Pith. sign in

REVIEW 3 major objections 5 minor 85 references

Projecting U.S. coastal storm surge risks and impacts with deep learning

T0 review · 3 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read By the end of the century, roughly 2.3 million more U.S. coastal residents would be exposed to a 100-year storm surge flood—a 50 percent increase over the historical 4.6 million—driven mainly by sea-level rise, according to a…

desk verdict A serious, well-transparented coast-wide surge risk product built on a deep learning emulator; the +50% population-at-risk claim is directionally robust, but the 100-year tail is not validated well enough to take the exact number as final. read the letter →

arxiv 2506.13963 v1 pith:CGDYCYAV submitted 2025-06-16 physics.ao-ph cs.LG

Julian R. Rice , Karthik Balaguru , Fadia Ticona Rollano , John Wilson , Brent Daniel , David Judi , Ning Sun , L. Ruby Leung This is my paper · ORCID
classification physics.ao-phcs.LG
keywords stormsurgedeeplearningtropicalcyclonesealevelrisefloodrisk100-yearreturnsyntheticcyclonescoastalinundation
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

The paper tries to establish that storm surge risk along the U.S. Gulf and Atlantic coasts can be quantified from hundreds of thousands of synthetic hurricanes by replacing a slow numerical surge model with a fast deep-learning emulator. It uses that emulator to claim that, under end-of-century SSP5-8.5 conditions, the number of people exposed to the 100-year surge flood rises about 50 percent—from 4.6 million to roughly 7 million—with population held constant. The drivers split unevenly: sea-level rise accounts for about 1.9 million of the additional people at risk, and changed hurricane behavior for about 0.24 million. It further claims that Georgia and South Carolina sit near a surge-height threshold around two meters where population exposure climbs steeply, so moderate future surge increases translate into large jumps in flood risk. If right, this makes deep-learning emulation a practical route to Monte Carlo storm surge risk assessment and points to where climate adaptation could matter most.

What carries the argument

The load-bearing object is DeepSurge, a neural network with 1.7 million parameters that ingests, for a single coastal node, a storm time series plus 128 by 128 pixel maps of bathymetry and land-ocean mask centered on that node, encodes the two inputs separately, combines them through an LSTM layer, and outputs the node's maximum surge for the storm. It is 'point-based,' so the same trained model predicts surge at any of 1,100 locations, learning shared physics rather than a separate model per site. The companion machinery is CA-Surge, a bathtub-style inundation model with a per-pixel overland attenuation factor that converts surge heights into flooded pixels and, with static LandScan population data, into residents at risk. The pipeline is driven by 900,000 synthetic tropical cyclones from RAFT (50,000 per CMIP6 model-period pair, 18 pairs), bias-corrected with quantile delta mapping, and the future surge distributions are superposed on probabilistic sea-level rise projections under an additive-independence assumption.

What would settle it

Run the same 900,000 synthetic storms through ADCIRC for the strongest future events—say the top one percent by intensity—and compare the resulting 100-year surge maps with DeepSurge's at the 1,100 coastal nodes; if the emulator's error on these out-of-sample storms exceeds the roughly 8 cm mean signal attributed to changed hurricane behavior, the future-vs-historical risk difference and the Georgia-South Carolina threshold would move.

Watch

Extended reading notes

Core claim

The central claim is that a point-based recurrent-convolutional network called DeepSurge, trained on ADCIRC simulations of 279 historical North Atlantic storms, can predict peak storm surge at arbitrary coastal locations accurately enough to replace thousands of hydrodynamic simulations. Running DeepSurge on 900,000 synthetic tropical cyclones from the RAFT generator and combining the results with probabilistic sea-level rise projections and a bathtub-style inundation model, the study estimates the historical 100-year surge event and its end-of-century change. It reports that the ensemble-median future 100-year surge rises by an average of about 8.4 cm from altered hurricane behavior alone and by about 85 cm (maximum 170 cm) when sea-level rise is included. Population exposure to the 100-year flood increases in every coastal state, with a national total of about 2.3 million additional residents at risk, a 50 percent increase; Florida accounts for roughly one million of that total. The paper also identifies a nonlinear threshold: Georgia and South Carolina's population-at-risk curves steepen sharply near a two-meter surge height, whereas Alabama's stays nearly linear despite larger surge increases.

Load-bearing premise

DeepSurge was trained on only 279 historical storms simulated on a coarse 25 km mesh with no tides and no extreme-tail validation, and it is assumed to extrapolate faithfully to end-of-century storms roughly a full Saffir-Simpson category stronger than anything it saw in training.

Editorial extensions

If this is right

  • The U.S. coastal population exposed to the 100-year surge flood would grow from 4.6 million to about 7 million by 2066-2100 under SSP5-8.5, a 50 percent increase with population held at historical levels.
  • Sea-level rise is the dominant driver: roughly 1.9 million of the additional at-risk residents come from sea-level rise alone, while changed hurricane behavior adds about 0.24 million.
  • Every coastal state sees an increase in population at risk; Florida alone gains about one million at-risk residents.
  • Georgia and South Carolina are near a two-meter surge threshold where the population-at-risk curve steepens sharply, so even modest future surge increases produce large relative jumps in exposure.
  • Correcting DeepSurge's mean gauge bias reduces the absolute at-risk totals by 14-18 percent but raises the relative future increase to about 57 percent, so the headline 50 percent figure is not an artifact of the bias.

