Raw Data Library
About
Aims and ScopeAdvisory Board Members
More
Who We Are?
User Guide
Green Science
​
​
EN
Sign inGet started
​
​

About
Aims and ScopeAdvisory Board Members
More
Who We Are?
User GuideGreen Science

Language

Sign inGet started
RDL logo

Verified research datasets. Instant access. Built for collaboration.

Navigation

About

Aims and Scope

Advisory Board Members

More

Who We Are?

Add Raw Data

User Guide

Legal

Privacy Policy

Terms of Service

Support

Got an issue? Email us directly.

Email: info@rawdatalibrary.netOpen Mail App
​
​

© 2025 Raw Data Library. All rights reserved.
PrivacyTerms
  1. Raw Data Library
  2. /
  3. Publications
  4. /
  5. Analysis of Nonstationary Typhoon Winds Based on Optimal Time-Varying Mean Wind Speed

Verified authors • Institutional access • DOI aware
50,000+ researchers120,000+ datasets90% satisfaction
Article
English
2022

Analysis of Nonstationary Typhoon Winds Based on Optimal Time-Varying Mean Wind Speed

0 Datasets

0 Files

English
2022
Journal of Structural Engineering
Vol 148 (12)
DOI: 10.1061/(asce)st.1943-541x.0003490

Get instant academic access to this publication’s datasets.

Create free accountHow it works

Frequently asked questions

Is access really free for academics and students?

Yes. After verification, you can browse and download datasets at no cost. Some premium assets may require author approval.

How is my data protected?

Files are stored on encrypted storage. Access is restricted to verified users and all downloads are logged.

Can I request additional materials?

Yes, message the author after sign-up to request supplementary files or replication code.

Advance your research today

Join 50,000+ researchers worldwide. Get instant access to peer-reviewed datasets, advanced analytics, and global collaboration tools.

Get free academic accessLearn more
✓ Immediate verification • ✓ Free institutional access • ✓ Global collaboration
Access Research Data

Join our academic network to download verified datasets and collaborate with researchers worldwide.

Get Free Access
Institutional SSO
Secure
This PDF is not available in different languages.
No localized PDFs are currently available.
Kang Cai
Kang Cai

Institution not specified

Verified
Kang Cai
Mingfeng Huang
Haiwei Xu
+1 more

Abstract

The time-varying mean (TVM) component has a major influence on the characterization of nonstationary wind events and associated parameters of the fluctuating components. However, it is challenging to determine the optimal TVM of the nonstationary wind speed accurately due to variability in the low-frequency signal contents. To address this challenge, this study develops an effective wavelet-based method together with necessary conditions to calculate the optimal TVM from nonstationary wind speed data. For comparison, the time-dependent memory method is also used and enhanced to obtain the optimal TVM. Wind field turbulence characteristics of Typhoon Mangkhut are then analyzed and presented based on the conventional stationary model and nonstationary model with the proposed optimal TVM. In addition, this study also proposes an empirical description of the wind spectrum, which provides a better fit to the typhoon-related nonstationary winds than those offered by classical wind spectral description.

How to cite this publication

Kang Cai, Mingfeng Huang, Haiwei Xu, Ahsan Kareem (2022). Analysis of Nonstationary Typhoon Winds Based on Optimal Time-Varying Mean Wind Speed. Journal of Structural Engineering, 148(12), DOI: 10.1061/(asce)st.1943-541x.0003490.

Related publications

Why join Raw Data Library?

Quality

Datasets shared by verified academics with rich metadata and previews.

Control

Authors choose access levels; downloads are logged for transparency.

Free for Academia

Students and faculty get instant access after verification.

Publication Details

Type

Article

Year

2022

Authors

4

Datasets

0

Total Files

0

Language

English

Journal

Journal of Structural Engineering

DOI

10.1061/(asce)st.1943-541x.0003490

Join Research Community

Access datasets from 50,000+ researchers worldwide with institutional verification.

Get Free Access