Situational Preparedness Dynamics for Sequential Tropical Cyclone Hazards

Dazhi Xi; Fengxiu Zhang; Ning Lin; Qingchun Li; Tianle Duan

REVIEW 3 major objections 4 minor 73 references

Situational Preparedness Dynamics for Sequential Tropical Cyclone Hazards

T0 review · 3 major / 4 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read Second hurricane: higher preparation, muted risk response.

desk verdict First systematic look at preparedness for back-to-back hurricanes, but the second-storm preparedness measure is built on a pre-first-storm baseline that likely inflates the headline sequence effect. read the letter →

arxiv 2504.16878 v1 pith:AAUFTILL submitted 2025-04-23 physics.soc-ph

Tianle Duan , Qingchun Li , Fengxiu Zhang , Dazhi Xi , Ning Lin This is my paper · ORCID

classification physics.soc-ph

keywords sequentialtropicalcyclonessituationalpreparednessmobilitydatapoint-of-interestvisitsspatialDurbinmodelpoweroutagespilloveraccessandfunctionalneedslandfallsequence

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 asks how people's hurricane preparedness changes when two tropical cyclones strike the same region within weeks. Using county-level mobility data showing visits to gas stations, grocery stores, and building-materials dealers before six sequential US landfalls (2020–2024), it argues that preparedness is a combined function of forecast wind speed and whether the storm is the first or second in the sequence. The central finding is that people prepare more, on average, for the second storm, but each additional unit of forecast wind moves them less than it did for the first storm. The paper also finds that a power outage during the first storm raises preparedness for the second, with effects spilling into neighboring counties. If these results hold, risk communication for back-to-back storms should expect elevated but partially fatigued audiences.

What carries the argument

The central machinery is a county-level measure of preparedness built from daily point-of-interest visit counts: a 7-day rolling average of visits to gasoline stations (mobility needs), grocery stores (daily supplies), and building-materials dealers (structural reinforcement), with a baseline defined as the average from four weeks to one week before the sequential event. Preparedness level is the ratio of the peak 7-day average in the week before landfall to that baseline; preparedness pattern is a ratio exceeding two standard deviations. These measures feed a spatial Durbin model that includes landfall sequence, forecast wind speed and their interaction, along with sociodemographic, access-and-functional-needs, infrastructure, and spatial-lag controls. The interaction term between forecast wind and sequence is what carries the claim that the second storm is prepared for more but heeded less.

What would settle it

Recompute the second-storm preparedness ratio using a baseline taken from the period after the first storm's landfall (for example, the week between the two storms); if the 'higher preparedness for the second TC' coefficient shrinks to zero or reverses sign, the claimed effect is an artifact of post-disaster activity rather than forward-looking preparation.

Watch

Extended reading notes

Core claim

On the paper's own terms, the discovery is that situational preparedness for sequential tropical cyclones is governed by an interaction between objective hazard information and recent experience. In the mobility-needs measure (visits to gasoline stations), each additional meter per second of forecast wind raises preparedness by about 0.34%, the second landfalling TC receives about 2.02% more preparedness than the first, and the interaction term shows that the wind slope is about 0.12% per m/s shallower for the second storm. First-storm power outages raise second-storm preparedness by roughly 13% for mobility needs and 24% for structural reinforcement, and these effects spill over to neighboring counties. The paper interprets the higher baseline for the second storm as heightened risk perception after the first event, and the shallower wind slope as psychological fatigue, resource depletion, or anchoring on the first storm's experience.

Load-bearing premise

The measure of second-storm preparedness uses a baseline from before the first storm, so any visits that remain elevated because of recovery, restocking, or repair after the first storm are counted as preparedness for the second rather than separated from it.

Editorial extensions

If this is right

Counties that prepare strongly for the first storm also prepare strongly for the second, suggesting preparedness is a stable county-level trait or habit.
Power outage experience is a stronger driver of subsequent preparedness than experienced wind speed, pointing to infrastructure disruption as the salient risk signal.
Spatial spillovers mean one county's power outage raises its neighbors' preparedness, so regional coordination can amplify or smooth preparedness.
AFN populations—children, elderly, and limited-English households—show persistently lower mobility-preparedness in sequential events, implying targeted outreach is needed.
Risk communication for the second storm must counter the diminished response to forecast intensity.