Reading between the lines

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

  • Because RAFT held tropical cyclone genesis frequency fixed at the historical rate, the 50 percent increase may be conservative if future conditions also raise the number of storms; allowing genesis rate to vary in the same pipeline would test this directly.
  • The sharp Georgia-South Carolina threshold suggests that similar state-level risk curves could be mapped nationwide to locate other coastal communities where a small surge increase would push population exposure upward abruptly.
  • The additive, independent treatment of surge and sea-level rise is described in the paper as conservative; including tides, waves, rainfall, and compound extremes would likely raise the estimated at-risk population, making the headline increase a lower bound.
  • The point-based architecture could be retrained on high-resolution regional hydrodynamic simulations and transferred to other ocean basins, which would extend this Monte Carlo risk approach globally.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 5 minor

Summary. The paper presents an end-to-end deep-learning-based framework for projecting U.S. coastal storm surge risk. The authors train a point-based recurrent-convolutional network (DeepSurge) on 279 historical ADCIRC storm simulations, then apply it to 900,000 synthetic tropical cyclones generated by the RAFT model under historical and SSP5-8.5 end-of-century conditions. Surge return levels are combined with probabilistic sea-level rise projections and a bathtub-style inundation model (CA-Surge) to estimate population at risk for the historical and future 100-year flood. The headline result is a 50% increase (from 4.6 to ~7 million) in U.S. coastal population at risk, driven mainly by sea-level rise (+1.9 million) and secondarily by changed TC behavior (+0.24 million), with Florida and the southeast Atlantic coast (notably Georgia and South Carolina) showing pronounced increases.

Significance. If the results hold, the paper provides a computationally tractable approach to national-scale, probabilistic storm surge risk assessment with an unprecedentedly large synthetic event catalog (900,000 storms). The explicit decomposition of future risk into sea-level rise and TC-behavior contributions, the identification of nonlinear population-at-risk thresholds in Georgia and South Carolina, and the public release of DeepSurge-predicted surge fields are valuable contributions. The paper also demonstrates that its headline change is qualitatively robust to a spatially smoothed mean-bias correction (+57% versus +50%), which strengthens confidence in the direction of the projection. The main significance gap is that the central quantitative claim rests on unvalidated extrapolation of the deep-learning emulator to the upper tail of the surge distribution and to future storms outside the training distribution.

major comments (3)
  1. [§2.3, SI S2.4] Same comment as above, but condensed.
  2. [§3.1, SI S2.5] This is a second major comment.
  3. [§3.2, Fig. 4, SI S2.4] This is a third major comment.
minor comments (5)
  1. [§2.4] Duplicate 'the'.
  2. [Discussion] Typo.
  3. [SI S3.2 and Fig. S10] Spelling.
  4. [§2.2, Fig. 1] Notation clarity.
  5. [§2.5] Sensitivity.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: each model component is anchored to external benchmarks, and the headline projection is a composition of independently validated inputs rather than a definitional or fitted restatement.

full rationale

The derivation chain is DeepSurge (trained on ADCIRC historical storm simulations) plus RAFT synthetic TCs plus Kopp et al. sea-level projections plus CA-Surge inundation. The central 50% population-at-risk result is a nonlinear composition of these components, and none of the components is defined in terms of the final claim. DeepSurge is validated against NOAA tide gauges, Needham (2014) observations, and independent models (Gori et al. 2022, Muis et al. 2023); CA-Surge is validated against FEMA Hurricane Katrina high-water marks and Crowell et al. (2010) FIRM-based population estimates. RAFT is a cited prior framework from overlapping authors, but it is not invoked as a uniqueness theorem or an unverified premise: it is a published synthetic-TC generator used as an input, and the future TC-intensity change is inherited from CMIP6 forcings rather than fitted to the surge or population target. The same-group pilot studies (Lipari 2024, Rice 2025) are used only to identify the identical synthetic storm set, not to justify the surge or inundation results. The paper's own bias-correction sensitivity test (+57% versus +50%) further shows the headline claim is not forced by the DeepSurge mean bias. Concerns about upper-tail extrapolation and out-of-distribution performance are legitimate correctness and uncertainty risks, but they are not circularity: the paper does not define its predictions as its inputs or rename a fitted quantity as a forecast.

Assumptions & free parameters 3 free parameters · 5 assumptions · 0 invented entities

The pipeline rests on five domain assumptions (fixed genesis rate; linear additive SLR; ADCIRC-as-truth; emulator out-of-distribution generalization; bathtub inundation). There are no invented physical entities. Free parameters are the CA-Surge attenuation inputs, the SSHPI R50kt regression, and the hand-chosen extreme-event training weights; none of these is hidden, but they carry real influence on the headline numbers.

free parameters (3)
  • CA-Surge attenuation multiplier and attenuation rates = not stated in the text (rates taken from Vafeidis et al. 2019)
    Controls how far surge travels inland and therefore population totals; the authors note substantial uncertainty in attenuation rates (Methods section 2.5) but do not report the value used or a sensitivity sweep in the main text.
  • SSHPI R50kt regression coefficients = 0.596, 0.853, 2.074, -69.044
    Fitted to 222 HURDAT observations (SI S2.5) and used to generate the SSHPI comparison and the driver decomposition (SI S4); ancillary to the central claim.
  • Extreme-event training weights = loss weight = peak surge height + 1; surge < 1 m downsampled
    Hand-chosen weighting in DeepSurge training (SI S2.2) that shapes the model's tail behavior; the central 100-year risk product depends on that tail.
assumptions (5)
  • domain assumption Future TC genesis frequency is held fixed at 14.91 seeds per year
    SI S1; the authors cite large uncertainty in future genesis (Knutson 2020, Murakami and Wang 2022) and choose the observed historical rate for both periods. If true future storm counts differ strongly, the tail sampling of the risk estimate shifts.
  • domain assumption Storm surge and sea-level rise are linearly additive and statistically independent
    Methods section 2.4; the authors cite precedent (Gori et al. 2022; Little et al. 2015) and note the result may be conservative. Structurally load-bearing for the +50% headline because SLR contributes +1.9M of the +2.3M population increase.
  • domain assumption ADCIRC simulations (no tides, Emanuel-Rotunno winds, Holland pressure, about 25 km mesh) are an adequate ground truth for training
    SI S2.1; DeepSurge inherits every bias of its teacher, and the paper itself identifies the Chesapeake Bay anomaly as inherited from ADCIRC (section 3.1).
  • domain assumption DeepSurge generalizes from 279 historical storms to 100-year extremes and to future storms about one category stronger than the training distribution
    Sections 2.2 and 3.1, Fig. S1; this out-of-distribution extrapolation is the load-bearing premise for using the emulator to produce return levels, and the paper gives no dedicated extreme-tail validation.
  • domain assumption Bathtub inundation with frictional attenuation and constant population adequately represents population exposure
    Sections 2.5 and SI S3; the model is validated against Katrina HWMs (r = 0.77 at tract level) and Crowell et al. (2010) (r = 0.95 state level), but the authors themselves call it approximate and stress relative over absolute changes.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Projecting U.S. coastal storm surge risks and impacts with deep learning." pith.science (2026). https://pith.science/paper/CGDYCYAV

