Validating the Rice neural network and the wing Kp real-time models

摘要

The 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).