Retrival of Aerosol optical depth using machine learning algorithm
FUNG Jimmy Chi Hung
FUNG Jimmy Chi Hung, LU Xingcheng
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.
UROP1000 UROP1100 UROP2100
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.