Precipitation play a role in scavenging the aerosol in the atmosphere down to the ground. The scavenging process is separated into in-cloud rainout and below-cloud washout (BCW). Currently, there is a large discrepancy between field observation and laboratory experiment for the BCW coefficient. Hence, it is necessary to further work on this coefficient in order to get a more reliable PM2.5 simulation during the rainy season. In this work, we propose to calculate the BCW coefficient by using the PM2.5 species observation data at HKUST super-site. We want to parameterize the BCW coefficient based on the rain intensity by using the regression method. Lastly, we will implement the results into the chemical transport model and improve the BCW module in the model.
UROP1000 UROP1100 UROP2100
We have 7-year PM species observation data (e.g. sulfate, nitrate and ammonium) at the HKUST super-site. The student is expected to apply linear regression on these data to estimate the BCW coefficient and find out whether this coefficient is correlated to the rain intensity. The candidate need to have the basic knowledge of calculus and linear regression. Programming skill is desirable, but not necessary.
Applicant's Learning Objectives:
By completing this project, students would have basic understand of weather forecasting and its uncertainty.