Generated storm lifecycles fill the rare-event tail of short rainfall records

Valerie Tsao; Nathaniel W. Chaney; Manolis Veveakis; Marta Zaniolo

cs.LGOct 30, 2026

Generated storm lifecycles fill the rare-event tail of short rainfall records

Authors: Valerie Tsao, Nathaniel W. Chaney, Manolis Veveakis, Marta Zaniolo

Organizations: Department of Civil and Environmental Engineering, Duke University, Durham, NC 27708, USA.

Abstract

Historically rare ''one-in-a-generation'' storms are occurring with unprecedented frequency, causing catastrophic damage and mounting casualties, and exposing the vulnerability of infrastructure designed around short observational records. To overcome the limitations of sparse data, we present StormGen, a generative machine-learning model that transforms an observed storm's initial hours into complete, physically plausible hourly rainfall lifecycles in minutes. Evaluated on a decade of southeast Texas rainfall, StormGen produced close counterparts for 4\5% of the most severe 2% of storms--compared to just 3% for stochastic storm transposition--and maintained spatial coherence over full 24-hour sequences. Crucially, from a single hour of initial rainfall, the model successfully reconstructed the specific lifecycle, spatial pattern, and extreme catchment totals of Hurricane Harvey itself, despite the event being entirely absent in training. Because storm timing alone can more than double peak flows, these complete generated scenarios provide a vital mechanism to stress-test flood protections against unprecedented extremes.

Figures & tables

Figure 1: StormGen generates multiple plausible storm futures from an observed beginning. ( a ) Study tile over the San Jacinto region of southeast Texas. ( b ) StormGen workflow. Six hours of observed rainfall (the seed) and user-specified controls (duration, intensity) condition the model, which returns an ensemble of complete continuations. ( c ) Rainfall for a storm on 24 January 2023 in HRRR and two StormGen members. Columns left of the dashed line show the six-hour history ending at initialization (0h); columns to the right show continuations at +1, +3, and +6 hours. ( d and e ) Domain-mean rainfall and wet-area percentage for HRRR (brown, dashed) and the median of 24 StormGen members (teal), with 25th to 75th and 10th to 90th percentile bands. Gray shading marks the history. Wet pixels have rainfall $\geq$ 0.05 mm h ${-1}$ . ( f ) Controllability. Continuations of the same seed requested as a shorter (6-hour) and a longer (18-hour) event, with rainfall maps and time series. ( g ) Joint distribution of duration and peak rainfall for HRRR and StormGen events, with density colors for the rarest 10, 4, and 2% of storms (1-in-10, 1-in-25, 1-in-50). These classes are fractions of catalog events, not annual return periods. Ensemble construction and event definitions are given in Materials and Methods.

Figure 2: StormGen generates realistic, nonduplicate storms and improves coverage of the extreme tail. ( a ) Continuous ranked probability score (CRPS) of StormGen and SST ensemble against HRRR by lead time; lower is better. Lines show means and shading shows the spread across events. ( b ) Spatial power spectra of rainfall fields. Higher spatial frequencies correspond to finer-scale features, such as localized area of heavy rain. ( c ) Distance of each event to its nearest training event against its similarity to an independent held-out reference set, with marginal densities. The shaded region marks events that resemble observations without duplicating training events. These quantities measure duplication and resemblance, not the probability of physical realism. ( d ) Percentage of reference events in each severity class that have a close counterpart in each catalog, at a fixed tolerance in storm-property space. Error bars show pointwise 95% percentile bootstrap confidence intervals from 2,000 resamples of reference events within each severity class, with candidate catalogs and matching criteria held fixed. ( e and g ) Joint distribution of duration and peak rainfall for the rarest 10, 5, and 2% events of errors in each catalog, shown as density contours. HRRR, StormGen, and SST are shown in brown, teal, and magenta, with 6,000 events each. Continuations that share a seed are dependent draws. Diagnostic definitions are given in Materials and Methods.

