Dr. Or Hadas

Dr. Or Hadas
Israeli Postdoctoral Scholar
2026-2027 Cohort
Princeton
Department of Geosciences
  • The Fueglistaler Research Group
  • Prof. Stephan A. Fueglistaler Lab website

Ever since joining a competitive sailing team at age 10 and experiencing winds and currents firsthand, Or Hadas has been fascinated by atmospheric dynamics. In high school, he was accepted into the highly selective Israeli Presidential Program for Future Scientists, earning a B.Sc. in physics at just 19. He continued his studies during his service in the Israeli air force, where he later worked as a meteorologist.

His MSc research in Atmospheric Dynamics in the Department of Earth and Planetary Sciences at the Weizmann Institute led to a publication in a prestigious journal addressing the “midwinter storm track minimum” paradox over the Pacific Ocean, which had puzzled atmospheric scientists since its discovery in 1992. By tracking thousands of cyclones, Dr. Hadas developed a novel theory that resolved the paradox. This work earned him the Azrieli Fellowship, a highly competitive stipend for PhD students.

Dr. Hadas completed his PhD in the same department at the Weizmann Institute, where he studied the dynamics of midlatitude storms—the primary drivers of weather and extreme events for billions of people worldwide. His work bridged two traditionally separate perspectives: the Eulerian approach, which examines climate through regional averages, and the Lagrangian approach, which follows individual weather events. The theoretical frameworks and tools he developed offer new ways to analyze how climate influences storm dynamics.

In his postdoctoral research in the Department of Geosciences at Princeton University, Dr. Hadas focuses on clouds, the largest source of uncertainty in weather and climate predictions. Clouds strongly influence Earth’s radiation balance but occur on spatial scales too small to be explicitly resolved (observed) in most models. He aims to quantify how much information about hidden variables—such as turbulence, clouds, and topographic effects—can be inferred from observable data, with the goal of improving climate models.