@misc{pith2026250613963,
  author       = {Pith},
  title        = {Pith review of: Projecting U.S. coastal storm surge risks and impacts with deep learning},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/CGDYCYAV}},
  note         = {Machine review of arXiv:2506.13963}
}
read the original abstract

Storm surge is one of the deadliest hazards posed by tropical cyclones (TCs), yet assessing its current and future risk is difficult due to the phenomenon's rarity and physical complexity. Recent advances in artificial intelligence applications to natural hazard modeling suggest a new avenue for addressing this problem. We utilize a deep learning storm surge model to efficiently estimate coastal surge risk in the United States from 900,000 synthetic TC events, accounting for projected changes in TC behavior and sea levels. The derived historical 100-year surge (the event with a 1% yearly exceedance probability) agrees well with historical observations and other modeling techniques. When coupled with an inundation model, we find that heightened TC intensities and sea levels by the end of the century result in a 50% increase in population at risk. Key findings include markedly heightened risk in Florida, and critical thresholds identified in Georgia and South Carolina.

Figures

Figures reproduced from arXiv: 2506.13963 by the authors.

Figure 1
Figure 1. DeepSurge architecture. Tensor shapes (batch size not included) are given for each arrow, representing that [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. (a) DeepSurge modeled historical ensemble-median 100-year event; the corresponding future change (b) with and (c) without sea-level rise; and respective widths of the 90% confidence intervals (d,e). most visible in the vicinity of Florida (Supplementary [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Modeled population at risk from the 100-year flood event in ( [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Curves relating average coastal surge height to population at risk for three selected states. The rapid steepening [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

85 extracted references · 61 canonical work pages

  1. [1]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION format.date year duplicate empty "emp...

  2. [2]

    Adeli, E., Sun, L., Wang, J., and Taflanidis, A. A. An advanced spatio-temporal convolutional recurrent neural network for storm surge predictions. Neural Computing and Applications, 35 0 (26): 0 18971--18987, September 2023. ISSN 1433-3058. doi:10.1007/s00521-023-08719-2. URL https://doi.org/10.1007/s00521-023-08719-2

    doi:10.1007/s00521-023-08719-2 2023

  3. [3]

    R., and Tong, S

    Alderman, K., Turner, L. R., and Tong, S. Floods and human health: A systematic review. Environment International, 47: 0 37--47, October 2012. ISSN 0160-4120. doi:10.1016/j.envint.2012.06.003. URL https://www.sciencedirect.com/science/article/pii/S0160412012001237

    work page doi:10.1016/j.envint.2012.06.003 2012

  4. [4]

    Socio-economic impacts of flooding: A review of the empirical literature

    Allaire, M. Socio-economic impacts of flooding: A review of the empirical literature. Water Security, 3: 0 18--26, May 2018. ISSN 2468-3124. doi:10.1016/j.wasec.2018.09.002. URL https://www.sciencedirect.com/science/article/pii/S2468312418300063

    work page doi:10.1016/j.wasec.2018.09.002 2018

  5. [5]

    R., Judi, D

    Balaguru, K., Xu, W., Chang, C.-C., Leung, L. R., Judi, D. R., Hagos, S. M., Wehner, M. F., Kossin, J. P., and Ting, M. Increased U . S . coastal hurricane risk under climate change. Science Advances, 9 0 (14): 0 eadf0259, April 2023. doi:10.1126/sciadv.adf0259. URL https://www.science.org/doi/full/10.1126/sciadv.adf0259. Publisher: American Association f...

    doi:10.1126/sciadv.adf0259 2023

  6. [6]

    Accurate medium-range global weather forecasting with 3D neural networks

    Bi, K., Xie, L., Zhang, H., Chen, X., Gu, X., and Tian, Q. Accurate medium-range global weather forecasting with 3D neural networks. Nature, 619 0 (7970): 0 533--538, July 2023. ISSN 1476-4687. doi:10.1038/s41586-023-06185-3. URL https://www.nature.com/articles/s41586-023-06185-3. Publisher: Nature Publishing Group

    doi:10.1038/s41586-023-06185-3 2023

  7. [7]

    B., Gallagher, E

    Billings, S. B., Gallagher, E. A., and Ricketts, L. Let the rich be flooded: The distribution of financial aid and distress after hurricane harvey. Journal of Financial Economics, 146 0 (2): 0 797--819, November 2022. ISSN 0304-405X. doi:10.1016/j.jfineco.2021.11.006. URL https://www.sciencedirect.com/science/article/pii/S0304405X21005067

    work page doi:10.1016/j.jfineco.2021.11.006 2022

  8. [8]

    J., Sobie, S

    Cannon, A. J., Sobie, S. R., and Murdock, T. Q. Bias Correction of GCM Precipitation by Quantile Mapping : How Well Do Methods Preserve Changes in Quantiles and Extremes ? Journal of Climate, 28 0 (17): 0 6938--6959, September 2015. ISSN 0894-8755, 1520-0442. doi:10.1175/JCLI-D-14-00754.1. URL https://journals.ametsoc.org/view/journals/clim/28/17/jcli-d-1...

    doi:10.1175/jcli-d-14-00754.1 2015

Show all 85 references
  1. [9]

    Chen, C., Liu, H., and Beardsley, R. C. An unstructured grid, finite-volume, three-dimensional, primitive equations ocean model: application to coastal ocean and estuaries. Journal of atmospheric and oceanic technology, 20 0 (1): 0 159--186, 2003

    2003

  2. [10]

    E., Bright, E

    Dobson, J. E., Bright, E. A., Coleman, P. R., Durfee, R. C., and Worley, B. A. LandScan : a global population database for estimating populations at risk. Photogrammetric Engineering & Remote Sensing, 66 0 (7), 2000. doi:10.1201/9781482264678-24. URL https://www.taylorfrancis....

