arxiv: 2604.10668 · v1 · submitted 2026-04-12 · ⚛️ physics.ao-ph

Recognition: unknown

Understanding Left-Moving Supercells: Environmental Factors and Forecasting Challenges

Aaron W. Zeeb , John T. Allen , Matthew Van Den Broeke

Authors on Pith no claims yet

Pith reviewed 2026-05-10 15:41 UTC · model grok-4.3

classification ⚛️ physics.ao-ph

keywords left-moving supercellssupercell environmentshail forecastinghodograph analysisproximity soundingsCAPElapse ratesLCL height

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The pith

Left-moving supercells form in right-mover environments but only realize the hodograph above their LCL, with lapse rates, CAPE, and LCL height as best predictors of strength and hail.

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

The paper analyzes a dataset of more than 850 left-moving supercell cases across North America to characterize their near-storm environments using proximity soundings. It establishes that these storms typically develop under conditions that also support right-moving supercells, but left-movers appear to interact primarily with the wind profile above the lifting condensation level. Lapse rates, convective available potential energy, and LCL height emerge as the strongest indicators of storm intensity and large hail production. The work supplies forecasters with a distinct parameter space for anticipating LM behavior and associated hazards.

Core claim

Left-moving supercells typically form in environments supportive of right movers, with the key difference being that LMs likely only realize the shape of the hodograph above their LCLs. Lapse rates, CAPE, and LCL height are the best predictors of LM strength and hail potential. LMs with wind reports show drier boundary layer moisture, steeper 0-3 km lapse rates, larger CAPE, and higher LCL heights. Longer-lived LMs often have weaker CAPE and stronger shear compared to shorter-lived ones.

What carries the argument

Manually compiled dataset of over 850 quality-controlled LM supercell cases, with near-storm environments characterized by RAP/RUC inflow proximity soundings and compared to storm properties including mesoanticyclone strength, hail size, wind speed, and duration.

If this is right

Environments can be used to differentiate LM supercells of varying strengths and hazard categories.
LMs with wind reports occur in conditions favoring greater evaporational cooling through drier boundary layers and steeper low-level lapse rates.
Longer-lived LMs tend to occur where CAPE is weaker but deep-layer shear is stronger.
The results provide a climatology of LM parameter spaces that can directly inform operational forecasting decisions.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Forecast models could improve by weighting wind profiles differently below and above the LCL when initializing LM potential.
The distinction in hodograph usage may explain why LM storms remain less common and more difficult to anticipate than their right-moving counterparts.
Similar analysis applied to southern-hemisphere anticyclonic supercells could test whether the LCL-relative pattern reverses with hemisphere.

Load-bearing premise

The manually compiled dataset of over 850 cases is representative without significant selection bias, and RAP/RUC proximity soundings accurately capture the true inflow environment for each storm.

What would settle it

An independent set of verified LM supercells in which lapse rates, CAPE, and LCL height show no correlation with observed hail size or mesoanticyclone strength would falsify the central predictors.

read the original abstract

Left-moving (LM) supercells, characterized by anticyclonically rotating updrafts in the Northern Hemisphere, are significant due to their propensity to produce large hail. Although less common than right-moving supercells, they present notable forecasting challenges and societal impacts. However, despite these impacts, the environments of LM supercells are poorly understood compared to their right-moving counterparts. To address this gap, this research focuses on enhancing the understanding of LM supercells by examining the environmental conditions conducive to their development. A manually compiled and quality-controlled dataset of over 850 LM supercell cases across North America is used to provide a robust sample. Near-storm environments are characterized through the use of RAP/RUC inflow proximity sounding profiles. Leveraging storm properties, including mesoanticyclone strength, hail size, wind speed, and duration, we investigate whether environments can differentiate between these varying strengths and categories, thereby enhancing forecaster awareness. Results show that LMs typically form in environments supportive of right movers, with a key difference being that LMs likely only realize the shape of the hodograph above their LCLs. Lapse rates, CAPE, and LCL height are the best predictors of LM strength and hail potential. LMs with wind reports have drier boundary layer moisture, steeper 0--3 km lapse rates, larger CAPE, and higher LCL heights, leading to increased evaporational cooling. Longer-lived LMs often have weaker CAPE and stronger shear as compared to shorter-lived LMs. These results establish a unique parameter space climatology of LM supercells, thus providing essential forecasting insight and reducing the research gap for these storms.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The main value is the first large climatology of left-moving supercell environments from 850+ cases, with practical predictors for strength and hail, though sounding resolution and case selection need checking.

