Papers
11
Total Citations
319
H-Index
8
About
Daisuke Goto is a leading figure in aerosol data assimilation and atmospheric modeling, whose work has fundamentally advanced our ability to observe and predict aerosol distributions from regional to global scales. His research centers on integrating high-frequency satellite observations—particularly from geostationary platforms like Himawari-8 and lidar instruments like CALIPSO—into cutting-edge ensemble Kalman filter systems. Goto’s most influential contributions include pioneering the assimilation of hourly geostationary aerosol optical thickness (AOT) data using the four-dimensional local ensemble transform Kalman filter, a breakthrough that captures the rapid spatiotemporal evolution of aerosols. His work has also demonstrated the critical importance of assimilating vertical aerosol profiles and has improved dust cycle simulations by coupling the NICAM model with aerosol transport schemes. With over 300 total citations, his top-cited papers (2014–2024) have reshaped how models represent aerosol optical properties and radiative effects, particularly over East Asia and the Tibetan Plateau. Notably, Goto has also explored innovative applications of shape memory gel in robotics, showcasing his versatility. His research remains essential for improving air quality forecasts and understanding aerosol-climate interactions.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4
- 5
- 6
- 7
- 8
- 9Experiments of a variable stiffness robot using shape memory gel8 citations · 2013
- 10