the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
The MATS satellite: Limb image data processing and calibration
Abstract. MATS (Mesospheric Airglow/Aerosol Tomography and Spectroscopy) is a Swedish satellite mission designed to investigate atmospheric gravity waves. In order to observe wave patterns MATS observes structures in the O2 atmospheric band airglow (light emitted by oxygen molecules in the Mesosphere and Lower Thermosphere), as well as structures in noctilucent clouds which form around the Mesopause. The main instrument is a telescope that continuously captures high-resolution images of the atmospheric limb. Using tomographic analysis of the acquired images, the MATS mission can reconstruct waves in three dimensions and provide a comprehensive global map of the properties of gravity waves. The data provided by the MATS satellite will thus be 3-dimensional fields of airglow and NLC properties in 200-km-wide strips along the orbit at 70 to 110 km altitude. Adding spectroscopic analysis, by separating light into six distinct wavelength channels, it also becomes possible to derive temperature and microphysical NLC properties. Based on those data fields, further analysis will yield gravity wave parameters, such as wavelengths, amplitudes, phase, and direction of the waves, on a global scale.
The MATS satellite, funded by the Swedish National Space Agency, was launched in November 2022 into a 580 km sun-synchronous orbit with a 17.25 local time of the ascending node (LTAN). This paper accompanies the public release of the level 1b (v. 1.0) data set from the MATS limb imager. The purpose of the paper is to provide background information in order to assist users to correctly and efficiently handle the data. As such, it details the image processing and how instrumental artefacts are handled. It also describes the calibration efforts that have been carried out on the basis of laboratory and in-flight observations, and it discusses uncertainties that affect the dataset.
Competing interests: JG 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 preprint. The responsibility to include appropriate place names lies with the authors.- Preprint
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RC1: 'Comment on egusphere-2025-265', Anonymous Referee #2, 11 Apr 2025
This article deals with the data processing and calibration of MATS satellite images. This is important for assessing data quality in scientific studies such as gravity waves and NLC characteristics in the mesosphere and lower thermosphere (M-LT). Gravity waves are an important component of the dynamics of the M-LT region and a good description of their three-dimensional structure is necessary to better understand their role and the mechanisms involved.
The description of all the data processing phases is very detailed. All the error budget terms are carefully estimated. This document could be an interesting contribution to AMT. However, it lacks a section showing some examples of scientific results to really assess whether the quality of the processed and calibrated data is sufficient to meet the main scientific objectives of the MATS mission on gravity waves and NLCs.
A few minor comments are made below:
- - Section 5.8.4: to show the in-flight degradation of the UV channels, it would be preferable to show the calibration coefficient as a function of time over the whole dataset.
- - Section 5.8.4 and Table 1: What is the database of star spectra used in the calibration? Please give the reference.
- - Lines 660-663: I am not convinced that the variation in polarization along the orbit is less than the variability in Rayleigh scattering due to variation in atmospheric density. Please justify with figures.
Citation: https://doi.org/10.5194/egusphere-2025-265-RC1 -
RC2: 'Comment on egusphere-2025-265', Anonymous Referee #1, 28 Apr 2025
This paper, for the most part, is very well written and is certainly relevant to AMT. I recommend the paper be published after a few minor issues, detailed below, are addressed.
One general comment is that I would consider using the term “uncertainty” and avoid the use of “error”, especially when discussing random uncertainties. In statistics, “error” is often used as a specific term which essentially means a known bias that can be corrected for. If you’re just using it in the generic sense of some deviation from the truth, “error” should be fine, but in this study it seems like what you’re mostly discussing is uncertainties. There’s a discussion of this in section 3.1 of the TUNER paper, https://doi.org/10.5194/amt-13-4393-202.
Line 7 – this sounds like 200-km in the direction of the orbit, is that what is meant? I would expect perpendicular to the orbital track. Maybe give both?
Lines 26-27 – “Firstly, gravity waves are relatively smallscale phenomena; thus, high-resolution observations are needed to detect them.” Please give examples of what the authors deem to be “small” and “high”.
Lines 35-38 – I recommend also discussing Aura/MLS when discussing other limb sounders as they also measure MLT temperatures and I believe are able to do tomographic retrievals.
Lines 52-53 – this makes it sound like the 200 km swath is in the nadir direction, please rephrase
Line 114 – when I first read this, I thought it was saying that smearing hasn’t been accounted for in the current L0-L1 processing, but it seems like it is. Please rephrase
Lines 180-182 – I’m not a python user, so I’m just assuming that “Scipy Rotation” is a Python package? I don’t think this is the best way to describe how you do this step. Maybe describe what is actually done by this “align_vector” function. Honestly, the function you use might simply be an unnecessary detail.
Line 213 – I’m not seeing any mention of pixel solid angle in section 5.8.4, but rather in the intro of section 5.8.
Equations 6-7 – It’s quite possible I’m missing something very obvious here, but shouldn’t Ω (units of sr) be in the denominator? If this equation is correct, wouldn’t that give L units of photons sr / m2 / s instead of what it should be, photons / sr / m2 / s? Please specify what the units are for all variables in these equations.
Line 267 (and Fig 5 caption) – what is meant by “server”?
Figures 3-4 – no notes! I like them!
Figure 5 – Could be nice to see a panel with an example of an event-free capture beside this
Line 313 – Should “darks” be “dark images”?
Lines 346-348 – how often are natural signals expected to be strong enough that measurements will reach this saturation threshold?
Figure 8 – I find everything about this figure confusing. The caption states, “the bottom of the MATS image is the top row of the matrix…” I don’t understand why this choice for the figure was made, it seems arbitrarily confusing. Then “so that readout occurs downwards in the figure but upwards in the MATS image.” But it appears that the readout occurs upwards in the figure and the main text clearly states that readout is downwards in the MATS image. This next issue might just be a pdf interpreter issue, but I’m seeing the colours as yellow and blue, not red and green. Also, in the figure, the text “atmospheric signal not read out” is also confusing as it could mean either “atmospheric signal that is never read out” or “atmospheric signal yet to be read out”. I would maybe call it something along this lines of “illuminated region” or “contaminated zone” or “low altitude, not measured”. Although, even if all of this wasn’t confusing, I don’t think I would find this figure helpful and I’d probably recommend simply omitting it.
Line 617 – please define what is meant by “small”
Line 713 – “manifesting” should be “manifest”
Citation: https://doi.org/10.5194/egusphere-2025-265-RC2
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