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REVIEW 4 major objections 4 minor 50 references

On Dust Devil Diameters, Occurrence Rates, and Activity

T0 review · 4 major / 4 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read Dust devil diameters follow a universal -5/3 power law.

desk verdict The empirical survey work is useful, but the derivation of the D^-5/3 law is internally inconsistent — the paper should not be accepted until that is resolved. read the letter →

arxiv 2507.03643 v1 pith:ZOFYVCBL submitted 2025-07-04 astro-ph.EP

Brian Jackson , Lori Fenton , Ralph Lorenz , Chelle Szurgot , Joshua Gambill , Gwendolyn Arzaga This is my paper · ORCID
classification astro-ph.EP
keywords dustdevilsdiameterdistributionMarsatmosphereplanetaryboundarylayerconvectivevorticesdevilactivitypower-lawlifting
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper argues that dust devils, the swirling dust-lifting vortices common on Mars and in arid regions on Earth, draw in air from a "diameter of influence" much larger than their visible width, and that these intake areas pack together as tightly as possible wherever devils form. From that close-packing assumption plus a heat-engine model of vortex flow, it derives that the number of devils with diameter $D$ falls off as $D^{-5/3}$, and shows that several Mars surveys, once binned properly, agree with that slope. The paper also derives that the number of dust devils per unit area grows in proportion to the product of thermodynamic efficiency and surface sensible heat flux, a quantity called the dust devil activity. If correct, the work gives a simple, physically grounded way to turn a measured size distribution into a contribution to the atmospheric dust budget on both worlds.

What carries the argument

The load-bearing object is the diameter of influence $D_\infty$, the distance out to which a dust devil draws in air and angular momentum, which the model estimates as roughly ten times the visible diameter for an observed terrestrial devil. The argument hinges on two scaling steps: mass conservation and inflow geometry give $D/D_\infty \sim (2 \alpha H / w)^{1/3}$, and friction along the intake streamline gives $D_\infty \sim (2 f D^2 / \alpha^2)^{1/3}$; combining the second with an assumed $D_\infty^{-2}$ close-packing distribution and changing variables produces the $D^{-5/3}$ law. The same machinery, through the fractional area of convective updrafts, yields $N_0 \propto \eta F_s$.

What would settle it

Measure the influence radius directly with a dense pressure-sensor or wind network around dust devils of known visible diameter: the model predicts $D_\infty \propto D^{2/3}$ at fixed ambient shear and friction, so a measured scaling of $D_\infty$ with $D^1$ or $D^0$ would falsify Equation 16 and with it the -5/3 diameter distribution.

Watch

Extended reading notes

Core claim

The central discovery is a derivation of the dust devil diameter distribution from vortex kinematics and thermodynamics. Treating each devil as a small heat engine in cyclostrophic balance, with angular momentum supplied by ambient wind shear and inflow balanced by friction, the paper finds that the influence diameter grows with visible diameter as $D_\infty \sim (2 f D^2 / \alpha^2)^{1/3}$. Assuming these influence areas are close-packed so that the count per influence area falls as $D_\infty^{-2}$, the visible diameters must follow $dN/dD \propto D^{-5/3}$. The paper reports that power-law fits to martian diameters from several surveys agree with this exponent when Poisson-weighted, and that the same model yields $N_0 \propto \eta F_s$, directly linking dust devil counts to the dust devil activity index.

Load-bearing premise

The -5/3 law assumes a single population-averaged ambient wind shear and friction apply to every devil regardless of size, so if frictional force actually grows with intake velocity for larger devils, the exponent shifts and the claimed agreement with data is no longer secured.

