Browsing by Author "Bala, Ramkumar"

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    Improvements in short-term forecasting of geomagnetic activity
    (American Geophysical Union, 2012) Bala, Ramkumar; Reiff, Patricia
    We have improved our space weather forecasting algorithms to now predict Dst and AE in addition to Kp for up to 6 h of forecast times. These predictions can be accessed in real time at http://mms.rice. edu/realtime/forecast.html. In addition, in the event of an ongoing or imminent activity, e-mail “alerts” based on key discriminator levels have been going out to our subscribers since October 2003. The neural network–based algorithms utilize ACE data to generate full 1, 3, and 6 h ahead predictions of these indices from the Boyle index, an empirical approximation that estimates the Earth’s polar cap potential using solar wind parameters. Our models yield correlation coefficients of over 0.88, 0.86, and 0.83 for 1 h predictions of Kp, Dst, and AE, respectively, and 0.86, 0.84, and 0.80 when predicting the same but 3 h ahead. Our 6 h ahead predictions, however, have slightly higher uncertainties. Furthermore, the paper also tests other solar wind functions—the Newell driver, the Borovsky control function, and adding solar wind pressure term to the Boyle index—for their ability to predict geomagnetic activity.
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    Testing the estimated hypothetical response of a major CME impact on Earth and its implications to space weather
    (Wiley, 2015) Bala, Ramkumar; Reiff, Patricia; Russell, C.T.
    The high-speed coronal mass ejection (CME), ejected on 23 July 2012, observed by STEREO-A on the same day as the leading edge of the CME arrived at 1AU was unique both in respect to the observed plasma and magnetic structure and the large solar energetic particle flux that dynamically regulated the shock front. Because of its great intensity, it has been hailed as “Carrington 2” by some, warning that, had that CME been heading toward the Earth, it might have caused a major space weather event. We used the Rice Artificial Neural Network algorithms with the solar wind and interplanetary magnetic field parameters measured in situ by STEREO-A as inputs to infer what the “geoeffectiveness” of that storm might have been. We have also used an MHD model in Open Geospace General Circulation Model to understand the global magnetospheric process in time sequence. We presently show our neural network models of Kp and Dst on our real-time prediction site: http://mms.rice.edu/realtime/forecast.html. Running this event through our models showed that, in fact, this would have been an exceptional event. Our results show a prediction resulting in a Kp value of 8+, a Dst of nearly −250 nT, but when assumptions about maximum dipole angle tilt and density are made, predictions resulting in Kp of 11− and Dst dipping close to −700 nT are found. Finally, when solar energetic proton flux is included, the Kp and Dst predictions drop to 8− and ≈−625 nT, respectively.