the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 16 Apr 2026
Comparison of tomographic wind retrievals with different geometric implementations for multistatic meteor radar networks
Abstract. A growing number of multistatic meteor radar networks are being established worldwide. The multistatic geometry with overlapping observation volumes of several meteor radars or passive receivers permits the inference of higher-order kinematic properties of the wind field or even the retrieval of arbitrary wind fields using tomographic algorithms. Furthermore, there is the long-standing question of the reliability of the vertical wind. In this study, we present a novel Volume Velocity Processing in spherical coordinates and perform an initial cross-comparison to previous implementations of the Volume Velocity Processing and the advanced 3DVAR+DIV retrieval. We performed a detailed climatological and multiyear comparison of mean winds, horizontal divergence, relative vorticity, stretching, and shearing deformation using observations of the Nordic Meteor Radar Cluster consisting of the meteor radars at Tromsø, Alta, Kiruna, and Sodankylä. Our results underscore that the spherical implementation of Volume Velocity Processing reduces/minimizes altitude-dependent biases caused by projection errors resulting from an incomplete representation of the observation geometry in the mean horizontal and vertical winds. All algorithms exhibit a very high correlation for the mean horizontal winds, but we found substantial differences in the vertical wind velocity and for the higher-order kinematic properties between the novel algorithm compared to previous versions of the Volume Velocity Processing. Furthermore, the novel algorithm reproduces a consistent seasonal pattern of the vertical velocity with upwelling during the hemispheric summer at the altitude of the zonal wind reversal and a corresponding but weaker downwelling during the winter months. The magnitudes of the vertical wind appear to be physically consistent with theoretically expected upward and downward motions and are in the order of a few cm/s. We also identified a scaling effect of the vertical wind in dependence on the temporal resolution and spatial averaging represented by a circle of influence in the new retrieval, which was confirmed by the measurement response of the 3DVAR+DIV retrieval. The most reliable vertical winds were obtained for a temporal resolution of 15–30 minutes and a spatial domain of about 200–250 km centered between all meteor radars of the Nordic Meteor Radar Cluster.
Competing interests: Wen Yi is a member of the editorial board of Atmospheric Measurement Techniques.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.- Preprint
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Status: open (until 07 Jun 2026)
- RC1: 'Comment on egusphere-2025-6377', Anonymous Referee #1, 04 May 2026 reply
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Review Report
This manuscript presents a significant methodological advancement for multistatic meteor radar wind retrievals by formulating the Volume Velocity Processing (VVP) in a rigorous spherical coordinate system (SVVP). The authors demonstrate the impact of this new implementation using four years of observations from the Nordic Meteor Radar Cluster and provide a thorough intercomparison with both the traditional plane‑geometry VVP and the more sophisticated 3DVAR+DIV tomographic retrieval. The topic is timely, the analysis is comprehensive, and the results convincingly show that accounting for Earth’s curvature is essential—particularly for vertical wind estimates and higher‑order kinematic quantities. The manuscript is generally well structured and the figures effectively support the narrative.
Nevertheless, several aspects require improvement before the paper can be recommended for publication in Atmospheric Measurement Techniques. These concern the presentation and discussion of the validation/uncertainty aspects, the quality of the language, and the completeness of the figure captions and citations. Below I list specific comments and suggestions, separated into major and minor issues.
Major Comments
1. Treatment of vertical wind bias and uncertainty. The manuscript mentions that a bias correction of 1−4 cm s⁻¹ is subtracted from the vertical winds (lines 225–227, 417–423) based on the assumption that the long‑term mean vertical wind should be zero.
This correction is critical because the absolute values of the vertical wind are central to the conclusions. The authors should provide more details: how exactly is the bias estimated? Is it a single constant per station, per altitude, or per domain size?
The statistical significance of the debiased vertical wind patterns (e.g., upwelling of ~10 cm s⁻¹ in summer) should be evaluated, perhaps with confidence intervals or by showing that the bias‑subtracted values indeed vary seasonally and not merely as residual noise.
Moreover, the 3DVAR+DIV vertical winds are an order of magnitude larger (lines 328–330). The authors attribute this to lower meteor counts per grid cell and the Tikhonov regularization. A more quantitative explanation (e.g., typical measurement response, averaging kernels) would strengthen the discussion.
2. Intercomparison with 3DVAR+DIV and sensitivity to Tikhonov parameter. Section 4.3 presents a single snapshot (1 January 2021) to illustrate the effect of the regularization parameter.
The representativeness of this single case should be commented on. Are the differences shown robust across many days/seasons?
The text states that “the 3DVAR+DIV performs best with regularization strengths between α = 0.4~5. How was this range determined? A brief description of the metric used (e.g., cross‑validation, spectral analysis) would be helpful.
The comparison between VVP and 3DVAR+DIV vertical winds (Fig. 14) shows a regression slope of only 0.105 (spherical) and 0.019 (plane). The discussion merely notes that this “validates and [is] consistent with the climatology results”. Given the huge difference in magnitude, a deeper analysis of why the two methods disagree so strongly is warranted. The role of the divergence‑derived vertical wind in 3DVAR+DIV versus the Doppler‑derived vertical wind in SVVP should be explicitly addressed.
3. Temporal and spatial resolution sensitivity. The analysis of temporal resolution (15, 30, 60 min) and domain radius (200–400 km) is well motivated.
In Fig. 9 the vertical wind regression slopes deviate markedly from unity (1.965 for 15 vs 30 min, 0.530 for 60 vs 30 min). The authors suggest this reflects the gravity‑wave spectrum, but they do not investigate whether this scaling is linear or whether a “true” reference exists. A more systematic treatment—e.g., power spectral density comparisons or an estimate of the gravity‑wave contribution—would elevate the discussion.
The optimal radius (200–250 km) is justified by the 3DVAR+DIV measurement response (Fig. 15). However, the measurement response shown is for the 3DVAR+DIV retrieval, not for SVVP. It would be more direct to show, for the SVVP, how the number of meteors and the condition number of the least‑squares matrix vary with radius. The current argument is indirect and should be clarified.
Minor Comments