Multistatic meteor radar observations to assess the spatial variability of mesospheric/lower thermospheric winds using a 3DVAR+div tomographic retrieval to measure spatially resolved 3D winds

<p>The middle atmospheric circulation is driven by atmospheric waves, which carry energy and momentum from their source to the area of their dissipation and thus providing an energetic coupling between different atmospheric layers. A comprehensive understanding of the wave-wave or wave-mean flow interactions often requires a spatial characterization of these waves. Multistatic meteor radar observations provide an opportunity to investigate the spatial and temporal variability of mesospheric/lower thermospheric winds on regional scales. We apply the 3DVAR+div retrievals to observations from the Nordic Meteor Radar Cluster and the Chilean Observation Network De Meteor Radars (CONDOR). Here we present preliminary results of a new 3DVAR+div retrieval to infer the vertical wind variability using spatially resolved observations. The new retrieval includes the continuity equation in the forward model to ensure physical consistency in the vertical winds. Our preliminary results indicate that the vertical wind variability is about +/-2m/s. The 3DVAR+div algorithm provides spatially resolved winds resolves body forces of breaking gravity waves, which are typically indicated by two counterrotating vortices. Furthermore, we infer horizontal wavelength spectra for all 3 wind components to obtain spectral slopes indicating a transition of the vertical to the divergent mode at scales of about 80-120 km at the mesosphere.</p>

Global balanced wind derived from SABER temperature and pressure observations and its validations

Zonal winds in the stratosphere and mesosphere play important roles in atmospheric dynamics and aeronomy. However, the direct measurement of winds in this height range is difficult. We present a dataset of the monthly mean zonal wind in the height range of 18–100 km and at latitudes of 50∘ S–50∘ N from 2002 to 2019, derived by the gradient balance wind theory and the temperature and pressure observed by the SABER instrument. The tide alias above 80 km at the Equator is replaced by the monthly mean zonal wind measured by a meteor radar at 0.2∘ S. The dataset (named BU) is validated by comparing with the zonal wind from MERRA2 (MerU), UARP (UraU), the HWM14 empirical model (HwmU), meteor radar (MetU), and lidar (LidU) at seven stations from around 50∘ N to 29.7∘ S. At 18–70 km, BU and MerU have (i) nearly identical zero wind lines and (ii) year-to-year variations of the eastward and westward wind jets at middle and high latitudes, and (iii) the quasi-biennial oscillation (QBO) and semi-annual oscillation (SAO) especially the disrupted QBO in early 2016. The comparisons among BU, UraU, and HwmU show good agreement in general below 80 km. Above 80 km, the agreements among BU, UraU, HwmU, MetU, and LidU are good in general, except some discrepancies at limited heights and months. The BU data are archived as netCDF files and are available at https://doi.org/10.12176/01.99.00574 (Liu et al., 2021). The advantages of the global BU dataset are its large vertical extent (from the stratosphere to the lower thermosphere) and 18-year internally consistent time series (2002–2019). The BU data is useful to study the temporal variations with periods ranging from seasons to decades at 50∘ S–50∘ N. It can also be used as the background wind for atmospheric wave propagation.

Four-dimensional mesospheric and lower thermospheric wind fields using Gaussian process regression on multistatic specular meteor radar observations

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 and hundreds of kilometers in the horizontal plane. To date, this scale size is challenging observationally, so structures are usually parameterized in global circulation models. The advent of multistatic specular meteor radar networks allows exploration of MLT mesoscale 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), which is a nonparametric 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 1 d of real meteor measurements. Based on the simulation results we find that the GPR implementation is robust, providing wind fields that are statistically unbiased 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 hyperparameters. The latter GPR approach has been applied to a 24 h dataset 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.

Atmospheric tomography using the Nordic Meteor Radar Cluster and Chilean Observation Network De Meteor Radars: network details and 3D-Var retrieval

Ground-based remote sensing of atmospheric parameters is often limited to single station observations by vertical profiles at a certain geographic location. This is a limiting factor for investigating gravity wave dynamics as the spatial information is often missing, e.g., horizontal wavelength, propagation direction or intrinsic frequency. In this study, we present a new retrieval algorithm for multistatic meteor radar networks to obtain tomographic 3-D wind fields within a pre-defined domain area. The algorithm is part of the Agile Software for Gravity wAve Regional Dynamics (ASGARD) and called 3D-Var, and based on the optimal estimation technique and Bayesian statistics. The performance of the 3D-Var retrieval is demonstrated using two meteor radar networks: the Nordic Meteor Radar Cluster and the Chilean Observation Network De Meteor Radars (CONDOR). The optimal estimation implementation provide statistically sound solutions and diagnostics from the averaging kernels and measurement response. We present initial scientific results such as body forces of breaking gravity waves leading to two counter-rotating vortices and horizontal wavelength spectra indicating a transition between the rotational k−3 and divergent k-5/3 mode at scales of 80–120 km. In addition, we performed a keogram analysis over extended periods to reflect the latitudinal and temporal impact of a minor sudden stratospheric warming in December 2019. Finally, we demonstrate the applicability of the 3D-Var algorithm to perform large-scale retrievals to derive meteorological wind maps covering a latitude region from Svalbard, north of the European Arctic mainland, to central Norway.

