We present the results of an effort 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 flux, 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 ( $$11.95^\circ \hbox { S}$$ 11 . 95 ∘ S , $$76.87^\circ$$ 76 . 87 ∘ W, $$\sim 1^\circ$$ ∼ 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 different set of Jicamarca measurements. We find that the machine learning model is able to capture the overall features of the diurnal variation of the equatorial drifts for different seasonal and solar flux 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.

中文翻译：

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使用机器学习模拟赤道电离层垂直等离子体漂移

我们展示了使用随机森林机器学习技术对低纬度 F 区电离层中的安静时间垂直等离子体漂移进行建模的结果。该模型能够将漂移的气候变化描述为世界时、一年中的哪一天、太阳通量和高度（200-600 公里）的函数。该模型已经使用 Jicamarca Radio Observatory 的非相干散射雷达测量的垂直等离子体漂移进行了训练 ($11.95^\circ\hbox {S}$$ 11 . 95 ∘ S , $76.87^\circ$$76 . 87 ∘ W, $$\sim 1^\circ$$ ~ 1 ∘ dip lat）。在我们的分析中，我们将我们的机器学习模型结果与 Scherliess 和 Fejer (J Geophys Res 104:6829–6842, 1999) 模型（SF99 模型）进行比较，这是一种广泛使用的垂直漂移经验模型，使用不同的 Jicamarca测量。我们发现机器学习模型能够捕捉不同季节和太阳通量条件下赤道漂移的日变化的整体特征。该模型还能够捕捉漂移的平均高度变化，特别是在日出和日落附近观察到的高度梯度增强。最后，随着更多漂移测量的进行并可用于训练，该模型可以轻松扩展和改进。