June Choi - Investigating Large precipitation systems with GPM
Overview: Understanding how the behavior of large-scale precipitating systems is impacted by atmospheric variables can yield important insights about subseasonal variability and predictability across different regions. For this project, we explored the relationship between atmospheric variables and the characteristics of large precipitating systems, such as their size, geographic distribution, volume of rain, and maximum height of the system.
Mentor: Chuntao Liu
2022 GPM Mentorship Program
June Choi - Investigating Large precipitation systems with GPM
Participant Name: June Choi
Project: Investigating Large Precipitation Systems with GPM
Affiliation: Stanford University, USA
Current role: PhD student
Mentor: Chuntao Liu
Download Project Overview Highlights (.pptx)
Download Final Presentation (.pdf)
Why are you participating in this program?
I wanted to become familiarized with GPM data products, and hear directly from experts about their potential research applications.
Tell us about your project.
Precipitation helps link Earth’s water and energy cycles, moving tremendous amounts of water and energy through Earth’s atmosphere. In particular, understanding how the behavior of large-scale precipitating systems is impacted by atmospheric variables can yield important insights about subseasonal variability and predictability across different regions. For this project, we explored the relationship between atmospheric variables and the characteristics of large precipitating systems, such as their size, geographic distribution, volume of rain, and maximum height of the system. We utilized the GPM large systems dataset provided by Texas A&M-CC for precipitation features greater than 2500km2 over the time period 2014-2020, and variables from ERA5 such as the convective available potential energy (e.g., CAPE, an indication of the stability of the atmosphere and can be used to assess the potential for the development of convection) and total precipitation (TP). As expected, the majority of large precipitating systems were centered around Southeast Asia and along the ITCZ. Within Southeast Asia, systems with above median height, CAPE and rain volume appeared most frequently along the western coast of Kalimantan and Sumatra islands. Next steps for the project will involve investigating additional ERA5 variables and training an AI model that relates the formation of large-scale systems to relevant variables. This model may then be applied to CMIP6 results to forecast large system formation in future climate change scenarios. This type of research can help us understand shifting regional precipitation patterns, providing important inputs for researchers aiming to quantify precipitation-related risks associated with climate change.
What communities or organizations may benefit from your case study project?
Any region facing shifting precipitation patterns may potentially benefit from this research, especially communities aiming to quantify precipitation-related risks associated with climate change.
What is something surprising that you have learned about the GPM mission, the data, or applying GPM data for applications?
Even as satellite measurements improve, with increasing temporal and spatial resolution, translating to precipitation patterns on the ground remains challenging.
What is a challenge you faced with using remote sensing data? Any lessons learned that helped overcome this barrier?
Ground validation is essential for testing the algorithms that translate remote sensed data to precipitation.
How do you plan to use GPM in the future?
I plan to continue researching the interaction between atmospheric variables, climate change, and the spatial scale of precipitation systems.