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Rivers in the Sky: A Down-to-Earth Analogy in Hydrometeorology

20241211
  • Date

    11 Dec 2024

  • Organiser

    Department of Land Surveying & Geo-Informatics

  • Time

    16:30 - 17:30

  • Venue

    Z414 Map  

Speaker

Dr Ruping MO

Enquiry

Mr Jimmy Kwan 27664350 jimmy.lh.kwan@polyu.edu.hk

Remarks

Moderator: Prof. George LIU, LSGI

Summary

Atmospheric rivers are long, narrow, and transient corridors of strong horizontal water vapor transport, typically associated with a low-level jet stream ahead of the cold front of an extratropical cyclone. This presentation demonstrates how to detect atmospheric rivers and assess their hydrometeorological impacts within research and operational forecasting environments. It also illustrates a multi-decade global atmospheric river catalogue for historical high-impact weather analysis.

POSTER

Keynote Speaker

Dr Ruping MO

Manager and Senior Research Scientist
Environment and Climate Change Canada

Dr. Ruping Mo is the manager and a senior research scientist of the National Laboratory West, Environment and Climate Change Canada. He completed his B.Sc. in Meteorology at Sun Yat-sen University in 1983, his M.Sc. in Atmospheric Sciences at the Chinese Academy of Meteorological Sciences in 1986, and his Ph.D. in Applied Mathematics at the University of Cambridge in 1994. Dr. Mo was a Postdoctoral Fellow at McGill University during 1995-1998, and a Research Scientist at the Center for Ocean-Land-Atmosphere Studies during 1998-1999, working on dynamical-statistical seasonal predictions. Dr. Mo’s current research projects focus on improving scientific understanding and prediction of high-impact weather in coastal and mountainous environments. These studies involve a combination of the analysis of atmospheric and oceanographic data with numerical modeling. Dr. Mo has developed several user-friendly programs to facilitate technology transfer of scientific results, especially advances in high-resolution NWP modeling and atmospheric river analysis, into operational weather forecasting.

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