Kp forecast models

Details Kp forecast models Kp forecast models

Dec 2004

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2004agufmsa51b0242m&link_type=abstract

American Geophysical Union, Fall Meeting 2004, abstract #SA51B-0242

Other

6979 Space And Satellite Communication, 2447 Modeling And Forecasting, 2499 General Or Miscellaneous, 2722 Forecasting, 2784 Solar Wind/Magnetosphere Interactions

Scientific paper

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.

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