“Meteorological Data Science” at the Institute of Meteorology and Climate Research (IMK) at KIT is using statistical methods and machine learning to improve weather forecasting skills. The interdisciplinary collaboration in the framework of the KIT Center MathSEE brings together experts in statistical modeling and meteorology.
Further information:
KIT Center MathSEE
https://www.mathsee.kit.edu/index.php
Method Area 4: Stochastic modeling, statistical data analysis and forecasting
https://www.mathsee.kit.edu/1106.php
Amidst the dance of clouds and the whispers of the winds lies the unseen orchestrator of nature’s grand symphony, the Institute of Meteorology and Climate Research at the Karlsruhe Institute of Technology. As we embark on a journey to unravel the secrets of the skies and decode the language of the elements,
Join us in this exploration of the fascinating world where science meets the sublime through mathematics. Welcome to Method Area 4 of MathSEE: Stochastic modeling, statistical data analysis and forecasting. My name is Julian Quinting. I’m Junior Research Group Leader here at the Institute of Meterology and Climate Research at KIT. And we basically aim
To understand the atmosphere and how we can improve weather predictions on longer timescales than ten days. So what is really important for us is to bring together the domain knowledge in atmospheric science, then with a mathematical method to actually improve our forecasting systems. I’m Fabian. I’m a Ph.D. student
in the MathSEE program. So basically I work in two different working groups connecting the mathematical methods, basically ensemble model output statistics or also later on, machine learning with the metrological forecasts. So this is quite an important interconnection to improve the forecasts. My name is Siyu Li. I’m a doctiral researcher at
The Institute of Meteorology and Climate Research. I’m connected to the MathSEE through the KIT Graduate School Computational and Data Science, where young researchers get together to discuss their inner disciplinary science. If you are a mathematician or a scientist who looks for mathematical approaches, we invite you to share your research challenges
And join us as part of the KIT Center MathSEE.