Polar mesospheric summer echoes (PMSEs) are very strong radar echoes
caused by the presence of ice particles, turbulence, and free electrons in
the mesosphere over polar regions. For more than three decades, PMSEs have
been used as natural tracers of the complicated atmospheric dynamics of this
region. Neutral winds and turbulence parameters have been obtained assuming
PMSE horizontal homogeneity on scales of tens of kilometers. Recent radar
imaging studies have shown that PMSEs are not homogeneous on these scales and
instead they are composed of kilometer-scale structures. In this paper, we
present a technique that allows PMSE observations with unprecedented angular
resolution (
The so-called MIMO (Multiple Input
Multiple Output) technique is being widely used in the fields of
telecommunications and radar remote sensing
Based on this successful implementation, we decided to implement coherent
MIMO to improve the angular resolution of the Middle Atmosphere ALOMAR Radar
System (MAARSY) (16.04
PMSEs are strong echoes, more than 50 dB stronger than expected echoes from
free electrons in the D region, and there is a consensus that they are
generated by atmospheric turbulence and require the presence of free
electrons and charged ice particles
Based on recent multibeam observations as well as radar imaging,
The results of
Given that PMSEs are highly associated with noctilucent clouds (NLCs)
Although progress has been made in discriminating between spatial and
temporal ambiguities in PMSE observations, the achieved angular resolution
has been mainly limited by two factors: (1) the effective area in the
visibility plane and (2) the number of independent spatial samples
In this work, we have implemented coherent MIMO at MAARSY using 3
spatially separated antenna sections on transmission and 15 on
reception. Moreover, time diversity was employed in order to isolate radar
echoes corresponding to each transmitting section; i.e., the transmitters
were interleaved every 4 ms. The resulting effective number of virtual
receivers by using MIMO was 45 and the angular resolution achieved was
Our paper is organized as follows. We first present the experiment
configuration with a specific emphasis on the MIMO implementation. Then we
describe the radar imaging implementation for both Capon and MaxEnt
techniques. The PMSE results are shown in Sect.
MAARSY is an active phased antenna array operating at 53 MHz, located in
Andoya, Norway (69.30
Parameters of MAARSY MIMO experiment.
In order to improve the performance of our imaging experiment, we applied a
coherent MIMO technique
Depending on the transmitting and receiving antenna configuration, some
virtual receivers can be redundant. In our experiment, we carefully selected
the transmitting and receiving antenna configuration to get three special
redundant virtual receivers. These three redundant virtual receivers were used
for phase calibration of the transmitters as was done by
MAARSY antenna configuration for SIMO
In order to separate the contribution of each transmitter, a form of transmit
diversity was needed. In
As explained by
A quick comparison between the visibility (sampling domain) for SIMO and
MIMO, shown in Fig.
Before inverting Eq. (
Once the imaging system is calibrated we can invert Eq. (
As described by
Even when MIMO is used, the problem is still underdetermined. Thus, there are
infinite possible image solutions,
Figure
A 24-bit image of a range–time Doppler intensity (RTDI) plot of PMSEs using MIMO, with time diversity conducted on 16 and 17 July 2017. The signal intensity is represented as lightness, Doppler information as hue, and spectral width as saturation. The legend on the left represents the SNR vs. Doppler color map for a saturation of 90 %. The legend on the right represents the spectral width vs. Doppler for a lightness of 50 %. Note that only the signal corresponding to the narrow region in the illuminated area is shown.
Since the estimated brightness is expressed in polar coordinates
Examples of EW–NS and EW–altitude 2-D images for Event 1 obtained by
applying Capon and MaxEnt to two different antenna configurations, SIMO and
MIMO, are shown in Figs.
EW–NS images for
Similar to Fig.
Coming back to our comparison of SIMO vs. MIMO, with MIMO-MaxEnt, small wave-like
structures of 2 km wavelength can be clearly observed, which are invisible
in SIMO implementations or MIMO-Capon. For example, observe the two
wavefronts at
We show similar 2-D cuts for Event 2 in Figs.
Same as Fig.
Same as Fig.
Having shown the better qualitative performance of MIMO-MaxEnt with respect to the other three implementations for the two selected events above, next we present extended results using just MIMO-MaxEnt.
Figure
24-bit time representation images of PMSE structures as a function of
altitude (RTDI)
The second and third row of Fig.
The keograms for Event 1, i.e., from 00:50 to 01:05 UTC, show that the
meridionally oriented wavefronts have a limited vertical extent centered at
85 km (Fig.
Figure
PMSEs have been used as a neutral wind tracer, assuming that
An animated sequence of the two events has been included in the Video supplement, i.e., Movies S1 and S2. For both events, the sequence includes selected cuts of EW–NS, EW–altitude, and NS–altitude. In Movie S1, we identify at least four examples of monochromatic waves with different wavelengths drifting with the wind in the northwest direction (at 23:57:37, 00:02:24, 00:10:57, and 00:55:33 UTC). Interestingly, in this case, longitudinal and transverse waves both drift with the background wind. In Movie S2, we show the complete evolution in time of Event 2. In the EW–altitude cut, the wave structure between 82 and 85 km drifts against the wind, whereas a layer at 87 km between 05:20 and 05:30 UTC follows the background wind. Note the projected radial wind (from red to blue) indicates a westward wind. These events are good examples of the complicated dynamics within PMSEs. Further analysis and interpretation of these high-resolution spatiotemporal structures will be done in a future work.
Figures
The 3-D contour plots at 00:55:33 UT on 17 July 2017,
i.e., Event 1, for four selected altitudes: 84, 84.6, 85.2, and 85.8 km.