Figure 3: StormGen generates storms beyond its training record, including catchment-scale rainfall hazard for Hurricane Harvey. ( a ) Withheld-tail experiment. Storms beyond a fixed exclusion boundary in duration and peak rainfall (dashed line) were withheld before training. Gray points show retained storms, open circles the 96 withheld storms, teal points generated members, and the orange diamond the available Harvey segment. The strip below orders withheld storms by severity and shows, for each, the fraction of its 32 members within a factor of 1.25 of both its observed duration and peak. ( b ) Next-24-hour accumulation for the HRRR reference, a typical member (nearest the ensemble median), and an upper-tail member (nearest the 90th percentile), initialized at 02:00 UTC on 26 August 2017 from a single hour of rainfall. Lower right, area receiving at least a given accumulation for HRRR (brown) and the ensemble median (teal), with 10th to 90th and 25th to 75th percentile bands. ( c ) Hourly catchment-average rainfall over the Greens and Halls Bayou watersheds for HRRR (top) and 64 members ordered by duration. The dashed line marks the end of the 24-hour window; the HRRR record ends at its first missing hour. ( d ) Probability that 24-hour rainfall exceeds 300 mm, with the watershed outline and the HRRR 300-mm contour ([ ]). Right, distribution of the 64 catchment-average 24-hour accumulations, with the ensemble median (dashed, 268 mm) and the HRRR reference (solid, 168 mm). Catchment averages use area-weighted intersections computed in an equal-area projection. Details are given in Materials and Methods.

Figure 4: Rainfall-pattern variability and modeled flood responses at a common design depth. ( a ) Greens and Halls Bayou catchments with $6\times 10^{6}$ m 3 of added storage divided between upper Greens (45%) and Halls (55%). ( b ) Rainfall fields and hydrographs for a uniform Atlas 14 design storm, 39 HRRR patterns, and 1,000 additional scenarios. All have 429 mm catchment-average depth over 24 h, using the Atlas 14 100-year point estimate, under identical wet conditions and routing. Heavy curves show medians; bands show central 50% and 80% scenario ranges. The dashed line marks the assumed 450 m 3 s -1 capacity. ( c ) Peak-discharge magnitude and timing (left) and scenario exceedance fractions (right), compared with 87 held-out HRRR patterns (open circles and dotted curves). ( d ) Held-out response coverage versus available HRRR events, with eight additional scenarios per parent. Counterparts match peak discharge within a factor of 1.25 and peak timing within 3 h. Curves show medians and bands the 10th–90th percentiles across 500 paired catalog-subsampling and held-out-bootstrap repetitions.

Figure S1: Agreement between conditional catalog probabilities and NBM. Panels show the change in agreement after RainyDay augmentation for ( a ) Caney Creek and ( b ) East Fork San Jacinto, using conservative and nearest-cell remapping. The plotted quantity is

D_{\mathrm{observed+RainyDay}}-D_{\mathrm{observed}}

, where

D

is the basin-area-weighted mean squared difference between catalog and NBM cellwise exceedance probabilities. Negative values indicate closer agreement with NBM.Points show mean changes, and whiskers show pointwise 95% intervals from 2,000 paired bootstrap resamples of meteorological storm clusters. Shading marks the prespecified primary thresholds of 25.4 and 50.8 mm; the remaining thresholds provide diagnostic comparisons. The evaluation includes 93 basin cases—46 in Caney Creek and 47 in East Fork—from 47 cross-basin storm clusters. Probabilities are compared six hours after event onset and refer to rainfall over the same fixed onset-to-24-hour interval. This comparison measures agreement between probability estimates, not verification against observed rainfall.

Figure S2: Sensitivity of catalog–NBM agreement to spatial remapping. Each point compares the effect of RainyDay augmentation for the same basin case and rainfall threshold under nearest-cell and conservative remapping. Panels show ( a ) Caney Creek and ( b ) East Fork San Jacinto. Both coordinates represent $D_{\mathrm{observed+RainyDay}}-D_{\mathrm{observed}}$ , with negative values indicating closer agreement with NBM, as in Fig. S1 . Color identifies the rainfall threshold, and the dashed diagonal marks identical augmentation effects under the two mappings. All seven available thresholds are included. Rainfall depths are remapped before calculating exceedances. Multiple thresholds and basin cases can belong to the same meteorological storm, so the plotted points are not independent replicates.

Figure S3: Stage IV and RainyDay rainfall-frequency estimates under a common tail model. Panels show ( a ) 24-hour and ( b ) 72-hour basin-average rainfall depths for East Fork San Jacinto above Cleveland. Both estimates use the complete source water years 2003–2020 and a common Poisson–generalized Pareto tail model with three exceedances per year. RainyDay uses 366 source storms at a candidate rate of

366/18

storms per year, with 100 realizations of 1,000 simulated years each. This candidate rate is distinct from the tail-exceedance rate. Lines show the Stage IV estimate and the mean RainyDay estimate. Brown bands show 90% intervals from resampling observed water years. Lavender bands show 90% simulated-water-year bootstrap intervals, based on 500 resamples distributed across the 100 realizations. Dotted limits mark the 5th–95th percentile range of the realization estimates. Both frequency curves draw on the same source period, rather than an independent held-out record.The RainyDay uncertainty summaries are conditional on the finite source catalog and simulation model; simulated years do not add independent observed climate exposure.

