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dc.contributor.authorBala, Ramkumar
Reiff, Patricia
dc.date.accessioned 2014-08-01T19:09:24Z
dc.date.available 2014-08-01T19:09:24Z
dc.date.issued 2014
dc.identifier.citation Bala, Ramkumar and Reiff, Patricia. "Validating the Rice neural network and the Wing Kp real-time models." Space Weather, 12, (2014) American Geophysical Union: 417-425. http://dx.doi.org/10.1002/2014SW001075.
dc.identifier.urihttps://hdl.handle.net/1911/76318
dc.description.abstractThe Rice neural network models of Kp have been running in real time at http://mms.rice.edu/realtime/forecast.html since October 2007; Dst and AE models were added to our operations in May 2010. All these models use the Boyle index as basis functions computed from ACE real time inputs. Later, two more driving functions were included in November 2012: (a) the “Ram” functions that had dynamic pressure term added to the Boyle index and (b) the Newell functions. The Wing models are a set of neural network-based Kp forecast models adopted by NOAA/Space Weather Prediction Center in March 2011 to supersede the Costello Kp model. This study indicates that any of the three Rice neural net predictors had a better success rate than the Wing model in predicting Kp (r=0.828 with Boyle, r=0.843 with Ram, and r=0.820 with Newell for 1 h predictions; similarly, r=0.739, 0.769, and 0.755 for 3 h predictions) in real time. In a head-to-head challenge using harvested real-time outputs between April 2011 and February 2013, the Rice Boyle Kp models predicted better than the Wing models (0.771 versus 0.714 for 1 h predictions and 0.770 versus 0.744 for 3 h predictions). In addition, Wing’s prediction was missing more often than the Rice prediction (≈6% versus 4.6%), meaning it had less reliability. The Rice models also predict AE (r=0.811 with Boyle; 0.806 with Ram; 0.765 with Newell, and 0.743 with Boyle; 0.747 with Ram for 1 h and 3 h predictions) and pressure-corrected Dst (r=0.790; 0.767, and 0.704, and r=0.795; 0.797 and 0.707 for 1 h and 3 h predictions).
dc.language.iso eng
dc.publisher American Geophysical Union
dc.rights Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
dc.title Validating the Rice neural network and the Wing Kp real-time models
dc.type Journal article
dc.contributor.funder National Aeronautics and Space Administration Magnetospheric Multiscale Mission
dc.citation.journalTitle Space Weather
dc.citation.volumeNumber 12
dc.type.dcmi Text
dc.identifier.doihttp://dx.doi.org/10.1002/2014SW001075
dc.type.publication publisher version
dc.citation.firstpage 417
dc.citation.lastpage 425


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