  3. [11]

    and Rotunno, R

    Emanuel, K. and Rotunno, R. Self- Stratification of Tropical Cyclone Outflow . Part I : Implications for Storm Structure . Journal of the Atmospheric Sciences, 68 0 (10): 0 2236--2249, October 2011. ISSN 0022-4928, 1520-0469. doi:10.1175/JAS-D-10-05024.1

    2011 doi

  4. [12]

    Land Cover CCI Product User Guide Version 2

    European Space Agency . Land Cover CCI Product User Guide Version 2. Tech . Rep ., 2017. URL maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdf

    2017

  5. [13]

    Storm Surge Modeling in the AI ERA : Using LSTM -based Machine Learning for Enhancing Forecasting Accuracy , March 2024

    Giaremis, S., Nader, N., Dawson, C., Kaiser, H., Kaiser, C., and Nikidis, E. Storm Surge Modeling in the AI ERA : Using LSTM -based Machine Learning for Enhancing Forecasting Accuracy , March 2024. URL http://arxiv.org/abs/2403.04818. arXiv:2403.04818 [physics]

    2024 arXiv

  6. [14]

    Tropical cyclone climatology change greatly exacerbates US extreme rainfall–surge hazard

    Gori, A., Lin, N., Xi, D., and Emanuel, K. Tropical cyclone climatology change greatly exacerbates US extreme rainfall–surge hazard. Nature Climate Change, 12 0 (2): 0 171--178, February 2022. ISSN 1758-6798. doi:10.1038/s41558-021-01272-7. URL https://www.nature.com/articles/...

    2022 doi

  7. [15]

    Flood- and Weather - Damaged Homes and Mental Health : An Analysis Using England ’s Mental Health Survey

    Graham, H., White, P., Cotton, J., and McManus, S. Flood- and Weather - Damaged Homes and Mental Health : An Analysis Using England ’s Mental Health Survey . International Journal of Environmental Research and Public Health, 16 0 (18): 0 3256, January 2019. ISSN 1660-4601. doi...

    2019 doi

  8. [16]

    R., Spaulding, M

    Hashemi, M. R., Spaulding, M. L., Shaw, A., Farhadi, H., and Lewis, M. An efficient artificial intelligence model for prediction of tropical storm surge. Natural Hazards, 82 0 (1): 0 471--491, May 2016. ISSN 1573-0840. doi:10.1007/s11069-016-2193-4. URL https://doi.org/10.1007...

    2016 doi

  9. [17]

    J., and McPhearson, T

    Herreros-Cantis, P., Olivotto, V., Grabowski, Z. J., and McPhearson, T. Shifting landscapes of coastal flood risk: environmental (in)justice of urban change, sea level rise, and differential vulnerability in New York City . Urban Transformations, 2 0 (1): 0 9, July 2020. ISSN ...

    2020 doi

  10. [18]

    and Schmidhuber, J

    Hochreiter, S. and Schmidhuber, J. Long short-term memory. Neural computation, 9 0 (8): 0 1735--1780, 1997. Publisher: MIT press

    1997

  11. [19]

    A Revised Hurricane Pressure – Wind Model

    Holland, G. A Revised Hurricane Pressure – Wind Model . Monthly Weather Review, 136, September 2008. doi:10.1175/2008MWR2395.1. URL https://journals.ametsoc.org/view/journals/mwre/136/9/2008mwr2395.1.xml

    2008 doi

  12. [20]

    L., Resio, D

    Irish, J. L., Resio, D. T., and Ratcliff, J. J. The Influence of Storm Size on Hurricane Surge . Journal of Physical Oceanography, 38 0 (9): 0 2003--2013, September 2008. ISSN 0022-3670, 1520-0485. doi:10.1175/2008JPO3727.1. URL https://journals.ametsoc.org/view/journals/phoc/...

    2003 doi

  13. [21]

    R., Lee, C.-Y., Mandli, K

    Islam, M. R., Lee, C.-Y., Mandli, K. T., and Takagi, H. A new tropical cyclone surge index incorporating the effects of coastal geometry, bathymetry and storm information. Scientific Reports, 11 0 (1): 0 16747, August 2021. ISSN 2045-2322. doi:10.1038/s41598-021-95825-7. URL h...

    2021 doi

  14. [22]

    J., Bowen, S

    Klotzbach, P. J., Bowen, S. G., Pielke, R., and Bell, M. Continental U . S . Hurricane Landfall Frequency and Associated Damage : Observations and Future Risks . Bulletin of the American Meteorological Society, 99 0 (7): 0 1359--1376, July 2018. ISSN 0003-0007, 1520-0477. doi:...

    2018 doi

  15. [23]

    R., Kruk, M

    Knapp, K. R., Kruk, M. C., Levinson, D. H., Diamond, H. J., and Neumann, C. J. The International Best Track Archive for Climate Stewardship ( IBTrACS ): Unifying Tropical Cyclone Data . Bulletin of the American Meteorological Society, 91 0 (3): 0 363--376, March 2010. ISSN 000...