Figure S4: Storm-property profiles by severity class. Faint lines connect six properties of individual catalog entries: duration, peak rainfall, wet-pixel mean rainfall, storm speed, wet-area fraction, and event depth. Colors distinguish low, medium, high, and extreme classes, separated by shared reference-rarity thresholds at the 50th, 80th, and 95th percentiles; ties are assigned to the upper class. Thick solid lines with filled markers show HRRR-labelled class medians, and thick dashed lines with open markers show StormGen-labelled class medians. For property j, the plotted coordinate is [(1+xj)-aj]/(bj-aj), where aj and bj are the pooled minimum and maximum of the log-transformed property across both catalogs. Both catalogs use the same scale without clipping; constant properties map to zero. The connecting lines link different properties, not successive times. Class profiles show coordinatewise medians and need not describe any single storm.

Figure S5: Illustrative comparison of nowcasting and full-storm continuation. The fields and forecasts are constructed examples; their diagnostics illustrate the evaluation framework rather than establish measured model skill. a, Proposed cross-site evaluation linking the San Jacinto catalogue to six-hour Southern Great Plains (SGP) seeds. Saved history frames illustrate the conditioning input. Parallel timelines contrast joint StormGen generation through +24 h with recursive three-hour NowcastNet rollouts. b, Rainfall at various hours for the reference, NowcastNet, and a single StormGen ensemble medoid, followed by StormGen exceedance probabilities, $P(R\geq 5~\mathrm{mm\,h^{-1}})$ . The medoid minimizes whole-sequence distance to other members. Rainfall panels share a spatial extent and colour scale; probabilities use a separate scale. The divider separates the native nowcasting horizon from the extended continuation. c, Rainfall-weighted centroid tracks. Thin cyan lines show StormGen members, and the thick cyan line shows their pointwise median. Ellipses depict one-standard-deviation reference rainfall footprints at +3, +6, +12, and +24 h, rather than confidence regions. d, Lifecycle trajectories relating wet-area percentage to mean rainfall intensity over wet pixels ( $R\geq 1~\mathrm{mm\,h^{-1}}$ ). Marker colour indicates lead time; faint cyan curves show individual StormGen members. e, Fractions skill score for $R\geq 2~\mathrm{mm\,h^{-1}}$ at 8-km (solid) and 32-km (dashed) neighbourhood scales, with persistence shown at 8 km. The vertical line and shading identify leads beyond the native three-hour horizon. Bands show 95% paired seed-bootstrap intervals (1,000 resamples; 24 seeds and 12 StormGen members per seed). f, Post–3 h error trends for six diagnostics, plotted on a common vertical scale as percentages of each metric’s pooled 95th-percentile error per hour; positive values indicate worsening. Points show mean trends, and whiskers show 95% seed-bootstrap intervals. The rightmost comparison reports termination-time mean absolute error on a separate scale, using follow-through to +36 h.

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Shunan Zheng, John J. Hasenbein

Operations Research and Industrial Engineering University of Texas at Austin Austin, TX, USA

Surrogate Modeling PDE Surrogate Modeling Conditional Diffusion Models

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Predicting storm surge induced by landfalling tropical cyclones is crucial for flood mitigation and coastal risk management. Traditionally, physics-based numerical models simulate storm surge by solving the Navier--Stokes equations using numerical methods, but these simulations are computationally expensive. Generative models are promising for storm surge emulation because they can generate diverse realizations rather than producing a single deterministic prediction. However, their use for storm surge emulation remains largely unexplored. In this paper, we leverage diffusion models for storm surge surrogate modeling, combining a baseline prediction stage with conditional generation to provide a more interpretable modeling framework. We develop SurgeGen, a two-stage generative framework for generating storm surge scenarios conditioned on hypothetical storms with parameters defined in a continuous space. First, a baseline model produces a coarse estimate of the storm surge height. This estimate then conditions a diffusion model, which generates refined storm surge scenarios that better capture spatial patterns and variability. We demonstrate that our approach can generate realistic and diverse storm surge scenarios under conditions both within and outside the training distribution.

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