A dual-pass data assimilation scheme for estimating surface energy fluxes with FY3A-VIRR land surface temperature

A dual-pass data assimilation scheme is developed to improve predictions of surface energy fluxes. Pass 1 of the dual-pass data assimilation scheme optimizes model vegetation parameters at the weekly temporal scale and pass 2 optimizes soil moisture at the daily temporal scale. Based on the ensemble Kalman filter (EnKF), land surface temperature (LST) data derived from the new generation of Chinese meteorology satellite (FY3A-VIRR) is assimilated into common land model (CoLM) for the first time. Four sites are selected for the data assimilation experiments, namely Arou, BJ, Guantao, and Miyun that include alpine meadow, grass, crop, and orchard land cover types. The results are compared with data set generated by a multi-scale surface energy flux observation system that includes an automatic weather station (AWS), an eddy covariance (EC) and a large aperture scintillometer (LAS). Results indicate that the CoLM can simulate the diurnal variations of surface energy flux, but usually overestimates sensible heat flux and underestimates latent heat flux and evaporation fraction (EF). With FY3A-VIRR LST data, the dual-pass data assimilation scheme can reduce model uncertainties and improve predictions of surface energy flux. Compared with EC measurements, the average model biases (BIAS) values change from 37.8 to 7.7 W m 2 and from 27.6 to 18.8 W m 2; the root mean square error (RMSE) values drop from 74.7 to 39.1 W m 2 and from 95.1 to 62.7 W m 2 for sensible and latent heat fluxes respectively. For evaporation fraction (EF), the average BIAS values change from 0.29 to 0.0 and the average RMSE values drop from 0.38 to 0.12. To compare the results with LAS-measured sensible heat flux, the source areas are calculated using a footprint model and overlaid with FY3A pixels. The four sites averaged BIAS values drop from 63.7 to 8.5 W m 2 and RMSE values drop from 118.2 to 69.8 W m 2. Ultimately, the error sources in surface energy flux predictions are investigated, and the results show that both soil moisture and vegetation parameters caused the big model biases in surface energy flux predictions. With Pass 1 and Pass 2, the dual-pass data assimilation scheme can cut down the surface energy flux prediction biases (BIAS) to nearly zero.