How to understand Earth science system using data science
- February 25 (Fri) at 16:00 - 18:00, 2022 (JST)
- Dr. Kaman Kong (Postdoctoral Researcher, Computational Climate Science Research Team, RIKEN Center for Computational Science (R-CCS))
- via Zoom
Hi everyone, my name is Kaman Kong. After I graduated from Nagoya University last April, I joined the computational climate science research team, R-CCS at Kobe. Although I have still not yet had the important results now, I would like to share my idea and future plan here.
In this talk, different from the previous seminar, I would like to highlight how to use data science approaches to understand our Earth system science. In the first 60 minutes, I would like to share my research experiences in ecosystems, dust outbreaks, and atmospheric sciences and try to discuss their limitation in my study. After a 10-minute break, the 30 minutes will be spent discussing the potential methodology to overcome these limitations and new opportunities and challenges in Earth system science.
In the first 60 minutes, I would like to talk about the relationships among ecosystems, dust outbreaks, and atmospheric conditions. I used the models of dust and ecosystem to explore seasonal variations of threshold wind speed, an index of soil susceptibility to dust outbreak, and its relations with land surface conditions, such as plant growth and soil moisture and temperature changes, in the Mongolian grasslands. On the other side, I am improving the weather forecast model to accurately predict dust emission and discuss its effects on the Earth system. Meanwhile, I am integrating the dust model into the ecosystem model. During this period, I realized there are many uncertainties of simulation.
In the second 30 minutes, I will explain these limitations as I mentioned before and try to discuss how to solve these problems. For example, using deep learning to identify the green and brown plants separately for discussing their different effect on the dust model. And, used data assimilation (e.g., EnKF and Bayesian calibration) to improve the simulated performance of land surface parameters (e.g., soil moisture and vegetation).
*If you would like to participate, please contact Keita Mikami.