Comparison between three algorithms for Dst predictions over the 2003–2005 period

Amata, E.; Pallocchia, G.; Consolini, Giuseppe; Marcucci, M. F.; Bertello, Igor

doi:10.1016/j.jastp.2007.08.041

Cited by 23 publications

(16 citation statements)

References 18 publications

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“…Another popular model for explaining solar eruptions is the breakout model (Antiochos et al 1999;Lynch et al 2008). In this model the eruption is again triggered by magnetic reconnection, but here the reconnection process occurs in a current sheet located above the sheared arcade.…”

Section: Cme Initiationmentioning

confidence: 99%

“…Model validation has also been carried out in a handful of studies on the performance of magnetic indices and specifically Dst, see Lundstedt et al (2002), Wei et al (2004), Pallocchia et al (2006), Temerin and Li (2006) and Amata et al (2008). However, much work has yet to be done to meet the space weather prediction requirements where much emphasis is put on numerical accuracy.…”

Section: Venues For Future Developmentmentioning

confidence: 99%

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Models of Solar Wind Structures and Their Interaction with the Earth’s Space Environment

et al. 2009

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The discipline of "Space Weather" is built on the scientific foundation of solarterrestrial physics but with a strong orientation toward applied research. Models describing the solar-terrestrial environment are therefore at the heart of this discipline, for both physical understanding of the processes involved and establishing predictive capabilities of the consequences of these processes. Depending on the requirements, purely physical models, semi-empirical or empirical models are considered to be the most appropriate. This review focuses on the interaction of solar wind disturbances with geospace. We J. Watermann ( ) Le Studium and LPC2E/CNRS, 234 J. Watermann et al.cover interplanetary space, the Earth's magnetosphere (with the exception of radiation belt physics), the ionosphere (with the exception of radio science), the neutral atmosphere and the ground (via electromagnetic induction fields). Space weather relevant state-of-the-art physical and semi-empirical models of the various regions are reviewed. They include models for interplanetary space, its quiet state and the evolution of recurrent and transient solar perturbations (corotating interaction regions, coronal mass ejections, their interplanetary remnants, and solar energetic particle fluxes). Models of coupled large-scale solar windmagnetosphere-ionosphere processes (global magnetohydrodynamic descriptions) and of inner magnetosphere processes (ring current dynamics) are discussed. Achievements in modeling the coupling between magnetospheric processes and the neutral and ionized upper and middle atmospheres are described. Finally we mention efforts to compile comprehensive and flexible models from selections of existing modules applicable to particular regions and conditions in interplanetary space and geospace.

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Section: Cme Initiationmentioning

confidence: 99%

Section: Venues For Future Developmentmentioning

confidence: 99%

Models of Solar Wind Structures and Their Interaction with the Earth’s Space Environment

et al. 2009

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“…Following Burton et al [1975], numerous attempts have been made to improve the Dst forecast model [ Murayama , 1986; Gonzalez et al , 1994, 2004; Thomsen et al , 1998; Klimas et al , 1998; Fenrich and Luhmann , 1998; O'Brien and McPherron , 2000; Wang et al , 2003; Temerin and Li , 2002, 2006; Xie et al , 2008]. Also, the Dst index was predicted by neural network methods [ Wu and Lundstedt , 1996, 1997; Barkhatov et al , 2000; Lundstedt et al , 2002; Watanabe et al , 2003; Pallocchia et al , 2006; Amata et al , 2008] and the NARMAX algorithm [ Boaghe et al , 2001; Boynton et al , 2011b]. Some of the above Dst forecast models are contributing to the prediction of geomagnetic storms via web pages as a part of the space weather forecast (e.g., http://sprg.ssl.berkeley.edu/dst-index/ and http://lasp.colorado.edu/space-weather/dsttemerin/dsttemerin.html).…”

Section: Introductionmentioning

confidence: 99%

Comparison of Dst forecast models for intense geomagnetic storms

Ji¹,

Moon²,

Gopalswamy³

et al. 2012

J. Geophys. Res.

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[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.

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“…The latest, third group uses various black box‐type statistical models, relating the solar wind and index: artificial neural networks, nonlinear auto‐regression schemes, etc. [ Valdivia et al , 1996; Vassiliadis et al , 1999; Lundstedt et al , 2002; Temerin and Li , 2002, 2006; Wei et al , 2004; Pallocchia et al , 2006; Sharifie et al , 2006; Zhu et al , 2006, 2007; Amata et al , 2008; Parnowski , 2009; Boynton et al, 2012]. These models with rather complex structure are capable to reveal information about the process without any prior assumptions.…”

Section: Introductionmentioning

confidence: 99%

Extended geomagnetic storm forecast ahead of available solar wind measurements

Podladchikova¹,

Petrukovich²

2012

Space Weather

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We develop a technique to predict geomagnetic storm magnitudes (peak Dst values) several hours in advance using the first indications of extreme solar wind conditions, as well as an assumption of constant driving function V Bs. For larger storms with clear V Bs jump we predict lower and upper limits of expected peak Dst. For smaller storms and storms with gradually increasing V Bs we predict Dst several hours ahead. The data from 1995–2010 were used to design the technique. The actual advance time of reliable forecasts (before the peak value is reached) is on average 5–6 hours, illustrating the realistic horizon of such “constant input” assumption. Larger storms are developing faster and thus are better predicted. False warnings (predictions more than 25% larger than the actual peak) occur in about 10% of events. The algorithm is implemented in real‐time in Space Research Institute, Moscow (www.spaceweather.ru).

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Comparison between three algorithms for Dst predictions over the 2003–2005 period

Cited by 23 publications

References 18 publications

Models of Solar Wind Structures and Their Interaction with the Earth’s Space Environment

Models of Solar Wind Structures and Their Interaction with the Earth’s Space Environment

Comparison of Dst forecast models for intense geomagnetic storms

Extended geomagnetic storm forecast ahead of available solar wind measurements

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