Editorial extensions

If this is right

  • Population-wide dust injection estimates can be computed from a single universal diameter distribution, so surveys need only count devils rather than fully resolve each one's size.
  • Small dust devils dominate the population: for a factor of ten in diameter, there are about $10^{5/3} \approx 46$ times as many small devils, skewing dust flux toward the smallest members.
  • Dust devil areal density is predicted to track the dust devil activity index, so meteorological models of $\eta F_s$ translate directly into expected occurrence rates.
  • Larger devils should appear preferentially when the dust devil activity is weaker, e.g., later in the day, matching tentative pressure-inferred diameters from the InSight lander.
  • The -5/3 slope should hold for terrestrial dust devils as well, since the derivation is planet-agnostic and depends only on boundary-layer shear and heat flux.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Editorial inference: the same close-packing logic could be applied to other convective vortices and plumes (e.g., waterspouts, fire whirls) whose influence areas exceed their visible cores, predicting the same -5/3 slope for their size distributions.
  • Editorial inference: if friction $f$ scales with intake velocity as the paper itself warns, the exponent should shift from -5/3 toward -2; a precise measurement of the exponent across environments with different surface roughness could detect this and turn the power-law slope into a roughness proxy.
  • Editorial inference: because $D_\infty$ is unobservable directly by orbiting cameras, the model also predicts nearest-neighbor dust devil spacings should be governed by $D_\infty$ packing, so high-resolution orbital images of devil pairs could test close-packing without ground truth.
  • Editorial inference: the $D^{-5/3}$ law plus a measured dust devil diameter distribution from a future landed or orbital survey would allow a direct, population-weighted dust injection budget for Mars's global climate models.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 4 minor

Summary. The paper proposes that dust devil diameters follow a power-law differential distribution dN/dD proportional to D^-5/3, and it attempts to justify this form from a close-packing hypothesis in which dust devils pack according to their 'diameter of influence' D_infinity rather than their visible diameter D. The authors combine an assumed D_infinity^-2 distribution with a scaling D_infinity proportional to D^(2/3) to obtain the D^-5/3 law, compare with histogrammed Mars datasets under several binning rules, and further derive a scaling N0 proportional to eta*Fs between areal dust-devil density and the dust-devil activity index. The paper also reports a new field measurement of one dust devil crossing a weather-station network, used to estimate D/D_infinity.

Significance. If the D^-5/3 distribution is robust, the result is practically important: it makes a quantitative statement about how strongly small dust devils dominate population dust injection, and it connects areal occurrence rates to the thermodynamic activity index in a falsifiable way. The paper is commendably transparent about its simplifying assumptions, it makes concrete predictions (e.g., the diurnal diameter trend), and it compares data under multiple histogramming schemes rather than relying on a single binning choice. The main limitations are that the central derivation rests on two mutually inconsistent scalings in Section 2, that the key D_infinity^-2 input is asserted rather than derived, and that the observational fits depend on fitting choices that are not fully reported. These are load-bearing issues for the core claim, so the paper needs substantial revision before the proposed exponent and N0 scaling can be considered established.