Reading between the lines

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

Inference: the paper's 'second storm higher preparedness' estimate rests on a baseline set before the first storm; if recovery activity kept POI visits elevated after the first landfall, part of the 2.02% effect may be post-disaster activity rather than forward-looking preparation, and recomputing with a post-first-storm baseline would separate these.
Inference: the same POI-visit framework could be applied to other compound hazards—sequential floods, heatwaves, or wildfire followed by rain—to see whether the 'more preparation but duller response' pattern generalizes beyond tropical cyclones.
Inference: the interaction effect suggests a behavioral model where prior experience anchors risk perception; this could be tested against survey-based risk-perception data collected between two storms.
Inference: because the study uses visit counts rather than purchase amounts or dwell time, the true preparedness response may be understated for grocery and building-material stores; point-of-sale data would provide a higher-fidelity test.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Colleague,

Worth a look: this is the first paper I know of that tracks situational preparedness across sequential tropical cyclones using mobility data, and the phenomenon is real and growing. The authors assemble six paired events across seven states and test whether forecast intensity, landfall sequence, first-storm impacts, and county demographics move visits to gas stations, grocery stores, and building material dealers. The core findings—higher preparedness for the second storm, weaker sensitivity to forecast wind for the second storm, and power-outage spillover into neighboring counties—are new and plausible, and the analysis is mostly careful. Linear spatial Durbin models are backed by logistic robustness checks, and the coefficient patterns are consistent. Credit where due: the data assembly is substantial (mobility, NHC forecasts, HURDAT2, Stage IV rainfall, EAGLE-I outages, Census), and the paper doesn't oversell the daily-supplies and structural-reinforcement results when they don't hold.

Where it gets soft: the stress-test note is right and it's load-bearing. The baseline for 'preparedness level' is the 7-day rolling average from four weeks to one week before the sequential events—i.e., before the first storm. The second TC's preparedness is then peak visits in the seven days before its landfall divided by that pre-first-storm baseline. If gas stations, grocery stores, and building-material dealers stay busy during recovery and restocking, the ratio for the second storm is contaminated by post-disaster activity. That directly inflates the landfall-sequence coefficient (0.0202, Table 1) and could bias the interaction if post-storm activity correlates with the second storm's forecast wind. The authors don't test any post-first-storm baseline, and the limitations section doesn't mention it. That's a fixable robustness check, not a fatal flaw, but it needs to be done before I'd trust the headline.

Other soft spots: Helene-Milton, the most salient pair, is excluded from the second-storm analysis due to missing outage data; no code or processed data is released (Dewey data is commercial, but the derived county-level measures could still be shared); and the prose occasionally slips into causal language for observational regressions. These are minor-to-moderate.

Bottom line: the central research question is well motivated, the empirical work is real, and the main results are probably in the right direction, but the baseline choice is an unaddressed threat to the main claim. Send it to peer review, but the referee should ask for an alternative-baseline robustness analysis and a clear statement of the limitation. I'd read a revised version carefully.

Referee Report

3 major / 4 minor

Summary. This paper uses county-level mobility data from Dewey Inc. to measure situational preparedness for six pairs of sequential tropical cyclones that made landfall in the same U.S. state within 21 days during 2020-2024. Preparedness is operationalized through elevated visits to gasoline stations (mobility needs), grocery stores (daily supplies), and building-material dealers (structural reinforcement), relative to a baseline. The authors fit linear spatial Durbin models and spatial logistic models to show that: stronger forecast wind is associated with higher preparedness; the second TC receives higher baseline preparedness but has a flatter response to forecast wind; first-TC power outages and preparedness levels predict second-TC preparedness, with spatial spillovers; and counties with more children, elderly, limited English proficiency, and other access/functional-need populations show lower preparedness.

