Comparison of Dst forecast models for intense geomagnetic storms

Abstract

[1] We have compared six Dst forecast models using 63 intense geomagnetic storms (Dst ≤ À100 nT) that occurred from 1998 to 2006. For comparison, we estimated linear correlation coefficients and RMS errors between the observed Dst data and the predicted Dst during the geomagnetic storm period as well as the difference of the value of minimum Dst (DDst min ) and the difference in the absolute value of Dst minimum time (Dt Dst ) between the observed and the predicted. As a result, we found that the model by Li (2002, 2006) gives the best prediction for all parameters when all 63 events are considered. The model gives the average values: the linear correlation coefficient of 0.94, the RMS error of 14.8 nT, the DDst min of 7.7 nT, and the absolute value of Dt Dst of 1.5 hour. For further comparison, we classified the storm events into two groups according to the magnitude of Dst. We found that the model of Lee (2002, 2006) is better than the other models for the events having À100 ≤ Dst < À200 nT, and three recent models (the model of Wang et al. (2003), the model of Li (2002, 2006), and the model of Boynton et al. (2011b)) are better than the other three models for the events having Dst ≤ À200 nT.

Forecasting the Dst index using a swarm‐optimized neural network

“…Nevertheless, based on the statistical analysis of these different ANN methods, our ANN+PSO method can be considered as a very accurate method. Still, a comparison can be made for some selected data sets; thus, we compared our method versus other six Dst forecast models for 1 h in advance (called B [ Burton et al , ], FL [ Fenrich and Luhmann , ], OM [ O'Brien and McPherron , ], TL [ Temerin and Li , ], W [ Wang et al , ], NM [ Boynton et al , ], and persistence P [ Witt and Malamud , ]) using 63 selected storms with Dst levels <−100 nT taken between 1998 and 2006, following the methodology and database proposed by Ji et al []. Then, we statistically compared these six methods using the correlation coefficient R , the root‐mean‐square error, RMSE, the difference in the minimum value of forecasted and measured Dst values |Δ Dst min |, and the absolute timing difference between the observed and measured Dst values |Δ t Dst | to test the accuracy and performance of these methods.…”

Space Weather Effects Produced by the Ring Current Particles

“…At present, these types of models are in great demand, since they can give exact (though perhaps not always accurate) values at some time moments in a future. Ji et al (2012) compared the outputs from six Dst-index forecast models during intense geomagnetic storms, namely, Burton et al (1975), Fenrich and Luhmann (1998) ) also included Burton-type representations (Burton et al 1975;Murayama 1986), NARMAX model (Boynton et al 2011a), and Rice Dst model (Bala and Reiff 2012). All of the models showed very reasonable results, NARMAX model (Boynton et al 2011a) was specifically mentioned.…”

Probabilistic forecasting of the disturbance storm time index: An autoregressive Gaussian process approach

“…After selecting the best performing GP‐AR and GP‐ARX models in the validation phase, we test and compare the performance of these models with the predictions generated from the persistence model , on a set of 63 storm events occurring between 1998 and 2006 as given in Table , which is the same list of storm events as used in Ji et al [].…”