    2010 doi

  16. [24]

    R., Diamond, H

    Knapp, K. R., Diamond, H. J., Kossin, J. P., Kruk, M. C., and Schreck, C. J. International Best Track Archive for Climate Stewardship ( IBTrACS ) Project , Version 4, North Atlantic , 2018

    2018

  17. [25]

    J., Chan, J

    Knutson, T., Camargo, S. J., Chan, J. C. L., Emanuel, K., Ho, C.-H., Kossin, J., Mohapatra, M., Satoh, M., Sugi, M., Walsh, K., and Wu, L. Tropical Cyclones and Climate Change Assessment : Part II : Projected Response to Anthropogenic Warming . Bulletin of the American Meteoro...

    2020 doi

  18. [26]

    P., and Hoyer, S

    Kochkov, D., Yuval, J., Langmore, I., Norgaard, P., Smith, J., Mooers, G., Klöwer, M., Lottes, J., Rasp, S., Düben, P., Hatfield, S., Battaglia, P., Sanchez-Gonzalez, A., Willson, M., Brenner, M. P., and Hoyer, S. Neural general circulation models for weather and climate. Natu...

    2024 doi

  19. [27]

    E., Horton, R

    Kopp, R. E., Horton, R. M., Little, C. M., Mitrovica, J. X., Oppenheimer, M., Rasmussen, D. J., Strauss, B. H., and Tebaldi, C. Probabilistic 21st and 22nd century sea-level projections at a global network of tide-gauge sites. Earth's Future, 2 0 (8): 0 383--406, 2014. ISSN 23...

    2014 doi

  20. [28]

    Learning skillful medium-range global weather forecasting

    Lam, R., Sanchez-Gonzalez, A., Willson, M., Wirnsberger, P., Fortunato, M., Alet, F., Ravuri, S., Ewalds, T., Eaton-Rosen, Z., Hu, W., Merose, A., Hoyer, S., Holland, G., Vinyals, O., Stott, J., Pritzel, A., Mohamed, S., and Battaglia, P. Learning skillful medium-range global ...

    2023 doi

  21. [29]

    L., Bensi, M

    Lee, J.-W., Irish, J. L., Bensi, M. T., and Marcy, D. C. Rapid prediction of peak storm surge from tropical cyclone track time series using machine learning. Coastal Engineering, 170: 0 104024, December 2021. ISSN 0378-3839. doi:10.1016/j.coastaleng.2021.104024. URL https://ww...

    2021

  22. [30]

    Neural network prediction of a storm surge

    Lee, T.-L. Neural network prediction of a storm surge. Ocean Engineering, 33 0 (3): 0 483--494, March 2006. ISSN 0029-8018. doi:10.1016/j.oceaneng.2005.04.012. URL https://www.sciencedirect.com/science/article/pii/S002980180500140X

    2006 doi

  23. [31]

    Berg, L., and Judi, D

    Lipari, S., Balaguru, K., Rice, J., Feng, S., Xu, W., K. Berg, L., and Judi, D. Amplified threat of tropical cyclones to US offshore wind energy in a changing climate. Communications Earth & Environment, 5 0 (1): 0 1--10, December 2024. ISSN 2662-4435. doi:10.1038/s43247-024-0...

    2024 doi

  24. [32]

    M., Horton, R

    Little, C. M., Horton, R. M., Kopp, R. E., Oppenheimer, M., Vecchi, G. A., and Villarini, G. Joint projections of US East Coast sea level and storm surge. Nature Climate Change, 5 0 (12): 0 1114--1120, December 2015. ISSN 1758-6798. doi:10.1038/nclimate2801. URL https://www.na...

    2015 doi

  25. [33]

    W., Lin, N., Oppenheimer, M., and Lai, C.-Y

    Lockwood, J. W., Lin, N., Oppenheimer, M., and Lai, C.-Y. Using Neural Networks to Predict Hurricane Storm Surge and to Assess the Sensitivity of Surge to Storm Characteristics . Journal of Geophysical Research: Atmospheres, 127 0 (24): 0 e2022JD037617, 2022. ISSN 2169-8996. d...

    2022 doi

  26. [34]

    A., Westerink, J

    Luettich, R. A., Westerink, J. J., and Scheffner, N. W. ADCIRC : an advanced three-dimensional circulation model for shelves, coasts, and estuaries. Report 1, Theory and methodology of ADCIRC - 2DD1 and ADCIRC - 3DL . 1992. Publisher: Coastal Engineering Research Center (US)

    1992

  27. [35]

    Luettich Jr., R. A. and Westerink, J. J. A solution for the vertical variation of stress, rather than velocity, in a three-dimensional circulation model. International Journal for Numerical Methods in Fluids, 12 0 (10): 0 911--928, 1991. ISSN 1097-0363. doi:10.1002/fld.1650121002

    1991 doi

  28. [36]

    W., Grineski, S

    Maldonado, A., Collins, T. W., Grineski, S. E., and Chakraborty, J. Exposure to Flood Hazards in Miami and Houston : Are Hispanic Immigrants at Greater Risk than Other Social Groups ? International Journal of Environmental Research and Public Health, 13 0 (8): 0 775, August 20...

    2016 doi

  29. [37]

    Martín Abadi , Ashish Agarwal , Paul Barham , Eugene Brevdo , Zhifeng Chen , Craig Citro , Greg S. Corrado , Andy Davis , Jeffrey Dean , Matthieu Devin , Sanjay Ghemawat , Ian Goodfellow , Andrew Harp , Geoffrey Irving , Michael Isard , Jia, Y., Rafal Jozefowicz , Lukasz Kaise...

    2015

  30. [38]

    Montgomery, M. C. and Chakraborty, J. Social Vulnerability to Coastal and Inland Flood Hazards : A Comparison of GIS - Based Spatial Interpolation Methods . International Journal of Applied Geospatial Research (IJAGR), 4 0 (3): 0 58--79, July 2013. ISSN 1947-9654. doi:10.4018/...

    2013 doi

  31. [39]

    Muis, S., Aerts, J. C. J. H., Á. Antolínez, J. A., Dullaart, J. C., Duong, T. M., Erikson, L., Haarsma, R. J., Apecechea, M. I., Mengel, M., Le Bars, D., O’Neill, A., Ranasinghe, R., Roberts, M. J., Verlaan, M., Ward, P. J., and Yan, K. Global Projections of Storm Surges Using...

