In the present study, a statistical investigation is carried out to explore whether there is a relationship between the critical frequency （foEs） of the sporadic-E layer that is occasionally seen on the E region of ...In the present study, a statistical investigation is carried out to explore whether there is a relationship between the critical frequency （foEs） of the sporadic-E layer that is occasionally seen on the E region of the ionosphere and the quasi- biennial oscillation （QBO） that flows in the east-west direction in the equatorial stratosphere. Multiple regression model as a statistical tool was used to determine the relationship between variables. In this model, the stationarity of the variables （foEs and QBO） was firstly analyzed for each station （Cocos Island, Gibilmanna, Niue Island, and Tahiti）. Then, a co- integration test was made to determine the existence of a long-term relationship between QBO and foes. After verifying the presence of a long-term relationship between the variables, the magnitude of the relationship between variables was further determined using the multiple regression model. As a result, it is concluded that the variations in foEs were explainable with QBO measured at 10 hPa altitude at the rate of 69%, 94%, 79%, and 58% for Cocos Island, Gibilmanna, Niue Island, and Tahiti stations, respectively. It is observed that the variations in foes were explainable with QBO measured at 70 hPa altitude at the rate of 66%, 69%, 53%, and 47% for Cocos Island, Gibilmanna, Niue Island, and Tahiti stations, respectively.展开更多
文摘In the present study, a statistical investigation is carried out to explore whether there is a relationship between the critical frequency （foEs） of the sporadic-E layer that is occasionally seen on the E region of the ionosphere and the quasi- biennial oscillation （QBO） that flows in the east-west direction in the equatorial stratosphere. Multiple regression model as a statistical tool was used to determine the relationship between variables. In this model, the stationarity of the variables （foEs and QBO） was firstly analyzed for each station （Cocos Island, Gibilmanna, Niue Island, and Tahiti）. Then, a co- integration test was made to determine the existence of a long-term relationship between QBO and foes. After verifying the presence of a long-term relationship between the variables, the magnitude of the relationship between variables was further determined using the multiple regression model. As a result, it is concluded that the variations in foEs were explainable with QBO measured at 10 hPa altitude at the rate of 69%, 94%, 79%, and 58% for Cocos Island, Gibilmanna, Niue Island, and Tahiti stations, respectively. It is observed that the variations in foes were explainable with QBO measured at 70 hPa altitude at the rate of 66%, 69%, 53%, and 47% for Cocos Island, Gibilmanna, Niue Island, and Tahiti stations, respectively.
