Articles | Volume 22, issue 2
https://doi.org/10.5194/angeo-22-431-2004
https://doi.org/10.5194/angeo-22-431-2004
01 Jan 2004
 | 01 Jan 2004

Anomalous nighttime electron temperatures over Millstone Hill: a statistical study

V. V. Lobzin and A. V. Pavlov

Abstract. A statistical study of anomalous nighttime electron temperature enhancements, NETEs, observed on 336 nights during Millstone Hill radar measurements on 730 nights from 1976 to 2001 is carried out. NETEs are most frequent in winter and in autumn. The NETE occurrence has a maximum probability in February and a minimum probability in July. The asymmetry between spring and autumn NETE occurrences is found for NETEs, which are observed during geomagnetially quiet time periods. The calculated value of the NETE occurrence probability is decreased with the solar activity index F10.7 increase. The increase in a 3-h geomagnetic index Kp or the decrease in a 1-h geomagnetic index Dst leads to the increase in the NETE occurrence probability. This tendency is more pronounced for current values of Kp or Dst rather than for delayed ones and becomes more weak with the delay increase. The NETEs are most likely to begin between 19:00 and 20:00 UT. The studied NETEs are characterized by the most typical duration from 1 to 3h with the percentage peak between 1 and 2h. The electron temperature increases are predominately between 100K and 300K. We did not find any relationship between the amplitude and duration of the NETEs studied. It is shown that there is a tendency for the NETE amplitude to increase if the value of Kp or ∣Dst∣ increases. To determine whether there exists a difference between NETEs observed during different solar cycles, we chose the data subsets corresponding to 21 and 22solar cycles and performed the statistical studies for each subset. It was found that, within the errors, the corresponding dependencies are the same for the cycles considered and for the entire data set.

Key words. Ionosphere (plasma temperature and density; ionospheric disturbances; modeling and forecasting)