Japan Geoscience Union Meeting 2019

Presentation information

[E] Oral

A (Atmospheric and Hydrospheric Sciences ) » A-AS Atmospheric Sciences, Meteorology & Atmospheric Environment

[A-AS01] High performance computing for next generation weather, climate, and environmental sc iences

Wed. May 29, 2019 9:00 AM - 10:30 AM 104 (1F)

convener:Hiromu Seko(Meteorological Research Institute), Takemasa Miyoshi(RIKEN), Chihiro Kodama(Japan Agency for Marine-Earth Science and Technology), Masayuki Takigawa(Japan Agency for Marine-Earth Science and Technology), Chairperson:Hiromu Seko(Meteorological Research Institute), Chihiro Kodama(Japan Agency for Marine-Earth Science and Technology)

10:15 AM - 10:30 AM

[AAS01-06] Model Parameter Estimation with Data Assimilation Using NICAM-LETKF

★Invited Papers

*Shunji Kotsuki1, Yousuke Sato2, Koji Terasaki1, Hasashi Yashiro1, Hirofumi Tomita1, Masaki Satoh3, Takemasa Miyoshi1 (1.RIKEN Center for Computational Science, 2.Graduate School of Engineering, Nagoya University, 3.Atmosphere and Ocean Research Institute, the University of Tokyo)

Keywords:Model Parameter Estimation, Large Scale Condensation, Ensemble Kalman Filter, Liquid Water Path, Radiation Budget, NICAM-LETKF

This study aims to improve forecasts of numerical weather prediction (NWP) models by optimizing model parameters with data assimilation. Kotsuki et al. (2018a, JGR) succeeded in improving global precipitation forecasts at 112-km-resolution NICAM (Nonhydrostatic ICosahedral Atmospheric Model) by estimating a parameter called B1 of Berry (1967)’s large-scale condensation scheme using satellite-observed precipitation data and the Local Ensemble Transform Kalman Filter (LETKF).

Extending the previous study, this study explores to improve the forecasts further using other satellite observations. This study estimates the parameter B1 as a global-constant parameter with cloud liquid water (CLW) data observed by GCOM-W/AMSR2. The parameter estimation successfully reduces excessive bias in CLW although precipitation forecasts are degraded. In addition, this study extends to estimate spatial distributions of the B1 parameter. The spatially-varying B1 parameter shows the best agreement to the spatial pattern of observed LWP. This presentation will include the most recent progress up to the time of the meeting.