A Kalman Filter Technique for Improving Medium-Term Predictions of the Sunspot Number |
| |
Authors: | T Podladchikova R Van?der Linden |
| |
Institution: | 1. Institute for Applied System Analysis, NTUU ??KPI??, Kiev, Ukraine 2. Solar?CTerrestrial Center of Excellence, ROB, Uccle, Belgium
|
| |
Abstract: | In this work we describe a technique developed to improve medium-term prediction methods of monthly smoothed sunspot numbers.
Each month, the predictions are updated using the last available observations (see the monthly output in real time at ). The improvement of the predictions is provided by applying an adaptive Kalman filter to the medium-term predictions obtained
by any other method, using the six-monthly mean values of sunspot numbers covering the six months between the last available
value of the 13-month running mean (the starting point for the predictions) and the “current time” (i.e. now). Our technique provides an effective estimate of the sunspot index at the current time. This estimate becomes the new
starting point for the updated prediction that is shifted six months ahead in comparison with the last available 13-month
running mean, and it provides an increase of prediction accuracy. Our technique has been tested on three medium-term prediction
methods that are currently in real-time operation: The McNish–Lincoln method (NGDC), the standard method (SIDC), and the combined
method (SIDC). With our technique, the prediction accuracy for the McNish–Lincoln method is increased by 17 – 30%, for the
standard method by 5 – 21% and for the combined method by 6 – 57%. |
| |
Keywords: | |
本文献已被 SpringerLink 等数据库收录! |
|