major comments (4)
  1. [2, Eqs. (9) and (16)] Equations (9) and (16) give incompatible scalings for the influence diameter. Equation (9), obtained from mass conservation and the streamline condition (5)-(8), implies D_infinity proportional to D when alpha, H, and w are fixed. Equation (16), obtained from the work balance (12)-(15) and the angular-momentum relation (2), implies D_infinity proportional to D^(2/3). These cannot both hold for one population with constant ambient parameters: over D = 10-1000 m the two forms differ by a factor of more than four in D_infinity/D. Since Equation (17) and hence the predicted D^-5/3 exponent use only Equation (16), while Equation (9) is presented earlier as a model consequence and checked against one field event, the paper currently has two mutually contradictory derivations of the same quantity. Please reconcile the two scalings, for example by making H, w, or f depend explicitly on D or D_infinity, or state clearly that Equation (9) is not part of the derivation of the diameter distribution.
  2. [3.1, Figure 3 and surrounding text] The observational support for D^-5/3 is weaker than the abstract suggests because the fits are made only after excluding all diameters below about 50 m and because the manuscript reports that unweighted fits give gamma <= -2, with weighted fits applied only after that finding. The text says 'if we do not include these uncertainties, the best-fit power-law indices are gamma <= -2, inconsistent with our expectations'; this makes the reported agreement with -5/3 depend on the weighting scheme and on the adopted lower cutoff. Please report the fit indices for all binning schemes both with and without weights and as a function of the diameter cutoff, so readers can see how much of the -5/3 result is built into the analysis choices.
  3. [2, Eq. (11) and Eq. (20)] The central input dN/dD_infinity proportional to D_infinity^-2 is assumed, not derived. The paper justifies it by noting that the observed visual-diameter distribution is close to D^-2 (Lorenz 2011), but that empirical scaling is for visible diameters D, while the assumption is transplanting it to the unobservable influence diameter D_infinity. Since Equation (17) follows directly from this assumption together with Equation (16), the derivation is in an important sense circular if the D_infinity^-2 form is borrowed from the very visual distribution the paper aims to explain. Please derive the D_infinity^-2 form from the close-packing idea, or test it against an independent statistic (e.g., nearest-neighbor spacings in D_infinity) rather than citing the observed D^-2 law.
  4. [4, Eqs. (19)-(26)] The derivation of N0 proportional to eta*Fs needs clarification. Equation (19) applies to the fractional area occupied by all convective motions, not specifically to dust devils, yet Equation (24) equates the geometric ratio (D/D_infinity)^2 with the same (eta*Fs)^-1/2 scaling and then uses this per-devil relation for both Dmin_infinity and Dmax_infinity in Equation (25). As written, only Dmax_infinity is argued to scale with (eta*Fs)^-1/2 through Equation (24), and the step from Equation (25) to Equation (26) appears to require that both integration limits scale in that way. Please state explicitly what is being assumed about the cutoffs, or provide a derivation that does not depend on unstated scaling of Dmin_infinity.
minor comments (4)
  1. [3.1, Figure 3] The text says 'the histogram for the Scott rule (red triangles)', but in the figure legend Scott's rule is shown as green stars and Bayesian Blocks as red triangles; the labeling should be corrected.
  2. [2] The word 'orthongonal' appears in the field-work description and should be 'orthogonal'.
  3. [3.2] The statement that the Stanzel et al. power-law indices agree 'to within 2 sigma' with Figure 3 is not accompanied by the fitted ranges or uncertainties; please report the numerical values and the diameter ranges used for each fit.
  4. [3.1] The caption to Figure 3 says the Scott-rule points are shown as green stars, but the text elsewhere refers to 'the Scott rule (red triangles)'; this inconsistency should be resolved throughout.

Circularity Check

3 steps flagged · score 5.0 of 10

Predicted D^-5/3 is a coordinate-rescaled restatement of the assumed D∞^-2 input, supported by load-bearing self-citations and an unresolved internal scaling contradiction.

  1. renaming known result [Section 2, between Eq. (10) and Eq. (11); close-packing paragraph and Eq. (11)]
    "Assuming the histogram of dust devils in D∞ is given by dN/dD∞ ∝ D−2 ∞, we can work out the histogram in D as dN dD = ( dN dD∞ )( dD∞ dD ). ... This dependence on D∞ seems to closely resemble the observed distribution of visual dust devil diameters reported in the literature."

    The load-bearing input to Eq. (17) is the assumed differential distribution dN/dD∞ ∝ D∞^-2, stated immediately before Eq. (11). This is not a derived consequence of close packing; the paper explicitly connects it to the already-known visual-diameter power law, noting Lorenz (2011) settled on a power-law index of -2, 'suspiciously close to the closest-packing hypothesis proposed here.' The only difference is the coordinate: the empirical -2 slope for visual D is relabeled as a slope for unobserved influence diameters D∞. Eq. (16) then supplies dD∞/dD ∝ D^-1/3, so Eq. (17) returns D^-5/3.

  2. self citation load bearing [Section 2, Eq. (1); assumptions revisited in Conclusions]
    "B. Jackson (2020) argued that the specific angular momentum (angular momentum per unit mass) l within the eyewall of a dust devil can be derived from the shear in lateral wind speed in the ambient wind field. Thus, l ∼ υθ (D/2) ∼ α (D∞/2)2, (1)"

    Equation (1) is the starting point of the central derivation: it yields Eq. (2), is used with Eq. (15) to obtain Eq. (16) (D∞ ∝ D^(2/3)), and hence fixes the exponent in Eq. (17). It is justified only by citing B. Jackson (2020), a prior paper by the lead author. The field-data checks in Eqs. (4) and (10) are consistency estimates using the same model, not independent tests of the shear-origin assumption. The Conclusions concede that 'confirmation of the source of dust devil vorticity remains unclear.' A load-bearing premise of the predicted slope therefore rests on a self-citation whose physical content is acknowledged to be unconfirmed.

