Felix M. Strnad

Felix M. Strnad is a researcher at the University of Tübingen, Germany, affiliated with both the Machine Learning in Climate Science and Machine Learning in Sustainable Energy Systems research groups. As corresponding author of recent publications, he demonstrates active involvement in cutting-edge climate research that bridges machine learning methodologies with atmospheric science.

Dr. Strnad's research focuses on extreme rainfall events, monsoon system interactions, and intraseasonal climate variability. His work particularly examines synchronization patterns between the Indian Summer Monsoon and West African Monsoon systems using climate network approaches. He applies machine learning techniques to analyze extreme weather events and uncover complex atmospheric teleconnections that operate on intraseasonal timescales.

His recent publication on intraseasonal synchronization of extreme rainfall between North India and the Sahel demonstrates expertise in analyzing how large-scale climate modes like the Boreal Summer Intraseasonal Oscillation (BSISO), Tropical Easterly Jet (TEJ), and ENSO interact to influence extreme rainfall patterns across continents. His methodological approach combines climate network theory with traditional meteorological analysis to reveal time-lagged connections between distant monsoon systems.

Dr. Strnad's research contributes to understanding how climate variability operates across spatial scales, with implications for improving extreme weather prediction in vulnerable regions. His work bridges computational approaches with physical climate science to address fundamental questions about atmospheric dynamics and monsoon interactions.