Abstract. Mesoscale dynamics in the mesosphere and lower thermosphere (MLT) region have been difficult to study from either ground- or satellite-based observations. For understanding of atmospheric coupling processes, important spatial scales at these altitudes range between tens to hundreds of kilometers in the horizontal plane. To date, this scale size is challenging observationally, and so structures are usually parameterized in global circulation models. The advent of multistatic specular meteor radar networks allows exploration of MLT mesocale dynamics on these scales using an increased number of detections and a diversity of viewing angles inherent to multistatic networks. In this work, we introduce a four dimensional wind field inversion method that makes use of Gaussian process regression (GPR), a non-parametric and Bayesian approach. The method takes measured projected wind velocities and prior distributions of the wind velocity as a function of space and time, specified by the user or estimated from the data, and produces posterior distributions for the wind velocity. Computation of the predictive posterior distribution is performed on sampled points of interest and is not necessarily regularly sampled. The main benefits of the GPR method include this non-gridded sampling, the built-in statistical uncertainty estimates, and the ability to horizontally-resolve winds on relatively small scales. The performance of the GPR implementation has been evaluated on Monte Carlo simulations with known distributions using the same spatial and temporal sampling as one day of real meteor measurements. Based on the simulation results we find that the GPR implementation is robust, providing wind fields that are statistically unbiased and with statistical variances that depend on the geometry and are proportional to the prior velocity variances. A conservative and fast approach can be straightforwardly implemented by employing overestimated prior variances and distances, while a more robust but computationally intensive approach can be implemented by employing training and fitting of model parameters. The latter GPR approach has been applied to a 24-hour data set and shown to compare well to previously used homogeneous and gradient methods. Small scale features have reasonably low statistical uncertainties, implying geophysical wind field horizontal structures as low as 20–50 km. We suggest that this GPR approach forms a suitable method for MLT regional and weather studies.

中文翻译：

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高斯过程回归在多静态镜面流星雷达观测中的四维中层和低层热层风场

摘要。无论是地面观测还是卫星观测，都很难研究中层和低热层（MLT）区域的中尺度动力学。为了理解大气耦合过程，在这些高度上重要的空间尺度在水平面上的范围在几十到几百公里之间。迄今为止，这种规模的规模在观察上具有挑战性，因此通常在整体环流模型中对结构进行参数化。多静态镜面流星雷达网络的出现允许使用增加数量的检测和多静态网络固有的多种视角在这些尺度上探索MLT中线动力学。在这项工作中，我们介绍了一种利用高斯过程回归（GPR），非参数和贝叶斯方法的四维风场反演方法。该方法将测得的预计风速和风速的先验分布作为空间和时间的函数，由用户指定或从数据中估算出来，并得出风速的后验分布。预测后验分布是在感兴趣的采样点上执行的，不一定必须定期采样。GPR方法的主要优点包括这种非网格采样，内置的统计不确定性估计以及在较小规模上水平解析风的能力。GPR实施的性能已在蒙特卡罗模拟中以已知分布进行了评估，使用的分布与第一天的实际流星测量相同。根据仿真结果，我们发现GPR实施是可靠的，提供统计上无偏的风场，其统计差异取决于几何形状，并且与先前的速度差异成正比。可以通过采用高估的先验方差和距离来直接实现保守，快速的方法，而可以通过采用模型参数的训练和拟合来实现更鲁棒但计算量大的方法。后者的GPR方法已应用于24小时数据集，并显示出与以前使用的均质和梯度方法的比较。小尺度特征的统计不确定性较低，这意味着地球物理风场的水平结构可低至20-50 km。我们建议这种GPR方法为MLT区域和天气研究提供一种合适的方法。