read the letter

The main takeaway is that left-moving supercells mostly sit in environments that also support right-movers, but they seem to only use the hodograph shape above the LCL, and lapse rates, CAPE, and LCL height are the strongest signals for their intensity and hail potential. The paper backs this with a manually quality-controlled set of over 850 cases and proximity soundings from RAP/RUC profiles. What the work does well is close a real gap. Right-mover environments have been mapped in detail for years, but left-movers have lacked this kind of scale. Tying the environmental variables to storm properties like mesoanticyclone strength, hail size, and lifetime gives some practical distinctions that forecasters can use. The note that wind-reporting LMs have drier boundary layers and steeper lapse rates, leading to more evaporational cooling, is a useful observation. The soft spots are the usual ones for this kind of study. Manual case collection from reports and radar can bias toward the stronger or longer-lived storms, which might exaggerate the links to high CAPE and steep 0-3 km lapse rates. RAP and RUC soundings are handy but their coarse grids often miss the fine boundary-layer details that matter for the low-level hodograph. The abstract does not lay out the exact statistical tests or how they dealt with sounding quality issues, so the predictor rankings need more backing. This paper is for people working on supercell forecasting or climatologies in severe storms. It supplies a new parameter space that others can build on or test against. I would send it to peer review. The dataset size and the topic make it worth the referees' time to sort out the methods and any selection effects.

Referee Report

3 major / 2 minor

Summary. The manuscript analyzes a manually compiled and quality-controlled dataset of over 850 left-moving (LM) supercell cases across North America, using RAP/RUC inflow proximity soundings to characterize near-storm environments. It concludes that LM supercells typically form in environments supportive of right-moving supercells, with the key distinction that they realize the hodograph shape primarily above their LCLs; lapse rates, CAPE, and LCL height emerge as the strongest predictors of LM strength and hail potential, while additional differences appear for wind-producing and longer-lived cases.

Significance. If the central empirical relationships hold, the work supplies a valuable observational climatology for an understudied class of supercells that frequently produce large hail. The sizable manually assembled dataset constitutes a concrete strength for an observational study in this domain and could directly inform forecaster guidance on environmental parameters that discriminate LM intensity.

major comments (3)

[Methods] Methods section on dataset compilation: explicit inclusion/exclusion criteria and quality-control rules for the >850 manually identified cases are not provided. Without these details, it is impossible to assess whether the sample over-represents intense or hail-producing storms, which would directly affect the reported correlations between CAPE, 0–3 km lapse rate, and LCL height and the storm-property categories.
[Results / Discussion] Proximity sounding discussion (near Results): the assumption that RAP/RUC profiles faithfully capture the inflow hodograph segment above the LCL is load-bearing for the headline claim that “LMs likely only realize the shape of the hodograph above their LCLs.” No quantitative assessment of boundary-layer moisture or low-level shear errors at 13–20 km grid spacing is supplied.
[Results] Results on predictors: the statement that lapse rates, CAPE, and LCL height are the “best predictors” of LM strength and hail potential lacks any reported correlation coefficients, regression diagnostics, or significance tests. This omission prevents evaluation of whether the claimed ranking is statistically supported or merely descriptive.

minor comments (2)