Significance. If the results hold, this is the first systematic empirical documentation of preparedness dynamics for sequential tropical cyclones, a growing hazard class. The use of high-frequency mobility data across multiple events is a strength, as are the complementary linear and logistic specifications and the spatial models that explicitly account for county interdependence. The findings on AFN disparities and infrastructure spillovers have clear policy relevance for emergency management. However, the headline 'second-TC preparedness is higher' rests on a baseline choice that may conflate recovery activity with forward-looking preparedness, and the inference is based on a modest number of event pairs; the paper would be substantially strengthened by robustness analyses that address these points.

major comments (3)

[Methods, 'Mobility data' paragraph; Table 1, Model 2] The preparedness baseline is defined as the average of the 7-day rolling average from four weeks to one week before the sequential TC events—i.e., before the first storm—and is used for both TCs. For the second TC, the numerator is the peak 7-day rolling average in the seven days before its landfall. If visits to gas stations, grocery stores, or building-material dealers remain elevated after the first storm because of recovery, restocking, or repair activity, the second-TC ratio will overstate preparedness attributable to the second storm. This directly inflates the landfall-sequence coefficient (0.0202 in Table 1, Model 2) and can bias the interaction term if post-storm activity correlates with the second TC's forecast wind. The manuscript does not test an alternative baseline measured after the first storm (e.g., the week immediately preceding the second preparedness window) and does not list this choice among the limitations. Please add a robustness check with a post-first-storm baseline and discuss whether the qualitative conclusions survive.
[Statistical analysis; Tables 1 and 2] The regression data contain two observations per county (one for each TC in the pair), yet the models use state fixed effects and spatial lags without clustering standard errors by county or including county-level random effects. The two observations from the same county are likely correlated through shared demographics, infrastructure, and the common baseline; this can understate standard errors and inflate the significance of the headline coefficients. Please report cluster-robust standard errors (at the county or event level) or fit a panel specification, and indicate whether the conclusions in Tables 1 and 2 are robust.
[Results, 'First landfall TCs influence the preparedness for the subsequent TCs'; Table 2] The analysis of first-TC impacts on second-TC preparedness (Table 2) excludes the Helene-Milton pair because of missing 2024 power outage data. This is the most prominent sequential event in the study window and the motivating example of the Introduction. With only five event pairs (N=613), the power-outage and spillover estimates are based on limited cross-event variation. The paper should either provide a sensitivity analysis that includes Helene-Milton with an alternative outage measure, or explicitly discuss how the exclusion may affect the generalizability of these findings.

minor comments (4)

[Abstract; Results, 'The county-level aggregated preparedness pattern'] There are two apparent typos: in the abstract, 'approximately 13% pct' should be 'approximately 13%,' and in the final paragraph of the results, the third coefficient (1.418) is attributed to 'mobility needs preparedness' but should be 'structural reinforcement preparedness.'
[Table 2 note] The significance legend reads '***p<0.001, ***p<0.01, *p<0.05'; the second entry should be '**p<0.01'.
[Discussion, 'A higher level of preparedness...'] Causal language such as 'the first TC heightened people's risk perception' and 'power outage experiences... would increase preparedness' goes beyond what the regression design can establish. Please soften these statements to associational language or explicitly acknowledge confounding by recovery activity, resource availability, and other unmeasured factors.
[Methods, 'Mobility data'] The description of the baseline window is confusing: 'from four weeks to one week before the sequential TC events' with the parenthetical '21-day before the preparedness period.' Please clarify whether the baseline is the same for both TCs and specify the exact calendar relationship.

Circularity Check

0 steps flagged · score 1.0 of 10

No circularity: central estimates come from independent mobility, forecast, and outage data; shared-baseline concern is a measurement issue, not a circular reduction.

full rationale

Walking the derivation chain, the paper makes no first-principles derivation: the headline quantities are regression coefficients estimated from independent inputs — Dewey mobility POI visits, NHC forecast winds, HURDAT2 best-track winds, EAGLE-I power outages, and Census sociodemographics. Preparedness level is explicitly operationalized in Methods as (peak 7-day rolling average during the 7 days before landfall)/(pre-storm baseline) − 1, and preparedness pattern is defined as a visit increase exceeding two standard deviations above the baseline. The landfall-sequence and forecast-wind coefficients in Table 1 are slopes from this constructed outcome; they are not parameters fitted to the same quantity they are used to explain. The Table 2 regressions of second-TC preparedness on first-TC preparedness are associational claims, not predictions derived from fitted inputs, and neither variable is defined in terms of the other. The shared baseline denominator can create a legitimate construct-validity and spurious-correlation concern, but that is a measurement/specification threat, not circularity within the meaning of this review: there is no equation-level identity or fitted-parameter-renamed-as-prediction. Self-citations (e.g., Li et al. 2023; Dargin et al. 2021; Xi and Lin references) support background context and operational choices, but the present analysis is self-contained against external data and includes independent robustness checks (linear SDM, spatial logistic models). No uniqueness theorem is imported from the authors' prior work, and no functional ansatz is smuggled in via citation: the POI categories and baseline rule are stated in the paper and can be inspected. Thus no circular step meeting the quote-and-reduction standard is present; the score of 1 reflects only minor, non-load-bearing methodological inheritance from prior work.

