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Liao-Fan Lin, Ardeshir M. Ebtehaj, Alejandro N. Flores, Satish Bastola, and Rafael L. Bras

1. Introduction Numerical climate and land–atmosphere models are widely used for providing land–atmospheric predictions at different time scales. These models typically capture both atmospheric thermodynamic processes and cloud microphysics to predict the dynamics of land–atmosphere water and energy fluxes. To improve the predictions of land–atmosphere state variables and parameters, a common practice is to assimilate observations from in situ gauges, radiosondes, and satellite measurements

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Sara Q. Zhang, T. Matsui, S. Cheung, M. Zupanski, and C. Peters-Lidard

the atmospheric initial condition. Land surface initial conditions (soil moisture and skin temperature) are derived from LIS spinup ( Kumar et al. 2008 ) of the Noah land surface model (LSM) with the MERRA-Land meteorological forcing ( Reichle 2012 ). In subsequent data assimilation cycles, the analysis produced by assimilating observations is used to issue ensemble 3-h forecasts, with new perturbations derived from updated analysis error covariance. The ensemble forecasts are used to update the

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