[Abstract / Introduction] Abstract and §1: the phrasing “LMs likely only realize…” mixes observational inference with mechanistic language; reword to clarify that the hodograph difference is an empirical pattern rather than a demonstrated dynamical process.
[Figures] Figure captions: ensure every panel in the environmental-comparison figures explicitly labels the plotted variable, units, and sample size for each category.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their insightful and constructive comments on our manuscript. We address each of the major comments point by point below, and we will revise the manuscript accordingly to improve its clarity and scientific rigor.

read point-by-point responses

Referee: [Methods] Methods section on dataset compilation: explicit inclusion/exclusion criteria and quality-control rules for the >850 manually identified cases are not provided. Without these details, it is impossible to assess whether the sample over-represents intense or hail-producing storms, which would directly affect the reported correlations between CAPE, 0–3 km lapse rate, and LCL height and the storm-property categories.

Authors: We agree with the referee that explicit inclusion/exclusion criteria and quality-control procedures are necessary to evaluate potential sampling biases. In the revised version of the manuscript, we will add a new subsection in the Methods detailing the criteria for case selection (e.g., confirmation of left-moving motion via radar, anticyclonic mesocyclone signatures, and temporal/spatial proximity to RAP/RUC soundings) and the quality-control steps performed, including independent verification by multiple researchers and exclusion of marginal or ambiguous cases. This addition will allow assessment of whether the sample over-represents intense or hail-producing storms. revision: yes
Referee: [Results / Discussion] Proximity sounding discussion (near Results): the assumption that RAP/RUC profiles faithfully capture the inflow hodograph segment above the LCL is load-bearing for the headline claim that “LMs likely only realize the shape of the hodograph above their LCLs.” No quantitative assessment of boundary-layer moisture or low-level shear errors at 13–20 km grid spacing is supplied.

Authors: This is a valid concern, as the accuracy of the model soundings underpins our interpretation of hodograph realization above the LCL. Although the use of RAP/RUC proximity soundings follows standard methodology in supercell climatology studies, we recognize the need for more explicit discussion of model limitations. In the revision, we will expand the relevant section to include discussion of known RAP/RUC biases in boundary-layer moisture and low-level winds at 13-20 km grid spacing, drawing on existing literature, and clarify how these considerations affect our conclusions. We will also emphasize that our key findings rely on consistent relative differences across the large dataset. revision: yes
Referee: [Results] Results on predictors: the statement that lapse rates, CAPE, and LCL height are the “best predictors” of LM strength and hail potential lacks any reported correlation coefficients, regression diagnostics, or significance tests. This omission prevents evaluation of whether the claimed ranking is statistically supported or merely descriptive.

Authors: We appreciate this observation. While the identification of lapse rates, CAPE, and LCL height as the strongest predictors was informed by systematic comparisons of parameter distributions across storm categories (e.g., via boxplots and mean differences), we agree that including formal statistical metrics would enhance the rigor. In the revised manuscript, we will report Pearson correlation coefficients and associated p-values between these environmental parameters and storm intensity metrics such as mesoanticyclone strength and hail size. This will provide a statistical basis for the predictor ranking. revision: yes

Circularity Check

0 steps flagged

No circularity: purely empirical observational analysis with no derivations or self-referential reductions

full rationale

The paper compiles a dataset of over 850 LM supercell cases and uses RAP/RUC proximity soundings to compare environmental variables (lapse rates, CAPE, LCL height, shear) against observed storm properties (mesoanticyclone strength, hail size, duration). No equations, parameters, or predictions are derived; results are direct statistical comparisons of observed quantities. No self-citations support load-bearing uniqueness theorems, ansatzes, or fitted inputs renamed as predictions. The central claims rest on empirical patterns in the data rather than any construction that reduces to its own inputs. This is self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

Observational climatology with no explicit free parameters, invented entities, or ad-hoc axioms stated in the abstract; relies on standard domain assumptions about sounding accuracy and case representativeness.

