摘要：We present the results of an efort to model quiet-time vertical plasma drifts in the low-latitude F-region ionosphere using the random forest machine learning technique.The model is capable of describing the climatological variation of the drifts as a function of universal time, day of the year, solar fux, and altitude (200–600 km).The model has been trained using measurements of the vertical plasma drifts made by the incoherent scatter radar of the Jicamarca Radio Observatory (11.95◦ S, 76.87◦ W, ∼ 1◦ dip lat).In our analysis, we compare our machine learning model results with the Scherliess and Fejer (J Geophys Res 104:6829–6842, 1999) model (SF99 model), a widely used empirical model of the vertical drifts developed using a diferent set of Jicamarca measurements.We fnd that the machine learning model is able to capture the overall features of the diurnal variation of the equatorial drifts for diferent seasonal and solar fux conditions.The model is also capable of capturing the mean height variation of the drifts, particularly the height gradient enhancements that have been observed near sunrise and sunset.Finally, the model can easily be expanded and improved as more drift measurements are made and become available for training.
关键词：Ionosphere; Drifts; Equatorial; Model; Machine learning; Random forest
