Statistical analysis and verification of 3‐hourly geomagnetic activity probability predictions

Wang, Jingjing; Zhong, Qing; Liu, Siqing; Miao, Jipeng; Liu, Fanghua; Li, Zhitao; Tang, Weiwei

doi:10.1002/2015sw001251

Cited by 12 publications

(12 citation statements)

References 19 publications

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“…Correlations between Int(ap) and the time-integrated B or North-South IMF component B z are weaker, sensibly smaller than the correlation between IntDst and Int(|B z |) found by [46]. As [72] have noticed good correlations between the 3-hour Kp index and peak values of some solar wind parameters during the preceding hours, we further examined different variants of this type and found a significant correlation (R = 0.64, not shown) between Int(ap) and the peak value over each Int(ap) > 1000 nT·hr event of the product of V sw (t 0 ) 2 B(t 0 ) by the maximum value of V sw (t) 2 B(t) over the 12 hours following t 0 .…”

Section: Best Fits To the Tail Of The Int(ap) Distribution And Physicmentioning

confidence: 73%

“…This is actually a key point for providing useful forecasts of the impact of such extreme events on satellites, because relativistic electrons fluxes usually reach their highest level near the end of high ap periods [40,65,28,63,49]. Combining the above predictor with other predictors of ap, V sw , and IMF B with several hours of lead time (e.g., [74,72,52]) might enable one to further increase the lead time of the predictions, or to suppress false alarms.…”

Section: A Preliminary Scheme For Predicting the Strength Of Extreme mentioning

confidence: 99%

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Statistics of Extreme Time‐Integrated Geomagnetic Activity

Mourenas

Artemyev

Zhang

2018

Geophysical Research Letters

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A statistical analysis of the time‐integrated Dst index is performed over 1958–2007. The tail of the probability distribution of extreme time‐integrated Dst events, which occur during strong geomagnetic storms, can be precisely fitted by a power law function with upper cutoff, apparently not exceeded even by the 1859 Carrington event. This time‐integrated Dst is expected to be a reasonable proxy for maximum densities of MeV electrons in the heart of the outer radiation belt, which are known to pose a serious threat to satellites. During such strong events, a correlation is found between the time‐integrated levels of various physical quantities, such as interplanetary magnetic field Bz, particle energy fluxes measured during injections in the magnetotail, geosynchronous ULF wave index, and geomagnetic activity in the inner magnetosphere, suggesting cumulative effects from successive disturbances.

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Section: Best Fits To the Tail Of The Int(ap) Distribution And Physicmentioning

confidence: 73%

Section: A Preliminary Scheme For Predicting the Strength Of Extreme mentioning

confidence: 99%

Statistics of Extreme Time‐Integrated Geomagnetic Activity

Mourenas

Artemyev

Zhang

2018

Geophysical Research Letters

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“…They also showed how the performance of the method can be improved by using the near‐real‐time values of the Kp index derived from near‐real‐time ground observations. The application of various ML tools has been the subject of a number of recent studies (Ayala Solares et al, ; Bala & Reiff, ; Ji et al, ; Tan et al, ; Wang et al, ; Wintoft et al, ).…”

Section: Introductionmentioning

confidence: 99%

Nowcasting and Predicting the Kp Index Using Historical Values and Real‐Time Observations

2019

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Current algorithms for the real-time prediction of the Kp index use a combination of models empirically driven by solar wind measurements at the L1 Lagrange point and historical values of the index. In this study, we explore the limitations of this approach, examining the forecast for short and long lead times using measurements at L1 and Kp time series as input to artificial neural networks. We explore the relative efficiency of the solar wind-based predictions, predictions based on recurrence, and predictions based on persistence. Our modeling results show that for short-term forecasts of approximately half a day, the addition of the historical values of Kp to the measured solar wind values provides a barely noticeable improvement. For a longer-term forecast of more than 2 days, predictions can be made using recurrence only, while solar wind measurements provide very little improvement for a forecast with long horizon times. We also examine predictions for disturbed and quiet geomagnetic activity conditions. Our results show that the paucity of historical measurements of the solar wind for high Kp results in a lower accuracy of predictions during disturbed conditions. Rebalancing of input data can help tailor the predictions for more disturbed conditions.

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“…The best 3 hr in advance result for RMSE is 0.7582. Finally, Wang proposed a probabilistic model combining solar wind and interplanetary magnetic field (IMF) parameters (Wang et al, ). They analyzed the probability of the occurrence of several levels of geomagnetic storms.…”

Section: Introductionmentioning

confidence: 99%

Geomagnetic Index Kp Forecasting With LSTM

Tan

Wang

et al. 2018

Space Weather

Self Cite

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Through making full use of the solar wind and interplanetary magnetic field data accumulated by ACE satellites we improve the prediction accuracy of the Kp geomagnetic index and accurately predict the occurrence of geomagnetic storms (Kp ≥ 5). Specially, we use long short‐term memory to train the Kp forecast model described in this study. Based on the large‐scale data, we build the Kp forecasting model with solar wind, interplanetary magnetic field parameters, and the historical Kp value as input. In this study, we first analyze the distribution of Kp and the effect of the data imbalance on the prediction of geomagnetic storms. Second, we analyze the correlation between the different input parameters and Kp. Thus, the input parameters of the model are selected by the results of the correlation. We consider two types of forecasting: one is the overall Kp forecasting and the other is the geomagnetic storm (Kp ≥ 5) forecasting. Hence, we design an integrated model which is then compared with other models. Some evaluation parameters are introduced: the root‐mean‐square error, the mean‐absolute error, and the correlation coefficient, as well as the measurement of geomagnetic storms (Kp ≥ 5) F1. The root‐mean‐square error and mean‐absolute error of our model are 0.4765 and 0.6382, respectively. The experimental results show that the proposed model with long short‐term memory improve the Kp forecasting.

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Statistical analysis and verification of 3‐hourly geomagnetic activity probability predictions

Cited by 12 publications

References 19 publications

Statistics of Extreme Time‐Integrated Geomagnetic Activity

Statistics of Extreme Time‐Integrated Geomagnetic Activity

Nowcasting and Predicting the Kp Index Using Historical Values and Real‐Time Observations

Geomagnetic Index Kp Forecasting With LSTM

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