• data assimilation;
  • remote sensing;
  • soil moisture

[1] The accurate specification of observing and/or modeling error statistics presents a remaining challenge to the successful implementation of many land data assimilation systems. Recent work has developed adaptive filtering approaches that address this issue. However, such approaches possess a number of known weaknesses, including a required assumption of serially uncorrelated error in assimilated observations. Recent validation results for remotely sensed surface soil moisture retrievals call this assumption into question. Here we propose and test an alternative system for tuning a soil moisture data assimilation system, which is robust to the presence of autocorrelated observing error. The approach is based on the application of a triple collocation approach to estimate the error variance of remotely sensed surface soil moisture retrievals. Using this estimate, the variance of assumed modeling perturbations is tuned until normalized filtering innovations have a temporal variance of one. Real data results over three highly instrumented watershed sites in the United States demonstrate that this approach is superior to a classical tuning strategy based on removing the serial autocorrelation in Kalman filtering innovations and nearly as accurate as a calibrated Colored Kalman filter in which autocorrelated observing errors are treated optimally.