HR: 0800h
AN: SA51B-0242    [Abstracts]
TI: Kp forecast models
AU: Meng, C
EM: ching.meng@jhuapl.edu
AF: Johns Hopkins University, 11100 Johns Hopkins Road, Laurel, MD 20723-6099 United States
AU: * Wing, S
EM: simon.wing@jhuapl.edu
AF: Johns Hopkins University, 11100 Johns Hopkins Road, Laurel, MD 20723-6099 United States
AU: Johnson, J R
AF: Princeton Plasma Physics Lab, POB 451 MS 28, Princeton, NJ 08543 United States
AU: Jen, J
AF: Johns Hopkins University, 11100 Johns Hopkins Road, Laurel, MD 20723-6099 United States
AU: Carr, S
AF: Johns Hopkins University, 11100 Johns Hopkins Road, Laurel, MD 20723-6099 United States
AU: Sibeck, D G
AF: NASA/GSFC, 8800 Greenbelt Rd, Greenbelt, MD 20771 United States
AU: Costello, K
AF: NASA/LSFC, Road, Houston, MD 00000 United States
AU: Freeman, J
AF: Rice University, POB 1892, Houston, MD 77005 United States
AU: Balikhin, M
AF: University of Sheffield, Mappin St, Sheffield, S1 3JD United Kingdom
AU: Bechtold, K
AF: Johns Hopkins University, 11100 Johns Hopkins Road, Laurel, MD 20723-6099 United States
AU: Bechtold, K
AF: Rice University, POB 1892, Houston, MD 77005 United States
AU: Vandegriff, J
AF: Johns Hopkins University, 11100 Johns Hopkins Road, Laurel, MD 20723-6099 United States
AB: Magnetically active times, e.g., Kp > 5, are notoriously difficult to predict, precisely when the predictions are crucial to the space weather users. Taking advantage of the routinely available solar wind measurements at Langrangian point (L1) and nowcast Kps, Kp forecast models based on neural networks were developed with the focus on improving the forecast for active times. In order to satisfy different needs and operational constraints, three models were developed: (1) model that inputs nowcast Kp, solar wind parameters, and predict Kp 1 hr ahead; (2) model with the same input as (1) and predict Kp 4 hr ahead; and (3) model that inputs only solar wind parameters and predict Kp 1 hr ahead (the exact prediction lead time depends on the solar wind speed and the location of the solar wind monitor). Extensive evaluations of these models and other major operational Kp forecast models show that while the new models can predict Kps more accurately for all activities, the most dramatic improvements occur for moderate and active times. The evaluations of the models over 2 solar cycles, 1975-2001, show that solar wind driven models predict Kp more accurately during solar maximum than solar minimum. This result, as well as information dynamics analysis of Kp, suggests that geospace is more dominated by internal dynamics during solar minimum than solar maximum, when it is more directly driven by external inputs, namely solar wind and IMF.
DE: 6979 Space and satellite communication
DE: 2447 Modeling and forecasting
DE: 2499 General or miscellaneous
DE: 2722 Forecasting
DE: 2784 Solar wind/magnetosphere interactions
SC: SPA-Aeronomy [SA]
MN: 2004 AGU Fall Meeting