Four Dimensional CO2 Data Assimilation of GOSAT Observation Data Using a Local Ensemble Transform Kalman Filter (LETKF)

Abstract:

Impacts of CO2 concentration data obtained from satellite (GOSAT TIR L2 Ver. 1.0) measurements on the estimation of global surface CO2 fluxes have been investigated using an ensemble-based four-dimensional data assimilation system (LETKF). An online atmospheric transport model (MJ98-CDTM) is employed in the data assimilation system to optimize surface CO2 fluxes from real observations at spatial and temporal resolutions of 6 days and about 2.8° (T42), respectively. The features of GOSAT TIR L2 Ver. 1.0 data are their larger data number than that of SWIR L2 (about 10 times) and smaller standard deviation than their former version (TIR L2 Ver. 0.01). These points are the advantageous features to CO2 data assimilation. One of the most important issues in satellite data assimilation is a bias correction technique. Therefore, we have tested 4 types of satellite bias correction experiments (w/o bias correction, monthly mean bias correction, all data bias correction and globally constant bias correction) using independent CO2 concentration analysis (JMA CO2 distribution) in our data assimilation system. Our results showed that estimated CO2 concentration and fluxes are significantly sensitive to bias correction scheme. The reason may come from that model biases are important issue on data assimilation. In conclusion, suitable satellite data bias correction allows obtaining realistic CO2 concentration field and modifying surface CO2 flux almost entire earth surface. In addition, this satellite bias correction scheme makes it possible to use multiple satellite observation data simultaneously in CO2 data assimilation.