Enabling process science with CubeSat intersections: An orbit resampling study inspired by PREFIRE

Vos, Natasha; L'Ecuyer, Tristan S.; Michaels, Tim

doi:https://doi.org/10.5194/egusphere-2024-2040

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https://doi.org/10.5194/egusphere-2024-2040

Preprints

22 Jul 2024

| 22 Jul 2024

Status: this preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).

Enabling process science with CubeSat intersections: An orbit resampling study inspired by PREFIRE

Natasha Vos, Tristan S. L'Ecuyer, and Tim Michaels

Abstract. The Polar Radiant Energy in the Far-InfraRed Experiment (PREFIRE) will use two 6U CubeSats to continuously measure spectral far-infrared (FIR) emissions for the first time in the modern satellite era. By strategically operating two CubeSats in separate sun-synchronous orbits, PREFIRE will achieve frequent orbit resampling ("intersections") that afford insights into the underlying polar processes that modulate FIR emissions. These orbit intersections are integral to PREFIRE science and will likely feature prominently in future CubeSat missions, motivating methods to characterize resampling distributions. In this study, we develop new methods to locate orbit intersections and extract co-located pixels within crossovers. Such methods are applied to simulated PREFIRE orbits to characterize the spatial and temporal distribution of hypothetical PREFIRE intersections and identify the subset with short revisit times that can be used to inter-calibrate the PREFIRE sensors. The analysis confirms that hundreds of intersections are anticipated each day, with the majority (> 75 %) occurring poleward of 66.5°. Inter-calibration intersections are concentrated between 72° and 78° N/S and will be used to monitor changes in spectral differences between PREFIRE sensors.

Generalizing the analysis to pairs of polar orbiting CubeSats with different equatorial crossing times, we conclude that the second CubeSat nearly quadruples the number of total intersections available for polar process studies. Spatial and temporal resampling coverage is clearly enhanced when more than one CubeSat is deployed, securing greater latitudinal representation and more diverse time differences between crossovers. The spatio-temporal profile of intersections between CubeSats varies with the relative offset in their equatorial crossing times. Further, when two CubeSats are placed at different altitudes, we find that their intersections exhibit time-varying, cyclic coverage, significantly increasing latitudinal coverage relative to two CubeSats placed at identical altitudes that resample each other in constant latitude bands. This study ultimately illustrates some factors to consider when designing future CubeSat science missions and outlines methods for conducting the associated trade studies.

Received: 03 Jul 2024 – Discussion started: 22 Jul 2024

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Natasha Vos, Tristan S. L'Ecuyer, and Tim Michaels

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'Comment on egusphere-2024-2040', Anonymous Referee #1, 27 Sep 2024 reply
This manuscript outlines a strategy to identify collocations between the swaths of one or more push-broom sensors. The method is applied to two months of simulated orbits for the PREFIRE constellation. The spatial distribution of collocations is displayed, highlighting that, for two sensors at the same altitude (or for a sensor with its previous orbits), collocations fall in bands of constant latitude while sensors at different altitudes achieve collocations at all latitudes. These distributions can be used in planning validation efforts for future satellite missions.
I recommend that this paper is rejected for publication because I cannot think of anyone that would benefit from reading it. The manuscript is well drafted with clear explanations and logic. With improved figures, it would make an excellent technical report. However, the algorithm described is largely a brute force search with efficiencies that rely on the geometry of PREFIRE’s swath. The authors claim that the methods will be useful to future CubeSat missions but they make no attempt to generalise their algorithm: referring to the “fourth scene” rather than the swath centre or sub-orbital track (S2.1.2); dedicating several plots to analysis of spectral differences that are fairly uncommon in satellite constellations (Fig. 14/15); asserting that intersections will be “relatively compact diamond shapes” when this will not be the case for sensors with a wide swath or point-and-scan strategies (S2.1.4); and providing no guidance on how to select a sensible altitude separation to achieve a desired distribution of collocations (Fig. 11).
I do not mean to denigrate the effort that went into this work. It is undeniably important to the PREFIRE team. Beyond them, far too many of us have spent far to long searching for collocations between satellite datasets. (A web search immediately reveals half-a-dozen “tools” to find them.) It would be beneficial to the community to agree best practice in this area, and this paper has a role to play in that conversation. But it adds only one team’s experience and followed too narrow a path to inspire greater action.
I add a few minor thoughts that the authors may wish to consider if the paper is revised, particularly if my overall judgement is found unduly harsh by the other reviewers.
I repeatedly desired a diagram that plainly illustrated the footprint of PREFIRE. In particular, I found it difficult to visualise how every sixth footprint is partially collocated. Was that in the across or along-track direction? Is the effect identical in each “scene” or does it vary? How does the pixel size relate to the spacing between them? Perhaps I’ve spent too long worrying about the MODIS bow-tie effect and overlaps between that sensor’s ten-line scans, but I struggled to create a mental image of what this paper described.