Interhemispheric differences of mesosphere–lower thermosphere winds and tides investigated from three whole-atmosphere models and meteor radar observations

Long-term and continuous observations of mesospheric–lower thermospheric winds are rare, but they are important to investigate climatological changes at these altitudes on timescales of several years, covering a solar cycle and longer. Such long time series are a natural heritage of the mesosphere–lower thermosphere climate, and they are valuable to compare climate models or long-term runs of general circulation models (GCMs). Here we present a climatological comparison of wind observations from six meteor radars at two conjugate latitudes to validate the corresponding mean winds and atmospheric diurnal and semidiurnal tides from three GCMs, namely the Ground-to-Topside Model of Atmosphere and Ionosphere for Aeronomy (GAIA), the Whole Atmosphere Community Climate Model Extension (Specified Dynamics) (WACCM-X(SD)), and the Upper Atmosphere ICOsahedral Non-hydrostatic (UA-ICON) model. Our results indicate that there are interhemispheric differences in the seasonal characteristics of the diurnal and semidiurnal tide. There are also some differences in the mean wind climatologies of the models and the observations. Our results indicate that GAIA shows reasonable agreement with the meteor radar observations during the winter season, whereas WACCM-X(SD) shows better agreement with the radars for the hemispheric zonal summer wind reversal, which is more consistent with the meteor radar observations. The free-running UA-ICON tends to show similar winds and tides compared to WACCM-X(SD).

Mesospheric gravity wave activity estimated via airglow imagery, multistatic meteor radar, and SABER data taken during the SIMONe–2018 campaign

We describe in this study the analysis of small and large horizontal-scale gravity waves from datasets composed of images from multiple mesospheric airglow emissions as well as multistatic specular meteor radar (MSMR) winds collected in early November 2018, during the SIMONe–2018 (Spread-spectrum Interferometric Multi-static meteor radar Observing Network) campaign. These ground-based measurements are supported by temperature and neutral density profiles from TIMED/SABER (Thermosphere, Ionosphere, Mesosphere Energetics and Dynamics/Sounding of the Atmosphere using Broadband Emission Radiometry) satellite in orbits near Kühlungsborn, northern Germany (54.1∘ N, 11.8∘ E). The scientific goals here include the characterization of gravity waves and their interaction with the mean flow in the mesosphere and lower thermosphere and their relationship to dynamical conditions in the lower and upper atmosphere. We have obtained intrinsic parameters of small- and large-scale gravity waves and characterized their impact in the mesosphere via momentum flux (FM) and momentum flux divergence (FD) estimations. We have verified that a small percentage of the detected wave events is responsible for most of FM measured during the campaign from oscillations seen in the airglow brightness and MSMR winds taken over 45 h during four nights of clear-sky observations. From the analysis of small-scale gravity waves (λh < 725 km) seen in airglow images, we have found FM ranging from 0.04–24.74 m2 s−2 (1.62 ± 2.70 m2 s−2 on average). However, small-scale waves with FM > 3 m2 s−2 (11 % of the events) transport 50 % of the total measured FM. Likewise, wave events of FM > 10 m2 s−2 (2 % of the events) transport 20 % of the total. The examination of large-scale waves (λh > 725 km) seen simultaneously in airglow keograms and MSMR winds revealed amplitudes > 35 %, which translates into FM = 21.2–29.6 m2 s−2. In terms of gravity-wave–mean-flow interactions, these large FM waves could cause decelerations of FD = 22–41 m s−1 d−1 (small-scale waves) and FD = 38–43 m s−1 d−1 (large-scale waves) if breaking or dissipating within short distances in the mesosphere and lower thermosphere region.

Trends in the Airglow Temperatures in the MLT Region—Part 3: Ground-Based and SABER Measurements

Ground-based temperature measurements at Svalbard, Wuppertal, and Hohenpeissenberg were analyzed to obtain F10.7, Ap index, and Dst index trends. The trends were then compared to those obtained from Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) temperature measurements at the same locations. Trend analysis was carried out for overlapped time periods, full range of available data, and the CO2-detrended full range of available data. The Svalbard meteor radar (SABER) temperature showed a weak (moderate) correlation with F10.7 and a moderate (weak) correlation with Ap and Dst indices. The trends in the Wuppertal OH* temperature compare well with the SABER temperature when a full range of data is used in the analysis. Both temperatures had a similar F10.7 trend with the same level of correlation coefficient. The F10.7 trend in the Hohenpeissenberg OH* temperature compared well with that obtained by SABER, but the former displayed a weak correlation. The Hohenpeissenberg data displayed a very weak correlation with Ap and Dst indices. Our study clearly shows that a longer dataset would better capture trends in temperature, as was evidenced by the results of Wuppertal data. The CO2-detrended temperatures overall showed slightly larger trend values with a slightly better correlation.