Assumptions & free parameters 4 free parameters · 6 assumptions · 0 invented entities

No free fitting parameters in the physical sense; the hand-set thresholds that define the outcome are listed above. The main assumptions concern what POI visits mean, how county aggregation works, whether the pre-event baseline is a valid counterfactual for the second TC, and whether aggregate Census demographics capture access and functional needs. The paper introduces no new entities.

free parameters (4)

Preparedness pattern threshold = 2 standard deviations above baseline mean
Used to define the presence of a county-level preparedness pattern in logistic models; chosen a priori with no sensitivity analysis reported.
Preparedness period window = 7 days before landfall
Defines the outcome as the maximum 7-day rolling average of POI visits in the week before landfall; no alternative windows are tested.
Baseline window = four weeks to one week before the sequential event
Determines the counterfactual for the preparedness ratio; load-bearing for the second-TC comparison and not tested for robustness.
Sequential event interval cutoff = 21 days between landfalls in the same state
Defines which TC pairs enter the sample because no official definition exists; alternative intervals are not examined.

assumptions (6)

domain assumption POI visit surges to gas stations, grocery stores, and building-material dealers are valid proxies for situational preparedness.
Used throughout the analysis; consistent with prior published work (Dargin et al. 2021; Li et al. 2023) but not independently validated for the sequential-TC setting.
domain assumption County-level aggregation of mobility records adequately represents household-level preparedness behavior.
All variables are mapped to county resolution; the paper provides no validation at the household or individual level.
domain assumption The pre-sequential-event baseline is an appropriate counterfactual for both the first and second TCs.
Methods define the baseline before the sequential event; the second-TC preparedness period lies after the first landfall, so recovery and restocking activity may contaminate the ratio.
domain assumption Queen-contiguity spatial weights capture the relevant neighborhood structure for spillover effects.
Used in all spatial models; alternative spatial weight definitions are not tested.
domain assumption County-level Census demographics adequately measure access and functional needs.
AFN is proxied by proportions of children under 5, adults over 65, disabled people, limited English speakers, and households without vehicles; these are aggregate proxies, not individual-level measures.
domain assumption Spatial regression residuals are independent across county-event observations.
Each county can appear twice in the N=1360 model (once per TC), yet no clustering by county or event is reported, so standard errors may be understated.

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Cite this review

Pith. "Pith review of Situational Preparedness Dynamics for Sequential Tropical Cyclone Hazards." pith.science (2026). https://pith.science/paper/AAUFTILL

@misc{pith2026250416878,
  author       = {Pith},
  title        = {Pith review of: Situational Preparedness Dynamics for Sequential Tropical Cyclone Hazards},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/AAUFTILL}},
  note         = {Machine review of arXiv:2504.16878}
}

read the original abstract

Sequential tropical cyclone hazards--two tropical cyclones (TCs) making landfall in the same region within a short time--are becoming increasingly likely. This study investigates situational preparedness dynamics for six sequential TC events that affected seven states in the United States from 2020 to 2024. We find a combined effect of forecast wind speed and landfall sequence of a TC. Stronger forecast wind is always associated with higher preparedness levels. People tend to show a higher preparedness level for the second TC but are more sensitive to the increasing forecast wind speed of the first TC. We also find that the counties showing high preparedness levels for the first TC consistently show high preparedness levels for the subsequent one. Power outages induced by the first TC significantly increase preparedness for the subsequent TC (e.g., approximately 13% pct for mobility needs preparedness and 24% for structural reinforcement preparedness when increasing one-unit customers out). We identified spatial dependency in preparedness across counties. Power outage experiences in first TCs show statistically significant spillover effects on neighboring counties' preparedness levels for second TCs. Throughout sequential TCs, people with access and functional needs consistently show lower preparedness levels.

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