1 more flagged steps
  1. ansatz smuggled in via citation [Section 2, close-packing paragraph preceding Eq. (11)]
    "In that case, we might expect the areal density of dust devils (number per unit area) to depend on D∞, and not on D, as D−2∞ (L. K. Fenton & R. Lorenz 2015)."

    The D∞^-2 distribution that drives Eq. (17) is introduced as 'we might expect' and attributed to L. K. Fenton & R. Lorenz (2015), two co-authors. Yet the Discussion later says that study 'only measured the heights of the dust devils and the relative spacings and not their diameters,' so it cannot independently establish a differential distribution in influence diameter. The citation supplies authority for an ansatz that is load-bearing for the central D^-5/3 result; since the cited study did not measure the relevant quantity, the support is a co-authored citation rather than an independent derivation.

full rationale

The paper is not circular in the strictest sense: the -5/3 exponent is not directly fit to the data, and the comparison to the Conway, Stanzel, and Greeley histograms is external. However, the central derivation is partially circular. The key step Eq. (17) is nothing but the assumed input dN/dD∞ ∝ D∞^-2 multiplied by the Jacobian dD∞/dD obtained from Eq. (16). That input is the empirically known D^-2 slope from Lorenz (2011), re-expressed in terms of an unobserved influence diameter; the predicted D^-5/3 is therefore a coordinate-rescaled version of an empirical result already in the literature, not a prediction from first principles. The transformation exponent itself is also not secure: Eq. (9) gives D∞ ∝ D, while Eq. (16) gives D∞ ∝ D^(2/3), and the paper never reconciles these two scalings with constant ambient parameters. That inconsistency is a correctness risk rather than a circular step, but it further weakens the claim that -5/3 is forced. Two load-bearing self-citations amplify the concern: the shear-origin of angular momentum is taken from Jackson (2020), and the D∞^-2 ansatz is attributed to Fenton & Lorenz (2015) even though the Discussion says that paper did not measure diameters. The areal-density scaling N0 ∝ ηFs (Eq. 26) is a separate chain based on Rennó & Ingersoll (1996) and is less implicated in this circularity. On balance, the central claim has partial independent content—the specific exponent -5/3 is not identical to -2—so a moderate partial-circularity score is appropriate.

Assumptions & free parameters 4 free parameters · 6 assumptions · 1 invented entities

The central -5/3 exponent is parameter-free given the assumed scaling forms, but the derivation rests on several domain assumptions: close packing in D_infinity, vorticity from ambient shear (Jackson 2020), friction and cyclostrophic balance (Renno 1998), and the Renno-Ingersoll convective area relation. The N0 proportional to eta F_s result additionally assumes dust devil convective area scales with total convective area, an asserted proportionality. Numeric inputs (alpha, H, f, diameter limits) affect D_infinity estimates but not the power-law index.