The following are shown for each altitude:
Same as Fig
Assuming that the
The animated versions of Figs.
Making a quantitative comparison between SIMO and MIMO for real targets is
not an easy task. We need a prior knowledge of the brightness to make a good
analysis. This is not the case for PMSE. Fortunately, our observations
include echoes from specular meteors; see the bright echoes located at
(
In Fig.
Normalized angular power distribution of a specular meteor echo as a function of
Performance of imaging techniques.
Mean wind values for the two events presented.
We have shown qualitatively and quantitatively that radar imaging of PMSEs is significantly improved when using MIMO instead of SIMO configurations, by at least 50 %. Two different imaging methods have been applied, Capon and MaxEnt. As expected from previous works, MaxEnt images are better than Capon images; however, MaxEnt is computationally more demanding. Similarly, we found that the quality of MIMO-Capon is comparable to SIMO-MaxEnt.
Even though MIMO allows us to improve the point spread function, it is not
perfect. We expect some artifacts due to the sidelobes which are
The preliminary results using MIMO-MaxEnt are allowing us to observe PMSEs
with unprecedented horizontal resolution (
We will leave the detailed analysis and interpretation of these events and other events observed with this new capability for a future effort. In the following paragraphs, we discuss the technical results and propose future improvements.
The improved resolution using MIMO results from the larger effective
visibility aperture and the larger number of independent samples, as compared
to a SIMO configuration, i.e., 125 m instead of 76 m and 475 samples
instead of 163 samples, respectively. In addition, the MaxEnt approach allows
an improvement at least a factor of 2 in angular resolution compared to
Capon. The maximum number of horizontal blobs that could theoretically be
estimated for each range, time, and “color” (i.e., frequency bin) would be
79 (
Despite the significant improvement, not everything is positive about applying MIMO. In the following paragraphs, we discussed the critical points of applying MIMO in terms of (a) power-aperture reduction and (b) computational demands and real-time applicability.
As indicated by
MaxEnt is known to be computationally more demanding than Capon in SIMO
applications
In general, a critical point for PMSE imaging is the drifting nature of the
echoes. PMSE correlation times are relatively short, and under stationary
conditions, one would require a few minutes of incoherent integration to
reduce the statistical uncertainties of the visibility estimates. However,
the structures to be imaged might move between 2 and 5 km in 60 s for typical
mesospheric motions (40–80 m s
To deal with the drifting nature of PMSEs, in future studies we will explore
tracking techniques, i.e., make use of this information to improve the
angular resolution
An additional improvement to the current observations would be the use of
shorter pulses and therefore better range resolution, for example, 150 m.
Further improvement in range could also be accomplished by applying range
imaging
In this work, we have successfully implemented coherent MIMO
with radar imaging at MAARSY to observe PMSEs with unprecedented angular
resolution. The obtained resolution results from the combination of a larger
effective aperture, a higher number of independent visibility samples resulting
from MIMO, and improved angular resolution resulting from MaxEnt.
Quantitatively, the maximum angular resolution accomplished is
The preliminary results with MIMO-MaxEnt allowed us to clearly identify structures slightly less than 1 km in diameter and wave-like structures with horizontal wavelengths less than 10 km, with a time resolution around 60 s. The identification of such structures, with varying degrees of intensity, suggests that one has to be careful about using PMSEs for estimating the background wind assuming horizontal homogeneity. Not only is the vertical wind not homogeneous, but also the brightness is not homogeneous horizontally.
Given the relatively long temporal correlation of PMSEs, i.e., a few minutes, larger integration of the noisy visibility in time would allow fewer statistical uncertainties in the resulting images of the two events presented. However, PMSE structures drift as they are imaged; therefore long integration times result in angular smearing. In the future, we plan to use the drifting information to improve the angular resolution by applying tracking techniques.
As mentioned above, the implementation of MIMO-MaxEnt is computationally intensive and is currently not applicable to real-time processing. On the other hand, MIMO-Capon can be implemented in real-time processing. Our strategy for near-future observations would be to use MIMO-Capon for real-time processing and use MIMO-MaxEnt for special events until more efficient implementations and/or faster computers are available.
Our MIMO-MaxEnt results for the two events presented here,
namely the PMSE power amplitude as a function of EW, NS, altitude, and time,
are shared at
An image sequence for the two events presented in this work has been added as a supplement. These sequences show the time evolution of PMSE structures for selected EW, NS, and altitude cuts. An example of wave structures drifting with and against the wind is showed in Movies S1 and S2, respectively.
JMU and JLC conceived the idea. JLC, TW, and JMU discussed the theoretical framework. JLC, JMU, and RL designed the experiment. RL and JMU carried out the experiment. JMU processed the experimental data and performed the analysis. JLC contributed to the interpretation of the results. JMU wrote the manuscript with support from JLC. All authors provided critical feedback and helped to improve the manuscript.
The authors declare that they have no conflict of interest.
This article is part of the special issue “Layered phenomena in the mesopause region (ACP/AMT inter-journal SI)”. It is a result of the LPMR workshop 2017 (LPMR-2017), Kühlungsborn, Germany, 18–22 September 2017.
We would like to thank Toralf Renkwitz for providing the receivers' phase offsets and Marius Zecha for MAARSY data handling. This work was partially supported by the Deutsche Forschunggemeinschaft (DFG, German Research Foundation) under SPP 1788 (CoSIP)-CH1482/3-1 and by the WATILA Project (SAW-2015-IAP-1). The publication of this article was funded by the Open Access Fund of the Leibniz Association. Edited by: William Ward Reviewed by: Jia Yue and Ian McCrea