Carbon Data Assimilation Using an Ensemble Kalman Filter

As a first step to build an ensemble data assimilation and source inversion system for atmospheric carbon, I implemented column-integrated carbon monoxide (CO) mixing ratio assimilation capability in an ensemble Kalman filter (EnKF) data assimilation system with the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem). In spite of its global coverage, the CO retrievals from the Measurements Of Pollution In The Troposphere (MOPITT) instrument onboard the Terra satellite are available only once per day. There has been restricted use of these CO data for atmospheric chemistry forecasting. Data assimilation provides an effective way to guide the model in time. This WRF-Chem/EnKF system has been tested for a real forest fire case in British Columbia in 2010. It has been observed that after assimilating MOPITT data, the model has been constrained closer toward the observations and the root-mean-square errors (RMSE) between the forecasts and the observations have been reduced. An inverse modeling of CO sources using parameter estimation with an EnKF was also performed. Comparisons of the assimilated CO profiles with optimal emissions to observations indicate that the assimilation leads to a considerable improvement of the model simulations as compared with a control run with no assimilation. Model biases in the simulation of background values are reduced and an improvement in the simulation of very high concentrations is observed