free parameters (4)
  • Lateral wind shear alpha = 0.01 s^-1 (field estimate, single event)
    Estimated from two stations in the Alvord Desert network; used to compute D_infinity approximately 190 m and to check D/D_infinity scaling, not needed for the -5/3 exponent itself.
  • Inflow height H = approximately 1 m
    Chosen from a Monin-Obukhov length argument and used in the mass conservation check (Eq 10); uncertain by a factor of a few.
  • Frictional acceleration f = not directly measured; order 1 m s^-2 implied by v_theta and D_infinity
    Appears in Eq 16 and in the D_infinity-D scaling; treated as a population-averaged constant independent of D.
  • Diameter range limits D_min and D_max = 10 m and about 1 km (observed)
    Used to normalize the D_infinity distribution and evaluate the fractional area integral; the scaling result is insensitive to the exact values.
assumptions (6)
  • domain assumption dN/dD_infinity proportional to D_infinity^-2 close packing of influence areas
    Assumed in Section 2 after Eq 16; motivated by analogy to observed visual D^-2 but not derived from packing geometry alone.
  • domain assumption Specific angular momentum l approximately alpha (D_infinity/2)^2
    Eq 1 from Jackson 2020; assumes vorticity is sourced by ambient wind shear.
  • domain assumption Friction work balance Delta P_c / rho approximately f D_infinity/2 and cyclostrophic v_theta = sqrt(Delta P_c / rho)
    Eqs 12-15 from Renno et al. 1998; used to eliminate v_theta and derive Eq 16.
  • domain assumption Fractional convective area scaling sigma proportional to (eta F_s)^(-1/2) applies dust devil by dust devil
    Eq 24: (D/D_infinity)^2 proportional to (eta F_s)^(-1/2), taken from Renno and Ingersoll 1996; this injects eta F_s into the derivation of N0 proportional to eta F_s.
  • ad hoc to paper Dust devil convective area fraction scales with total convective area fraction
    Section 4 before Eq 26: 'Since the population of dust devils supplies at least some of that fractional coverage, we expect...' This proportionality is asserted, not derived.
  • domain assumption Observed decline of small devils is detection bias, not a real population decline
    Section 3.1: fits exclude diameters below approximately 50 m for the Conway data; if the decline is real, the fitted slope is biased.
invented entities (1)
  • Diameter of influence D_infinity independent evidence
    purpose: Latent scale for close packing and for transforming D_infinity^-2 into D^-5/3
    Inferred from one field encounter (D_infinity approximately 190 m) and from Eq 2; not directly observed. The close-packing claim relies on this unobservable quantity.

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Pith. "Pith review of On Dust Devil Diameters, Occurrence Rates, and Activity." pith.science (2026). https://pith.science/paper/ZOFYVCBL

@misc{pith2026250703643,
  author       = {Pith},
  title        = {Pith review of: On Dust Devil Diameters, Occurrence Rates, and Activity},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ZOFYVCBL}},
  note         = {Machine review of arXiv:2507.03643}
}
abstract

As a phenomenon that occurs on Earth and on Mars, the diameter of a dust devil helps determine the amount of dust the devil injects into the atmosphere for both worlds -- for a given dust flux density (dust lifted per area per time), a wider devil will lift more dust into the air. However, the factors that determine a dust devil's diameter $D$ and how it might relate to ambient conditions have remained unclear. Moreover, estimating the contribution to an atmospheric dust budget from a population of dust devils with a range of diameters requires an accurate assessment of the differential diameter distribution, but considerable work has yet to reveal the best representation or explain its physical basis. In this study, we propose that this distribution follows a power-law $\propto D^{-5/3}$ and provide a simple physical explanation for why the distribution takes this form. By fitting diameter distributions of martian dust devil diameters reported in several studies, we show that the data from several studies support this proposed form. Using a previous model that treats dust devils as thermodynamic heat engines, we also show that the areal density of dust devils (number per unit area) $N_0$ scales with the product of their thermodynamic efficiency $\eta$ and the sensible heat flux $F_{\rm s}$ as $N_0 \propto \eta F_{\rm s}$.

Figures

Figures reproduced from arXiv: 2507.03643 by the authors.

Figure 1
Figure 1. (a) Approximate locations of weather stations in the field. The stations were deployed in a regular grid, but the positions shown exhibit the GPS uncertainties of a few meters. The x axis shows the location along lines of longitude and the y axis along lines of latitude, both in microdegrees (µ ◦ ). Stations that registered the dust devil encounter considered here are shown in orange circles, and the ambient wind ve… view at source ↗
Figure 2
Figure 2. Schematic of dust devil inflow. The innermost circle with radius D/2 represents the convective cell at the center of the dust devil. The outermost circle with radius D∞/2 represents the diameter of influence. The component of the wind perpendicular to the dust devil is given by U ≈ α (D∞/2) where α is the lateral wind shear, and the radial component of the wind is given by υin. An example streamline is also shown. P… view at source ↗
Figure 3
Figure 3. Histogram of dust devil diameters as reported in S. J. Conway et al. (2025). The different color and marker-style points represent histograms created using various binning algorithms as indicated in the legend text and described in the narrative. The horizontal bars on each marker show the width of each bin, and the vertical bars show the Poisson uncertainties. The best-fit power-law for each histogram is shown in t… view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: Histogram of dust devil diameters as reported in C. Stanzel et al. (2008). The symbols and legend all are defined in the same ways as in [PITH_FULL_IMAGE:figures/full_fig_p009_4.png]
Figure 5
Figure 5. Figure 5: The histogram of dust devil diameters reported in R. Greeley et al. (2006) and R. Greeley et al. (2010). Unlike Figures 3 and 4, we do not have the diameters directly, only a histogram of values. Thus, the binning shown here does not follow any of the schemes used in t…

