Development of a High‐Latitude Convection Model by Application of Machine Learning to SuperDARN Observations
Development of a High‐Latitude Convection Model by Application of Machine Learning to SuperDARN Observations
复制标题
将机器学习应用于 SuperDARN 观测开发高纬度对流模型
作者：
W. Bristow;C. Topliff;M. Cohen
提出了一个利用机器学习（ML）建立的北半球高纬对流新模型。ML算法随机森林回归应用于超级双极光雷达网络（superdam）观测数据的速度数据库，该数据库使用潜在映射技术Map - potential进行处理（Ruohoniemi & Baker, 1998, https://doi.org/10.1029/98ja01288）。用于训练模型的特征为行星际磁场分量Bx、By和Bz；太阳风速度，vsw；极光指数Au和Al；地磁指数SYM‐H。SuperDARN速度被分成南北分量和东西分量，并被分类到从55°到磁极的磁地方时-磁纬度网格中，在纬度上为2°，在MLT上为1 - hr。在网格的每个bin中为每个速度分量创建了单独的模型。研究发现，尽管每个箱中的模型彼此独立，但当这些模型集中在一起观察时，形成了连贯的对流模式。由此产生的对流模式通过扩张和收缩的方式响应极光指数的变化，这与对亚暴周期的预期一致。此外，我们还发现，该模式的预测值与观测值之间的均方差大大低于没有使用ML技术形成的现有气候学计算的相同数量。
A new model of northern hemisphere high‐latitude convection derived using machine learning (ML) is presented. The ML algorithm random forests regression was applied to a database of velocities derived from the Super Dual Auroral Radar Network (SuperDARN) observations processed with the potential mapping technique, Map‐Potential (Ruohoniemi & Baker, 1998, https://doi.org/10.1029/98ja01288). The features used to train the model were the interplanetary magnetic field components Bx, By, and Bz; the solar wind velocity, vsw; the auroral indices, Au and Al; and the geomagnetic index, SYM‐H. The SuperDARN velocities were separated into north‐south, and east‐west components and sorted into a magnetic local time ‐ magnetic latitude grid that ran from 55° to the magnetic pole with a bin size of 2° in latitude, and 1‐hr in MLT. Separate models were created for each velocity component in each bin of the grid. It is found that even though the models in each bin are independent of one another a coherent convection pattern is formed when the models are viewed in aggregate. The resulting convection pattern responds to changes in the auroral indices by expanding and contracting in a way that is consistent with expectations for a substorm cycle. Further it is found that the mean‐squared difference between predictions of the model and observed values of the velocity are substantially lower than the same quantity calculated for an existing climatology that was not formed with ML techniques.