I found many of the figures unhelpful. To explain a few:
Fig. 2 looks like a flow chart but doesn’t summarise any of the logic applied. It appears to only specify every case that the example made in the text applies to.

Figs. 3 and 4 are explained as showing that the interpolation scheme introduces negligible differences between orbits but shows absolute values rather than differences. This makes the magnitude of difference impossible to assess, either because the colour images are identical to the human eye or because the points overlap.

Tab. 2 provides standard deviations for quantities that are obviously not normally distributed.

Figs. 6 and 7 provide absolute counts of collocations. This might have been a clever way of avoiding making a colour legend but I struggle to interpret “counts”. Perhaps omit entirely or plot the number of collocations relative to the number of pixels at that latitude?

Figs. 14 and 15 use two whole pages to convey that the difference in spectral radiance between satellites largely does not depend on scene. There must be a more efficient way of doing that, given almost every frame looks the same.

Though the introductory details of the PREFIRE mission were interesting, they aren’t particularly relevant to the algorithms and data presented. One or more pages could be saved by trimming these down to a brief introduction.

The fact that there are a fixed number of self-overlaps for each orbit, and that they fall at fixed latitudes that vary with altitude, strongly hints at there being a functional way of deriving many of the results of this work. The mathematics to do so are almost certainly beyond me so there is obligation to work it out, but I’d have been much more interested in this paper if it had attempted to empirically derive that relationship from an ensemble of simulated orbits.

Section 2 cites a number of mathematically apt methods of collocation but, as best I can tell, fails to mention the theory underpinning many of the “collocation tools” that NASA and ESA have funded over the last few decades. I’m familiar with the Community Intercomparison Suite but one of its many competitors would be better suited to your application.

L138: Did you mean “skipping every sixth along-track segment”?

L149: You omit pixel pairs located within 500 km. Was there any physical motivation for this choice, as it strikes me that near the poles there may be almost complete overlap between orbits so they could occur quite close together.

L172: You have my sympathy. My methods usually fail at the International Date Line.

S1.4: In would be curious to know more about why you omitted repeating ground tracks. These seem ideal for your purposes, albeit with a long temporal separation.

S2.2: The first paragraph caught my attention. I’ve had great success using the Python package shapely to efficiently manipulate the overlap of orbit tracks. Getting the pixels in the right order to produce a valid polygon is annoying, but the method explained in the rest of this section strikes me an as ad hoc solution. I’m not saying its wrong, but fixes like this section are what inclined me to reject the paper as the logic herein would be difficult to adapt to another sensor.

Something horrible has happened to page number 18.

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Citation: https://doi.org/10.5194/egusphere-2024-2040-RC1

Natasha Vos, Tristan S. L'Ecuyer, and Tim Michaels

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Short summary

PREFIRE uses two CubeSats to make novel measurements of outgoing energy. The CubeSats will frequently resample regions, forming orbit “intersections” that reveal how polar processes impact thermal emissions. This study develops new methods to characterize orbit intersections and applies them to simulated PREFIRE orbits to assess the hypothetical resampling distribution. Generalizing our results informs future missions that two CubeSats at different altitudes greatly enhance resampling coverage.


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