    2023 doi

  32. [40]

    A Data - Driven Storm Surge Analysis for the U

    Needham, H. A Data - Driven Storm Surge Analysis for the U . S . Gulf Coast . Doctor of Philosophy , Louisiana State University and Agricultural and Mechanical College, March 2014. URL https://repository.lsu.edu/gradschool_dissertations/3250

    2014

  33. [41]

    E., Emanuel, K., Ravela, S., Ludwig, L., Kirshen, P., Bosma, K., and Martinich, J

    Neumann, J. E., Emanuel, K., Ravela, S., Ludwig, L., Kirshen, P., Bosma, K., and Martinich, J. Joint effects of storm surge and sea-level rise on US Coasts : new economic estimates of impacts, adaptation, and benefits of mitigation policy. Climatic Change, 129 0 (1): 0 337--34...

    2015 doi

  34. [42]

    Oliveira, M. M. F. d., Ebecken, N. F. F., Oliveira, J. L. F. d., and Santos, I. d. A. Neural Network Model to Predict a Storm Surge . Journal of Applied Meteorology and Climatology, 48 0 (1): 0 143--155, January 2009. ISSN 1558-8424, 1558-8432. doi:10.1175/2008JAMC1907.1. URL ...

    2009 doi

  35. [43]

    and Nash, R

    O'Shea, K. and Nash, R. An introduction to convolutional neural networks. arXiv preprint arXiv:1511.08458, 2015

    2015 arXiv

  36. [44]

    FourCastNet : A Global Data -driven High -resolution Weather Model using Adaptive Fourier Neural Operators , February 2022

    Pathak, J., Subramanian, S., Harrington, P., Raja, S., Chattopadhyay, A., Mardani, M., Kurth, T., Hall, D., Li, Z., Azizzadenesheli, K., Hassanzadeh, P., Kashinath, K., and Anandkumar, A. FourCastNet : A Global Data -driven High -resolution Weather Model using Adaptive Fourier...

    2022 arXiv

  37. [45]

    Flood- Related Work Disruption and Poor Health Outcomes Among University Students

    Peek-Asa, C., Ramirez, M., Young, T., and Cao, Y. Flood- Related Work Disruption and Poor Health Outcomes Among University Students . Prehospital and Disaster Medicine, 27 0 (6): 0 503--508, December 2012. ISSN 1049-023X, 1945-1938. doi:10.1017/S1049023X1200129X. URL https://w...

    2012 doi

  38. [46]

    R., El-Kadi, A., Masters, D., Ewalds, T., Stott, J., Mohamed, S., Battaglia, P., Lam, R., and Willson, M

    Price, I., Sanchez-Gonzalez, A., Alet, F., Andersson, T. R., El-Kadi, A., Masters, D., Ewalds, T., Stott, J., Mohamed, S., Battaglia, P., Lam, R., and Willson, M. GenCast : Diffusion -based ensemble forecasting for medium-range weather, May 2024. URL http://arxiv.org/abs/2312....

    2024 arXiv

  39. [47]

    J., Wirasaet, D., Roberts, K

    Pringle, W. J., Wirasaet, D., Roberts, K. J., and Westerink, J. J. Global storm tide modeling with ADCIRC v55: unstructured mesh design and performance. Geoscientific Model Development, 14 0 (2): 0 1125--1145, February 2021. ISSN 1991-959X. doi:10.5194/gmd-14-1125-2021. URL ht...

    2021 doi

  40. [48]

    Disparities of population exposed to flood hazards in the United States

    Qiang, Y. Disparities of population exposed to flood hazards in the United States . Journal of Environmental Management, 232: 0 295--304, February 2019. ISSN 0301-4797. doi:10.1016/j.jenvman.2018.11.039. URL https://www.sciencedirect.com/science/article/pii/S0301479718313057

    2019 doi

  41. [49]

    Rappaport, E. N. Fatalities in the United States from Atlantic Tropical Cyclones : New Data and Interpretation . Bulletin of the American Meteorological Society, 95 0 (3): 0 341--346, March 2014. ISSN 0003-0007, 1520-0477. doi:10.1175/BAMS-D-12-00074.1. URL https://journals.am...

    2014 doi

  42. [50]

    R., Balaguru, K., Staid, A., Xu, W., and Judi, D

    Rice, J. R., Balaguru, K., Staid, A., Xu, W., and Judi, D. Projected increases in tropical cyclone-induced U . S . electric power outage risk. Environmental Research Letters, 20 0 (3): 0 034030, February 2025. ISSN 1748-9326. doi:10.1088/1748-9326/adad85. URL https://dx.doi.or...

    2025 doi

  43. [51]

    and Van Banning, G

    Roelvink, J. and Van Banning, G. Design and development of DELFT3D and application to coastal morphodynamics. Oceanographic Literature Review, 11 0 (42): 0 925, 1995

    1995

  44. [52]

    Rupe, A., Vesselinov, V., and Crutchfield, J. P. Nonequilibrium statistical mechanics and optimal prediction of partially-observed complex systems. New Journal of Physics, May 2022

    2022

  45. [53]

    and Bhaskaran, P

    Sahoo, B. and Bhaskaran, P. K. Prediction of storm surge and inundation using climatological datasets for the Indian coast using soft computing techniques. Soft Computing, 23 0 (23): 0 12363--12383, December 2019. ISSN 1433-7479. doi:10.1007/s00500-019-03775-0. URL https://doi...

    2019 doi

  46. [54]

    Shchepetkin, A. F. and McWilliams, J. C. The regional oceanic modeling system ( ROMS ): a split-explicit, free-surface, topography-following-coordinate oceanic model. Ocean Modelling, 9 0 (4): 0 347--404, January 2005. ISSN 1463-5003. doi:10.1016/j.ocemod.2004.08.002. URL http...

