🌦 Numerical Weather Prediction with Machine Learning/Artificial Intelligence

Kayo Ide

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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.

Alumni/Previous Projects

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