Yukari Takayabu is Emeritus Professor of the University of Tokyo (UT). Her research interests extend from tropical meteorology (Convectively-Coupled Equatorial Waves, MJO and ENSO) to global climate and precipitation. Her career started at National Institute for Environmental Studies and continued at UT from 2000, served as Professor at CCSR and AORI/UT for 2007-2024, and as Vice Director of AORI for 2019-2023. She contributed as the leader of JAXA’s Science Team for TRMM and GPM satellite missions for 2010-2025, and herself put emphasis on science with space-borne precipitation radar observations. She led the Spectral Latent Heating algorithm group to publish four-dimensional atmospheric convective latent heating data. She also focused on global extreme rainfalls and their environments. Yukari received the Society’s Award and the Fujiwara Award from the Japan Meteorological Society, Saruhashi Award, a distinguished women scientist award, AMS Fellow 2021, and the Joanne Simpson Tropical Meteorology Research Award from the AMS.
In various regions globally, we are now threatened by increase of extreme rainfalls with the climate change. From worldwide observational studies, IPCC AR6 states that ‘the frequency and intensity of heavy precipitation events have increased since the 1950s over most land area for which observational data are sufficient for trend analysis.’ However, we still do not clearly understand the mechanism or the degree of heavy precipitation increase with the climate change.
From the end of 1990s, space-borne precipitation radars, the TRMM Precipitation Radar, and the GPM Dual-frequency Precipitation Radar were sequentially launched. A huge amount of 3D precipitation data over most of the globe has been collected over 28 years.
Utilizing these 3D observations of precipitation, we study structures of precipitation systems of heavy precipitation and related them to large-scale environments both globally and around-Japan region. As a result, it is shown that there are two different types of warm-season extreme precipitation associated with distinct systems. One is intense rainfall associated with very deep convection which is linked to Convective Available Potential Energy (CAPE), while another is intense rainfall embedded in mesoscale convective systems (MCSs), organized in very moist environment and often associated with moist absolutely unstable layer (MAUL) with large precipitable water. Therefore, extreme precipitation can increase with the moisture increase, even if the atmosphere becomes more stable in terms of CAPE.
With a large global collection of precipitation events, or contiguous precipitation volumes observed from space-borne precipitation radars, a well-designed and well-trained Neural Network model can well retrieve characteristics of major precipitation events in each region from its local environmental variables.