    2005 doi

  47. [55]

    T., Noy, I., Wehner, M

    Smiley, K. T., Noy, I., Wehner, M. F., Frame, D., Sampson, C. C., and Wing, O. E. J. Social inequalities in climate change-attributed impacts of Hurricane Harvey . Nature Communications, 13 0 (1): 0 3418, August 2022. ISSN 2041-1723. doi:10.1038/s41467-022-31056-2. URL https:/...

    2022 doi

  48. [56]

    U., Grew, D., Chin, K., Kadetz, P., Greenough, P

    Stephens, K. U., Grew, D., Chin, K., Kadetz, P., Greenough, P. G., Burkle, F. M., Robinson, S. L., and Franklin, E. R. Excess mortality in the aftermath of Hurricane Katrina : a preliminary report. Disaster Medicine and Public Health Preparedness, 1 0 (1): 0 15--20, July 2007....

    2007 doi

  49. [57]

    H., Ziemlinski, R., Weiss, J

    Strauss, B. H., Ziemlinski, R., Weiss, J. L., and Overpeck, J. T. Tidally adjusted estimates of topographic vulnerability to sea level rise and flooding for the contiguous United States . Environmental Research Letters, 7 0 (1): 0 014033, March 2012. ISSN 1748-9326. doi:10.108...

    2012 doi

  50. [58]

    Forecast of storm surge by means of artificial neural network

    Sztobryn, M. Forecast of storm surge by means of artificial neural network. Journal of Sea Research, 49 0 (4): 0 317--322, June 2003. ISSN 1385-1101. doi:10.1016/S1385-1101(03)00024-8. URL https://www.sciencedirect.com/science/article/pii/S1385110103000248

    2003 doi

  51. [59]

    T., Schuerch, M., Wolff, C., Spencer, T., Merkens, J

    Vafeidis, A. T., Schuerch, M., Wolff, C., Spencer, T., Merkens, J. L., Hinkel, J., Lincke, D., Brown, S., and Nicholls, R. J. Water-level attenuation in global-scale assessments of exposure to coastal flooding: a sensitivity analysis. Natural Hazards and Earth System Sciences,...

    2019 doi

  52. [60]

    Flooded House or Underwater Mortgage ? The Macrofinancial Implications of Climate Change and Adaptation , March 2023

    Van der Straten, Y. Flooded House or Underwater Mortgage ? The Macrofinancial Implications of Climate Change and Adaptation , March 2023. URL https://papers.ssrn.com/abstract=4393731

    2023

  53. [61]

    Surge of Inequality : How Different Neighborhoods React to Flooding

    Varela Varela, A. Surge of Inequality : How Different Neighborhoods React to Flooding . SSRN Electronic Journal, 2023. ISSN 1556-5068. doi:10.2139/ssrn.4396481. URL https://www.ssrn.com/abstract=4396481

    2023 doi

  54. [62]

    Williams, L. L. and Lück-Vogel, M. Comparative assessment of the GIS based bathtub model and an enhanced bathtub model for coastal inundation. Journal of Coastal Conservation, 24 0 (2): 0 23, March 2020. ISSN 1874-7841. doi:10.1007/s11852-020-00735-x. URL https://doi.org/10.10...

    2020 doi

  55. [63]

    WMO Atlas of Mortality and Economic Losses from Weather , Climate and Water Extremes (1970–2019) , volume WMO-No

    World Meteorological Organization . WMO Atlas of Mortality and Economic Losses from Weather , Climate and Water Extremes (1970–2019) , volume WMO-No. 1267 of WMO . WMO, Geneva, 2021. ISBN 978-92-63-11267-5

    1970

  56. [64]

    Developing a deep learning-based storm surge forecasting model

    Xie, W., Xu, G., Zhang, H., and Dong, C. Developing a deep learning-based storm surge forecasting model. Ocean Modelling, 182: 0 102179, April 2023. ISSN 1463-5003. doi:10.1016/j.ocemod.2023.102179. URL https://www.sciencedirect.com/science/article/pii/S1463500323000203

    2023

  57. [65]

    Deep Learning Experiments for Tropical Cyclone Intensity Forecasts

    Xu, W., Balaguru, K., August, A., Lalo, N., Hodas, N., DeMaria, M., and Judi, D. Deep Learning Experiments for Tropical Cyclone Intensity Forecasts . Weather and Forecasting, 36 0 (4): 0 1453--1470, August 2021. ISSN 1520-0434, 0882-8156. doi:10.1175/WAF-D-20-0104.1. URL https...

    2021 doi

  58. [66]

    R., Rice, J., Leung, L

    Xu, W., Balaguru, K., Judi, D. R., Rice, J., Leung, L. R., and Lipari, S. A North Atlantic synthetic tropical cyclone track, intensity, and rainfall dataset. Scientific Data, 11 0 (1): 0 130, January 2024. ISSN 2052-4463. doi:10.1038/s41597-024-02952-7. URL https://www.nature....

    2024 doi

  59. [67]

    P., Avtar, R., Kraines, S., Yamamuro, M., Lindberg, F., and Grimmond, C

    Yunus, A. P., Avtar, R., Kraines, S., Yamamuro, M., Lindberg, F., and Grimmond, C. S. B. Uncertainties in Tidally Adjusted Estimates of Sea Level Rise Flooding ( Bathtub Model ) for the Greater London . Remote Sensing, 8 0 (5): 0 366, May 2016. ISSN 2072-4292. doi:10.3390/rs80...

    2016 doi

  60. [68]

    Flood hazards impact on neighborhood house prices: A spatial quantile regression analysis

    Zhang, L. Flood hazards impact on neighborhood house prices: A spatial quantile regression analysis. Regional Science and Urban Economics, 60: 0 12--19, September 2016. ISSN 0166-0462. doi:10.1016/j.regsciurbeco.2016.06.005. URL https://www.sciencedirect.com/science/article/pi...