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Works this paper leans on

50 extracted references · 37 canonical work pages

  1. [1]

    Arya, S. P. 2001, Introduction to Micrometeorology Astropy Collaboration, Robitaille, T. P., Tollerud, E. J., et al. 2013, A&A, 558, A33, doi: 10.1051/0004-6361/201322068 Astropy Collaboration, Price-Whelan, A. M., Sip˝ ocz, B. M., et al. 2018, AJ, 156, 123, doi: 10.3847/1538-3881/aabc4f

    doi:10.1051/0004-6361/201322068 2001

  2. [2]

    R., Pathare, A., Metzger, S

    Balme, M. R., Pathare, A., Metzger, S. M., et al. 2012, Icarus, 221, 632, doi: 10.1016/j.icarus.2012.08.021

    work page doi:10.1016/j.icarus.2012.08.021 2012

  3. [3]

    J., Bickel, V

    Conway, S. J., Bickel, V. T., Fenton, L. K., et al. 2025, Planet. Space Sci., 259, 106072, doi: 10.1016/j.pss.2025.106072

    work page arXiv 2025

  4. [4]

    D., Gunnlaugsson, H

    Ellehoj, M. D., Gunnlaugsson, H. P., Taylor, P. A., et al. 2010, Journal of Geophysical Research (Planets), 115, E00E16, doi: 10.1029/2009JE003413

    work page doi:10.1029/2009je003413 2010

  5. [5]

    D., & Babu, G

    Feigelson, E. D., & Babu, G. J. 2012, Modern Statistical Methods for Astronomy, doi: 10.48550/arXiv.1205.2064

    arXiv doi:10.48550/arxiv.1205.2064 2012

  6. [6]

    2016, SSRv, 203, 89, doi: 10.1007/s11214-016-0243-6

    Fenton, L., Reiss, D., Lemmon, M., et al. 2016, SSRv, 203, 89, doi: 10.1007/s11214-016-0243-6

    work page doi:10.1007/s11214-016-0243-6 2016

  7. [7]

    K., & Lorenz, R

    Fenton, L. K., & Lorenz, R. 2015, Icarus, 260, 246, doi: 10.1016/j.icarus.2015.07.028

    work page doi:10.1016/j.icarus.2015.07.028 2015

  8. [8]

    K., Metzger, S

    Fenton, L. K., Metzger, S. M., Michaels, T. I., et al. 2022, Aeolian Research, 59, 100831, doi: 10.1016/j.aeolia.2022.100831

    arXiv 2022

Show all 50 references
  1. [9]

    1981, Zeitschrift f¨ ur Wahrscheinlichkeitstheorie und Verwandte Gebiete, 57, 453, doi: 10.1007/BF01025868

    Freedman, D., & Diaconis, P. 1981, Zeitschrift f¨ ur Wahrscheinlichkeitstheorie und Verwandte Gebiete, 57, 453, doi: 10.1007/BF01025868

    1981 doi

  2. [10]

    2023, in LPI Contributions, Vol

    Golombek, M., Charalambous, C., Newman, C., et al. 2023, in LPI Contributions, Vol. 2806, 54th Lunar and Planetary Science Conference, 2467

    2023

  3. [11]

    L., Arvidson, R

    Greeley, R., Whelley, P. L., Arvidson, R. E., et al. 2006, Journal of Geophysical Research (Planets), 111, E12S09, doi: 10.1029/2006JE002743

    2006 doi

  4. [12]

    A., Cabrol, N

    Greeley, R., Waller, D. A., Cabrol, N. A., et al. 2010, Journal of Geophysical Research (Planets), 115, E00F02, doi: 10.1029/2010JE003608

    2010 doi

  5. [13]

    R., Millman, K

    Harris, C. R., Millman, K. J., van der Walt, S. J., et al. 2020, Nature, 585, 357, doi: 10.1038/s41586-020-2649-2

