A dual-pass data assimilation scheme for estimating turbulent fluxes with FY3A data

Abstract. A dual-pass data assimilation scheme is developed to improve predictions of turbulent fluxes with FY3A land surface temperature (LST) data. This scheme is constructed based on the ensemble Kalman filter (EnKF) and common land model (CoLM). Pass 1 of the dual-pass data assimilation scheme optimizes model vegetation parameters at a long temporal scale and pass 2 optimizes soil moisture at a short temporal scale. Four sites are selected for the data assimilation experiments, namely Arou, BJ, Guantao, and Miyun in the People's Republic of China (PRC) that include grass, alpine meadow, crop, and orchard land cover types. The results are compared with data generated by a multi-scale turbulent flux observation system that includes an eddy covariance (EC) and a large aperture scintillometer (LAS) system. Results indicate that the CoLM can simulate the diurnal variations of turbulent flux, but usually underestimates the latent heat flux and evaporation fraction (EF) and overestimates sensible heat flux. With the assimilation of FY3A LST data, the dual-pass data assimilation scheme can improve the predictions of turbulent flux. The average root mean square error (RMSE) values drop from 81.2 to 39.6 W m⁻² and from 101.7 to 58.9 W m⁻² (the RMSE values drop 51.2% and 42.1%) for sensible and latent heat fluxes, respectively. To compare the results with LAS measurements, the source areas are calculated using a footprint model and overlaid with FY3A pixels since the LAS cover more than one FY3A pixel. The comparisons show that the assimilation results are closer to LAS measurements. With the dual-pass data assimilation scheme, the estimated soil moistures are generally closer to observations. Furthermore, the vegetation parameters are retrieved and incorporated into CoLM which enhanced the model's predictive abilities.