Estimating turbulent fluxes through assimilation of geostationary operational environmental satellites data using ensemble Kalman filter

Abstract

In this study, a data assimilation scheme is developed on the basis of the ensemble Kalman filter algorithm and the common land model (CoLM); soil moisture and model parameters are simultaneously optimized to improve the estimates of turbulent fluxes. Land surface temperature (LST) is retrieved from geostationary operational environmental satellites (GOES) data, validated, and then assimilated into the model. The data assimilation results are validated at six observation sites in the United States that include grassland, cropland, and forestland cover types. Data assimilation results indicate that in addition to improvements in the prediction of turbulent fluxes, model uncertainties are also reduced as a result of the assimilation of GOES LST retrieval data. The average reductions in root mean square error (RMSE) values are 47.5 and 31.1 W m ^-2. The effects of simultaneous optimization of soil moisture and model parameters are compared with those resulting from separate optimization; simultaneous optimization is found to yield smaller RMSE values. Further, in this study, the effects of Moderate Resolution Imaging Spectroradiometer (MODIS) and GOES temporal resolution data on data assimilation results are studied. The assimilation results indicate that the average RMSE values for GOES temporal resolution data are smaller than that for MODIS temporal resolution data. During the assimilation time period, the soil moisture obtained from assimilation closely agrees with the observed values, and the four vegetation parameters show distinct seasonal variations. However, the lack of sufficient information makes it difficult to estimate the true value of these variables and parameters. Copyright 2011 by the American Geophysical Union.