TREND 2022 end-of-summer conference poster gallery and booklet.
Project type: Computation
How do we forecast future state accurately? How do we know forecast will be reliable? Data assimilation addressed these questions by combining computational model for forecast and observations for representation of current state using probability.
In this project, TREND participants will apply machine learning/artificial intelligence algorithms to better understand unknown nature of observations and investigate potential for improving forecast.
The project offers hands-on experience with real satellite observations data, simulation of data using computational model as well as machine learning/artificial intelligence algorithms. The students will gain insights real atmospheric phenomena and what makes daily weather forecast accurate.
Check out previous alumni and projects mentored by Ide below. You can access the table and sort by Mentor here.
Want help getting in touch with this mentor? Reach out to Daniel.