Can hydrological observations improve global NWP in land-atmosphere-coupled data assimilation?
Present numerical weather prediction (NWP) systems assimilate massive atmospheric observations, but hydrological observations has not been used. This study aims to examine whether assimilating hydrological observations improve global NWP. For that purpose, we developed a land-atmosphere-coupled data assimilation system by extending the global atmospheric data assimilation system composed of Nonhydrostatic ICosahedral Atmospheric Model (NICAM) and Local Ensemble Transform Kalman Filter (LETKF). The NICAM incorporates the Minimal Advanced Treatments of Surface Interaction and RunOff (MATSIRO) as the land surface model. The new system can update state variables of the MATSHIRO by assimilating hydrological observations.
Satellite instruments can measure several hydrological parameters such as soil moisture, surface skin temperature and snow amount. To avoid complexity of quality control of such satellite observation data, this study assimilates hydrological parameters obtained from global land data assimilation systems (GLDAS) as the first step. Our preliminary experiments show proper, stable performance; assimilating snow amount and soil moisture data successfully reduces errors in those variables relative to the GLDAS. This poster presents the most recent results by the time of the symposium.
Mr. Kenta Kurosawa RIKEN Center for Computational Science