Using Machine Learning to Predict Urban Canopy Flows for Land Surface Modeling-Reference-Cited by-同舟云学术

Using Machine Learning to Predict Urban Canopy Flows for Land Surface Modeling

Published:2023-01-07 Issue:1 Volume:50 Page:
ISSN:0094-8276
Container-title:Geophysical Research Letters
language:en
Short-container-title:Geophysical Research Letters

Author:

Lu Yanle¹,Zhou Xu‐Hui²,Xiao Heng²^ORCID,Li Qi¹^ORCID

Affiliation:

1. School of Civil and Environmental Engineering Cornell University Ithaca NY USA

2. Kevin T. Crofton Department of Aerospace and Ocean Engineering Virginia Tech Blacksburg VA USA

Funder

National Science Foundation

National Center for Atmospheric Research

Publisher

American Geophysical Union (AGU)

Subject

General Earth and Planetary Sciences,Geophysics

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1029/2022GL102313

Reference49 articles.

1. Dispersive Fluxes Within and Over a Real Urban Canopy: A Large-Eddy Simulation Study

2. Effects of high rise building complex on the wind flow patterns on surrounding urban pockets;Bharat A.;International Journal of Engineering Research and Development,2012

3. A scale-dependent Lagrangian dynamic model for large eddy simulation of complex turbulent flows

4. CFD modelling: The most useful tool for developing mesoscale urban canopy parameterizations

5. Predicting clustered weather patterns: A test case for applications of convolutional neural networks to spatio-temporal climate data

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