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A neural network-based foF2 model for a single station in the polar cap

机译:基于神经网络的foF2模型用于极地帽中的单个站点

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A neural network (NN) model has been developed for the critical frequency of the F2 layer (foF2) at Resolute (74.70°N, 265.10°E) using data obtained from the Space Physics Interactive Data Resource (no longer available) for the period between 1975 and 1995. This model is a first step toward addressing the discrepancies of the International Reference Ionosphere (IRI) foF2 or peak electron density (NmF2) at high latitudes recently presented by Themens et al. (2014). The performance of the NN model has been evaluated using foF2 data obtained from the Canadian Advanced Digital Ionosonde at Resolute (74.75°N, 265.00°E) for the period between 2009 and 2013, in comparison with the IRI predictions. The 2012 version and the International Union of Radio Science option of IRI have been used. The NN nighttime monthly median foF2 variation demonstrates good agreement with observations compared to the IRI. The NN model is able to reproduce the enhancements in foF2 during the equinoxes and also shows an improvement during disturbed days. Root mean square errors were computed between hourly and monthly median model predictions and observations, and on the whole, the NN model seems to perform better during low solar activity and the equinoxes. The NN model shows an improvement in performance on average by 26.638% for hourly foF2 and 32.636% for monthly median foF2, compared to 7.877% obtained for the same station by the most recent NN model that attempted to predict foF2 at a polar cap station (Oyeyemi and Poole, 2005).
机译:使用在一段时间内从太空物理互动数据资源(不再可用)获得的数据,开发了神经网络(NN)模型,用于确定F2层(foF2)在Resolute(74.70°N,265.10°E)的临界频率该模型是1975年Themens等人提出的解决国际参考电离层(IRI)foF2或峰值电子密度(NmF2)在高纬度方面的差异的第一步。 (2014)。 NN模型的性能已使用从加拿大先进数字离子探空仪在Resolute(74.75°N,265.00°E)处获得的foF2数据进行了评估,该数据与IRI的预测相比较(2009年至2013年)。使用了2012版和IRI的国际无线电科学联盟选件。与IRI相比,NN夜间每月foF2中位数变化与观测值显示出良好的一致性。 NN模型能够在春分时重现foF2的增强,并且在受干扰的日子也能显示出改善。均方根误差的计算是在每小时和每月中位数模型的预测和观察之间进行的,总的来说,NN模型在太阳活动低和春分低时表现更好。 NN模型显示每小时foF2的平均性能提高了26.638%,每月中位数foF2的性能平均提高了32.636%,相比之下,最新的NN模型试图在极地上限站预测foF2的平均性能为7.877%( Oyeyemi和Poole,2005年)。

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