    2020 doi

  6. [14]

    D., & Spillane, K

    Hess, G. D., & Spillane, K. T. 1990, Journal of Applied Meteorology, 29, 498, doi: 10.1175/1520- 0450(1990)029⟨0498:CODDIA⟩2.0.CO;2

    1990 doi

  7. [15]

    Hunter, J. D. 2007, Computing In Science & Engineering, 9, 90

    2007

  8. [16]

    2020, Icarus, 338, 113523, doi: 10.1016/j.icarus.2019.113523

    Jackson, B. 2020, Icarus, 338, 113523, doi: 10.1016/j.icarus.2019.113523

    2020

  9. [17]

    2021, PSJ, 2, 206, doi: 10.3847/PSJ/ac260d

    Jackson, B., Crevier, J., Szurgot, M., et al. 2021, PSJ, 2, 206, doi: 10.3847/PSJ/ac260d

    2021 doi

  10. [18]

    2018, Remote Sensing, 10, 65, doi: 10.3390/rs10010065

    Jackson, B., Lorenz, R., Davis, K., & Lipple, B. 2018, Remote Sensing, 10, 65, doi: 10.3390/rs10010065

    2018 doi

  11. [19]

    2025, in LPI

    Jackson, B., Szurgot, C., Fenton, L., et al. 2025, in LPI

    2025

  12. [20]

    A., Murphy, J

    Kahre, M. A., Murphy, J. R., Newman, C. E., et al. 2017, in Asteroids, Comets, Meteors - ACM2017, ed. R. M

    2017

  13. [21]

    Haberle, R. T. Clancy, F. Forget, M. D. Smith, & R. W. Zurek, 229–294, doi: 10.1017/9781139060172.010

    doi

  14. [22]

    Kanak, K. M. 2005, Quarterly Journal of the Royal Meteorological Society, 131, 1271, doi: 10.1256/qj.03.172 Kleinb¨ ohl, A., Willacy, K., Slipski, M. J., et al. 2024, Nature Astronomy, 8, 827, doi: 10.1038/s41550-024-02268-x

    2005 doi

  15. [23]

    Knuth, K. H. 2006, arXiv e-prints, physics/0605197, doi: 10.48550/arXiv.physics/0605197

    2006 doi arXiv

  16. [24]

    Kurgansky, M. V. 2006, Geophys. Res. Lett., 33, L19S06, doi: 10.1029/2006GL026142

    2006 doi

  17. [25]

    Kurgansky, M. V. 2012, Icarus, 219, 556, doi: 10.1016/j.icarus.2012.04.006

    2012 doi

  18. [26]

    Kurgansky, M. V. 2018, Icarus, 300, 97, doi: 10.1016/j.icarus.2017.08.029

    2018 doi

  19. [27]

    Kurgansky, M. V. 2019, Icarus, 317, 209, doi: 10.1016/j.icarus.2018.08.004

    2019 doi

  20. [28]

    V., Lorenz, R

    Kurgansky, M. V., Lorenz, R. D., Renno, N. O., et al. 2016, SSRv, 203, 209, doi: 10.1007/s11214-016-0281-0

    2016 doi

  21. [29]

    2011, Icarus, 215, 381, doi: 10.1016/j.icarus.2011.06.005

    Lorenz, R. 2011, Icarus, 215, 381, doi: 10.1016/j.icarus.2011.06.005

    2011 doi

  22. [30]

    2013, Icarus, 226, 964, doi: 10.1016/j.icarus.2013.06.031

    Lorenz, R. 2013, Icarus, 226, 964, doi: 10.1016/j.icarus.2013.06.031

    2013 doi

  23. [31]

    Lorenz, R. D. 2009, Icarus, 203, 683, doi: 10.1016/j.icarus.2009.06.029

    2009 doi

  24. [32]

    Lorenz, R. D. 2012, Geoscientific Instrumentation, Methods and Data Systems, 1, 209, doi: 10.5194/gi-1-209-201210.5194/gid-2-477-2012 15

    2012 arXiv

  25. [33]

