Organization: University of Maryland
Personal Biography: Upon completing the B.S. degree in theoretical physics from the Kyoto University in 2000, Dr. Takemasa Miyoshi started his professional career as a civil servant at the Japanese Meteorological Agency (JMA). After two years of administrative work at the Planning Division, Dr. Miyoshi started working on numerical weather prediction (NWP) at the Numerical Prediction Division and developed the three-dimensional variational (3D-Var) data assimilation system from scratch for the operational nonhydrostatic regional NWP model. In 2003, Dr. Miyoshi received the Japanese government fellowship to study at the University of Maryland (UMD), and completed both M.S. and Ph.D. degrees on ensemble data assimilation within two years. Many studies have been published using the experimental system that Dr. Miyoshi developed for his dissertation. In 2005, Dr. Miyoshi moved back to JMA and was in charge of developing the JMA’s next generation global/regional ensemble data assimilation systems. During the four years at JMA, Dr. Miyoshi came to be recognized as a leading scientist in the field of data assimilation; he was asked to give invited talks at several international conferences and to be a member of the organizing committee of the World Meteorological Organization’s data assimilation symposium in Melbourne, the most prestigious conference in the field. In 2008, Dr. Miyoshi received the Yamamoto-Syono Award from the Japanese Meteorological Society. In 2009, Dr. Miyoshi moved to UMD and has been working towards his goals of advancing the science of data assimilation as well as a deep commitment to education.
Lecture Topic: Numerical Weather Prediction – Chaos and Predictability
Doesn’t it sound exciting to simulate the actual weather with a computer and to make a forecast? Numerical Weather Prediction (NWP) plays an essential role in actual weather forecasting and warning activities at Weather Service offices around the world. We enjoy the current advanced weather forecasts due to technological advancement ranging from telecommunications and high-performance computing to weather observations including satellites, as well as due to scientific advancement of atmospheric dynamics and physics. A number of earth-observing satellites have been launched in the past few decades, and frontier research has been seeking the wisest way of using the new observations. In spite of the consistent improvement, we sometimes find weather forecasts unreliable. Why can’t we predict the weather perfectly although we can predict ocean tides almost perfectly? Is there a limit of predictability?
This lecture will introduce the fundamental ideas of NWP and present some state-of-the-art NWP results. We will also discuss predictability limit and the future of weather forecasting.
Directed Study Topic: Weather Forecasting
In this 3-day directed study, groups of 2-3 students will make their own weather forecasts at the camp and compete. Past observation records, weather maps, and other weather data will be provided. Students can use any of these data in any reasonable way to make their own forecasts. Day 1: following introduction, students will go out to make weather observations such as winds, temperature, humidity, precipitation, cloud cover, and cloud types. Day 2: following discussions about possible forecasting methods, students will make their own forecasts for the next day. The forecast category will be weather type (sunny, overcast, rain), winds, temperature, humidity, and precipitation amount (24-hour accumulation). Day 3: following students presentations on their forecasts, students will go out to observe and compare the accuracy of the forecasts.