Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 325

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Risk assessment of hurricane storm surge for New York City

Open access
Lin, N., Emanuel, K. A., Smith, J. A., Vanmarcke, E.·2010·Journal of Geophysical Research: Atmospheres

Abstract

Hurricane storm surge presents a major hazard for the United States. We apply a model‐based risk assessment methodology to investigate hurricane storm surge risk for New York City (NYC). We couple a statistical/deterministic hurricane model with the hydrodynamic model SLOSH (sea, lake, and overland surges from hurricanes) to generate a large number of synthetic surge events; the SLOSH model simulations are compared to advanced circulation model simulations. Statistical analysis is carried out on the empirical data. It is observed that the probability distribution of hurricane surge heights at the Battery, NYC, exhibited a heavy tail, which essentially determines the risk of New York City being struck by a catastrophic coastal flood event. The peaks‐over‐threshold method with the generalized Pareto distribution is applied to estimate the upper tail of the surge heights. The resulting return periods of surge heights are consistent with those of other studies for the New York area. This storm surge risk assessment methodology may be applied to other coastal areas and can be extended to consider the effect of future climate change.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1029/2009jd013630

Cites

1 of 1 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Metadata

Authors
Lin, N., Emanuel, K. A., Smith, J. A., & Vanmarcke, E.
Venue
Journal of Geophysical Research: Atmospheres
Published
2010-09-25
Volume
115
Issue
D18
ISSN
0148-0227 (ISSN-L)
OpenAlex
W2170692035
Cited by
325
References
1
FWCI
6.79
Type
journal article
Language
en
Open access
bronze

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Risk assessment of hurricane storm surge for New York City. https://app.cochat.ai/papers/10.1029/2009jd013630
The paper
Lin, N., Emanuel, K. A., Smith, J. A., & Vanmarcke, E. (2010). Risk assessment of hurricane storm surge for New York City. Journal of Geophysical Research: Atmospheres. https://doi.org/10.1029/2009jd013630

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.