    2016 doi

  61. [69]

    R., Camargo, S

    Chavas, D. R., Camargo, S. J., and Tippett, M. K. Tropical cyclone genesis potential using a ventilated potential intensity, April 2024. URL http://arxiv.org/abs/2404.01572. arXiv:2404.01572 [physics] version: 1

    2024 arXiv

  62. [70]

    An Estimate of the U

    Crowell, M., Coulton, K., Johnson, C., Westcott, J., Bellomo, D., Edelman, S., and Hirsch, E. An Estimate of the U . S . Population Living in 100- Year Coastal Flood Hazard Areas . Journal of Coastal Research, 262: 0 201--211, March 2010. ISSN 0749-0208, 1551-5036. doi:10.2112...

    2010 doi

  63. [71]

    Danielson, J. J. and Gesch, D. B. Global multi-resolution terrain elevation data 2010 ( GMTED2010 ). USGS Numbered Series 2011-1073, U.S. Geological Survey, 2011. URL http://pubs.er.usgs.gov/publication/ofr20111073. Code Number: 2011-1073 Code: Global multi-resolution terrain ...

    2010

  64. [72]

    C., Westerink, J

    Dietrich, J. C., Westerink, J. J., Kennedy, A. B., Smith, J. M., Jensen, R. E., Zijlema, M., Holthuijsen, L. H., Dawson, C., Luettich, R. A., Powell, M. D., Cardone, V. J., Cox, A. T., Stone, G. W., Pourtaheri, H., Hope, M. E., Tanaka, S., Westerink, L. G., Westerink, H. J., a...

    2008

  65. [73]

    A Statistical Deterministic Approach to Hurricane Risk Assessment

    Emanuel, K., Ravela, S., Vivant, E., and Risi, C. A Statistical Deterministic Approach to Hurricane Risk Assessment . Bulletin of the American Meteorological Society, 87 0 (3): 0 299--314, March 2006. ISSN 0003-0007, 1520-0477. doi:10.1175/BAMS-87-3-299. URL https://journals.a...

    2006 doi

  66. [74]

    An introductory review of deep learning for prediction models with big data

    Emmert-Streib, F., Yang, Z., Feng, H., Tripathi, S., and Dehmer, M. An introductory review of deep learning for prediction models with big data. Frontiers in Artificial Intelligence, 3: 0 4, 2020. Publisher: Frontiers Media SA

    2020

  67. [75]

    Final Coastal and Riverine High Water Mark Collection for Hurricane Katrina in Mississippi

    Group, U. Final Coastal and Riverine High Water Mark Collection for Hurricane Katrina in Mississippi . Technical report, March 2006 a . URL https://www.fema.gov/pdf/hazard/flood/recoverydata/katrina/katrina_ms_hwm_public.pdf

    2006

  68. [76]

    High Water Mark Collection for Hurricane Katrina in Alabama

    Group, U. High Water Mark Collection for Hurricane Katrina in Alabama . Technical report, April 2006 b . URL https://www.fema.gov/pdf/hazard/flood/recoverydata/katrina/katrina_ms_hwm_public.pdf

    2006

  69. [77]

    High Water Mark Collection for Hurricane Katrina in Louisiana

    Group, U. High Water Mark Collection for Hurricane Katrina in Louisiana . Technical report, March 2006 c . URL https://www.fema.gov/pdf/hazard/flood/recoverydata/katrina/katrina_ms_hwm_public.pdf

    2006

  70. [78]

    R., Satoh, M., and Takagi, H

    Islam, M. R., Satoh, M., and Takagi, H. Tropical Cyclones Affecting Japan Central Coast and Changing Storm Surge Hazard since 1980. Journal of the Meteorological Society of Japan. Ser. II, 100 0 (3): 0 493--507, 2022. doi:10.2151/jmsj.2022-024

    1980 doi

  71. [79]

    R., Neumann, C

    Jarvinen, B. R., Neumann, C. J., and Davis, M. A. S. A tropical cyclone data tape for the North Atlantic basin, 1886-1983 : contents, limitations, and uses. 1984. URL https://repository.library.noaa.gov/view/noaa/7069/

    1983

  72. [80]

    R., Balaguru, K., Xu, W., Mapes, B., and Soden, B

    Kelly, P., Leung, L. R., Balaguru, K., Xu, W., Mapes, B., and Soden, B. Shape of Atlantic Tropical Cyclone Tracks and the Indian Monsoon . Geophysical Research Letters, 45 0 (19): 0 10,746--10,755, 2018. ISSN 1944-8007. doi:10.1029/2018GL080098. URL https://onlinelibrary.wiley...

    2018 doi

  73. [81]

    Kingma, D. P. and Ba, J. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014

    2014 arXiv

  74. [82]

    Deep learning

    LeCun, Y., Bengio, Y., and Hinton, G. Deep learning. Nature, 521 0 (7553): 0 436--444, May 2015. ISSN 1476-4687. doi:10.1038/nature14539. URL https://www.nature.com/articles/nature14539. Bandiera\_abtest: a Cg\_type: Nature Research Journals Number: 7553 Primary\_atype: Review...

    2015 doi

  75. [83]

    Marks, D. G. The Beta and advection model for hurricane track forecasting. 1992. URL https://repository.library.noaa.gov/view/noaa/7184

    1992

  76. [84]

    and Wang, B

    Murakami, H. and Wang, B. Patterns and frequency of projected future tropical cyclone genesis are governed by dynamic effects. Communications Earth & Environment, 3 0 (1): 0 1--10, April 2022. ISSN 2662-4435. doi:10.1038/s43247-022-00410-z. URL https://www.nature.com/articles/...

    2022 doi

  77. [85]

    E., Darling, R

    Willoughby, H. E., Darling, R. W. R., and Rahn, M. E. Parametric Representation of the Primary Hurricane Vortex . Part II : A New Family of Sectionally Continuous Profiles . Monthly Weather Review, 134, April 2006. doi:10.1175/MWR3106.1. URL https://journals.ametsoc.org/view/j...

    2006 doi

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.