Search

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. [ABSTRACT FROM AUTHOR]

A 31 MHz meteor radar located in Svalbard has been used to observe polar mesospheric echoes (PMSE) during summer 2020. Data from 19 July was selected for detailed analysis, with a focus on extracting additional information to characterize the atmosphere in the PMSE region. The use of an all-sky meteor radar adds an additional use to data collected for meteor observations and enables the detection of PMSE layers across a wide field of view. Comparison with data from a 53.5 MHz narrow-beam MST radar shows good agreement in the morphology of the layer as detected between the two systems. Doppler spectra of PMSE layers reveal fine structure, including regions of enhanced return that move across the radar's field of view. The relationship between range and Doppler shift of off-zenith portions of the layer enable the estimation of wind speeds with high temporal resolution during PMSE conditions. Trials demonstrate good agreement between wind speeds obtained from PMSE Doppler spectra and those calculated from specular meteor trail radial velocities. Combined with the antenna polar diagram of the radar, this same relationship was used to infer the aspect sensitivity of observed PMSE backscatter, yielding a mean backscatter angular width of 6.6 ± 2.8°. A comparison of underdense meteor radar echo decay times during and outside of PMSE conditions did not demonstrate a strong correlation between the presence of PMSE and shortened underdense meteor radar echo durations. [ABSTRACT FROM AUTHOR]