    Lorenz, R. D. 2016, Icarus, 271, 326, doi: 10.1016/j.icarus.2016.02.001

    2016 doi

  26. [34]

    Lorenz, R. D. 2021, Advances in Space Research, 67, 2219, doi: 10.1016/j.asr.2021.01.023

    2021 doi

  27. [35]

    D., & Jackson, B

    Lorenz, R. D., & Jackson, B. K. 2016, SSRv, 203, 277, doi: 10.1007/s11214-016-0277-9

    2016 doi

  28. [36]

    Neakrase, L. D. V., & Greeley, R. 2010, Icarus, 206, 306, doi: 10.1016/j.icarus.2009.08.028

    2010 doi

  29. [37]

    E., Kahanp¨ a¨ a, H., Richardson, M

    Newman, C. E., Kahanp¨ a¨ a, H., Richardson, M. I., et al. 2019, Journal of Geophysical Research (Planets), 124, 3442, doi: 10.1029/2019JE006082

    2019 doi

  30. [38]

    E., & Richardson, M

    Newman, C. E., & Richardson, M. I. 2015, Icarus, 257, 47, doi: 10.1016/j.icarus.2015.03.030

    2015 doi

  31. [39]

    E., Bertrand, T., Fenton, L

    Newman, C. E., Bertrand, T., Fenton, L. K., et al. 2022, in Treatise on Geomorphology (Second Edition), second edition edn., ed. J. J. F. Shroder (Oxford: Academic Press), 637–666, doi: https://doi.org/10.1016/B978-0-12-818234-5.00143-7

    2022 doi

  32. [40]

    V., Balme, M

    Pathare, A. V., Balme, M. R., Metzger, S. M., et al. 2010, Icarus, 209, 851, doi: 10.1016/j.icarus.2010.06.027

    2010 doi

  33. [41]

    2016, SSRv, 203, 183, doi: 10.1007/s11214-016-0307-7 Renn´ o, N

    Rafkin, S., Jemmett-Smith, B., Fenton, L., et al. 2016, SSRv, 203, 183, doi: 10.1007/s11214-016-0307-7 Renn´ o, N. O., Burkett, M. L., & Larkin, M. P. 1998, Journal of the Atmospheric Sciences, 55, 3244, doi: 10.1175/1520-0469(1998)055 ⟨3244: ASTTFD⟩2.0.CO;2 Renn´ o, N. O., & ...

    1998 doi

  34. [42]

    I., Toigo, A

    Richardson, M. I., Toigo, A. D., & Newman, C. E. 2007, Journal of Geophysical Research (Planets), 112, E09001, doi: 10.1029/2006JE002825

    2007 doi

  35. [43]

    A., & Carroll, J

    Ryan, J. A., & Carroll, J. J. 1970, J. Geophys. Res., 75, 531, doi: 10.1029/JC075i003p00531

    1970 doi

  36. [44]

    D., Norris, J

    Scargle, J. D., Norris, J. P., Jackson, B., & Chiang, J. 2013, ApJ, 764, 167, doi: 10.1088/0004-637X/764/2/167

    2013 doi

  37. [45]

    Scott, D. W. 1979, Biometrika, 66, 605, doi: 10.1093/biomet/66.3.605

    1979 doi

  38. [46]

    Sinclair, P. C. 1966, PhD thesis, University of Arizona

    1966

  39. [47]

    A., et al

    Stanzel, C., P¨ atzold, M., Williams, D. A., et al. 2008, Icarus, 197, 39, doi: 10.1016/j.icarus.2008.04.017

    2008 doi

  40. [48]

    D., Lee, C., Newman, C

    Toigo, A. D., Lee, C., Newman, C. E., & Richardson, M. I. 2012, Icarus, 221, 276, doi: 10.1016/j.icarus.2012.07.020

    2012 doi

  41. [49]

    E., et al

    Virtanen, P., Gommers, R., Oliphant, T. E., et al. 2020, Nature Methods, 17, 261, doi: 10.1038/s41592-019-0686-2

    2020 doi

  42. [50]

    E., & Deardorff, J

    Willis, G. E., & Deardorff, J. W. 1979, J. Geophys. Res., 84, 295, doi: 10.1029/JC084iC01p00295

    1979 doi

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