I got my PhD degree in Atmospheric Science from Stony Brook University. During that period, my research topic is to analyze climate data, understand the climate impacts of cloud processes, and represent them using statistics and computer models.
Cloud has great impacts on our climate system. Cloud amount and vertical distribution play a significant role in changing the solar energy incoming to the Earth surface. However, representing cloud is still a great challenge. Nowadays more and more statistical techniques and methods are introduced to describe cloud processes, especially the unresolved cloud subgrid characteristics. My PhD advisor and I finished a paper describing a cloud inhomogeneity relationship with model resolution and atmospheric instability based on realistic observed data. This scale-aware parameterization is able to adjust the microphysics inhomogeneity parameter automatically when climate model resolution needs to be changed. The math tools developed by statisticians and computer scientists can be introduced to the climate modeling field.
My PhD research interests focus on:
I am also interested in data science field (see my Data Science page). Statistical learning provides us another way of doing prediction when the target system is not well understood or too uncertain to build a physical model (think about stock market, pattern recognition, and so on). We may construct statistical model and feed it with lots of data and let it automaticaly learn the pattern inherent in the data. The limitation of statistical model is that we are usually lack of understanding how the internal system works but this can be also seen as an advantage, no need to look into it too deep. For a lot of applications, statistical learning model gives us very satisfactory results and that has already made our life much easier.