Estimation of Planetary Boundary Layer Height From Lidar by Combining Gradient Method and Machine Learning Algorithms
Author:
Affiliation:
1. School of Electronic Information, Wuhan University, Wuhan, China
2. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan, China
Funder
National Natural Science Foundation of China
Fundamental Research Funds for the Central Universities
Natural Science Fund of Hubei Province
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Earth and Planetary Sciences,Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/36/10006360/10304166.pdf?arnumber=10304166
Reference73 articles.
1. Climatology of the planetary boundary layer over the continental United States and Europe
2. Retrieving the vertical distribution of PM2.5 mass concentration from LiDAR via a random forest model;zhu;IEEE Trans Geosci Remote Sens,2021
3. Observed Diurnal Cycle Climatology of Planetary Boundary Layer Height
4. Improved Random Forest for Classification
5. Surface-based remote sensing of the mixing-layer height a review
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1. Deep-learning-derived planetary boundary layer height from conventional meteorological measurements;Atmospheric Chemistry and Physics;2024-06-04
2. Estimation of Boundary Layer Height From Radar Wind Profiler by Deep Learning Algorithms;IEEE Transactions on Geoscience and Remote Sensing;2024
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