Nickolos
Chen Jiahua
Pizzo Vic
Slinker S.
Wahl Sean
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