Study of advanced data assimilation and cloud resolving ensemble technique
Description: To overcome the difficulties to predict local convective rains without strong synoptic or orographic forcing, research and development on mesoscale data assimilation and ensemble forecast were conducted.A storm scale 4DVAR data assimilation scheme, local ensemble transform Kalman filter for the JMA nonhydrostatic model, an ensemble variational data assimilation scheme were developed and applied to data assimilation experiments for local heavy rainfall events and typhoons. Mesoscale assimilation experiments for mesoscale observation data (e.g., GPS data and Doppler lidar data) were conducted. Initial and lateral boundary perturbation methods for a mesosocale ensemble prediction system were developed and the system was applied to a cloud resolving model. Feasibility of the probabilistic forecast of local heavy rainfall was shown through validation of mesoscale ensemble forecasts.
Amount: ¥40,170,000
Funding type: research
Funder scheme: Grant-in-Aid for Scientific Research (A)
Start date: 2009-04-01
End date: 2014-03-31
Investigator: Kazuo SAITO (lead)
Institutions: Meteorological Research Institute, Japan, Meteorological Research Institute
Subfield: Atmospheric Science
Field: Earth and Planetary Sciences
Domain: Physical Sciences
Funded outputs count: 20
Funder award ID: 21244074
OpenAlex ID: G618756004