axioms (2)

domain assumption RAP/RUC model soundings provide a sufficiently accurate representation of near-storm inflow conditions
Used to characterize environments for all cases.
domain assumption Manually compiled cases form an unbiased sample of left-moving supercells
Basis for the climatology and strength comparisons.

pith-pipeline@v0.9.0 · 5603 in / 1424 out tokens · 30351 ms · 2026-05-10T15:41:13.850795+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

7 extracted references · 6 canonical work pages

[1]

L., 1997: The origin and evolution of the WSR-88D mesocyclone recognition nomogram.28th Conf

Andra, J., D. L., 1997: The origin and evolution of the WSR-88D mesocyclone recognition nomogram.28th Conf. on Radar Meteorology Amer. Meteor. Soc., Austin, TX, 364–365. Aon, 2025: 2025 climate catastrophe insight. Accessed 30-04-2025, https://assets.aon.com/-/ media/files/aon/reports/2025/2025-climate-catastrophe-insight.pdf. Ashley, W. S., A. M. Haberli...

work page doi:10.1175/waf858.1 1997
[2]

E., and M

Coffer, B. E., and M. D. Parker, 2015: Impacts of increasing low-level shear on supercells during the early evening transition.Monthly Weather Review,143 (5), 1945–1969, https://doi.org/ 10.1175/MWR-D-14-00328.1. Coffer, B. E., M. Taszarek, and M. D. Parker, 2020: Near-ground wind profiles of tornadic and nontornadic environments in the United States and ...

work page doi:10.1175/mwr-d-14-00328.1 2015
[3]

M., and R

Davies, J. M., and R. H. Johns, 1993:Some Wind and Instability Parameters Associated With Strong And Violent Tornadoes:

1993
[4]

American Geophysical Union (AGU), https://doi.org/https://doi.org/10.1029/GM079p0573

Wind Shear And Helicity, 573–582. American Geophysical Union (AGU), https://doi.org/https://doi.org/10.1029/GM079p0573. Davies-Jones, R., 1984: Streamwise vorticity: The origin of updraft rotation in supercell storms.Journal of the Atmospheric Sciences,41 (20), 2991–3006, https://doi.org/10.1175/ 1520-0469(1984)041⟨2991:SVTOOU⟩2.0.CO;2. Davies-Jones, R., ...

work page doi:10.1029/gm079p0573 1984
[5]

31, ESSA technical memorandum

No. 31, ESSA technical memorandum. IERTM-NSSL, U.S. Department of Commerce, Environmental Sci- ence Services Administration, Institutes for Environmental Research, National Severe Storms Laboratory, 1–75 pp. Homeyer, C. R., M. J. Bunkers, J. T. Allen, and A. M. Murphy, 2025: United states supercell storms and their severity: A 14-yr radar-based climatolog...

work page doi:10.1175/jamc-d-24-0185.1 2025
[6]

Accessed 30-04-2025, https://www.munichre.com/ content/dam/munichre/mrwebsitespressreleases/MunichRe-Mediarelease2025-NatCat2024. pdf. Nielsen-Gammon, J. W., and W. L. Read, 1995: Detection and interpretation of left-moving severe thunderstorms using the WSR-88D: A case study.Weather and Forecasting,10 (1), 127–140, https://doi.org/10.1175/1520-0434(1995)...

work page doi:10.1175/1520-0434(1995)010 2025
[7]

hot towers

Zeeb, A. W., W. S. Ashley, A. M. Haberlie, V. A. Gensini, and A. C. Michaelis, 2024: Supercell pre- cipitation contribution to the United States hydroclimate.International Journal of Climatology, 44 (5), 1489–1512, https://doi.org/10.1002/joc.8395. Zeitler, J., and M. Bunkers, 2005: Operational forecasting of supercell motion: Review and case studies usin...

work page doi:10.1002/joc.8395 2024