Retrieval of Aerosol optical depth using machine learning algorithm
Project Description

Aerosol optical depth (AOD) is an important parameter for describing the aerosol impact on solar radiation. Due to the passing time of satellite and cloud effect, current AOD product has the issue of low time resolution (~1 day) and spatial incompleteness. In this project, we plan to combine machine learning method, ground observation data and numerical model simulation results to generate a high temporal resolution AOD product over the Pearl River Delta (PRD) region.

Supervisor
FUNG Jimmy Chi Hung
Co-Supervisor
FUNG Jimmy Chi Hung
LU, Xingcheng
Quota
4
Course type
UROP1000
UROP1100
UROP2100
UROP3100
UROP4100
Applicant's Roles

The applicants are expected to finish subjects in basic calculus and statistics, he/she should have a good programming skills. He/she will help the supervisor to conduct the numerical simulation part of the project.

Applicant's Learning Objectives

Conclude the fundamental principles of AI and air quality.

Complexity of the project
Moderate