Data Assimilation in the Presence of Forecast Bias: The GEOS Moisture Analysis

By Dick Dee, Ricardo Todling. 2000

470 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change, Oceanography

View Paper: https://www.semanticscholar.org/paper/4250f4ec9c1dadf117e630612cdfa77d43cb3492

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Citations in the corpus

Reanalyses Suitable for Characterizing Long-Term Trends (2010, background): “Indeed, there have already been substantial efforts to understand the model error term in a data assimilation context (e.g., Dee and da Silva 1998; Danforth et al. 2007; Li et al. 2009b).”
Bias and data assimilation (2005, methodology): “Dee and Todling (2000) proved optimality of the two-step algorithm, in the sense that it provides unbiased minimum-variance state estimates, when Kx = BxHT(HBxHT + R)−1 (19) Kb = BbHT(HBbHT + HBxHT + R)−1, (20) where Bx is the background error covariance matrix for the state estimates and Bb is the…”
Nonlinearity in Data Assimilation Applications: A Practical Method for Analysis (2001, methodology): “where the mean flow with velocity u c 5 1ms 21 is perurbed with a randon forcing from the left boundary v. This stochastic forcing has an exponential correlation in time with e-folding time t . A similar example was analyzed in detail by Cohn (1993) , where it was shown that the viscosity term is n”
Accounting for Model Errors in Ensemble Data Assimilation (2009, methodology): “Dee and da Silva (1998) proposed a method for the online estimation and correction of model bias.”
Ensemble Kalman filter assimilation of temperature and altimeter data with bias correction and application to seasonal prediction (2005, methodology): “The approach used here to account for the presence of forecastmodel bias is adapted from Dee and Todling (2000). In analogy to Eq.”
An adaptive covariance inflation error correction algorithm for ensemble filters (2007, background): “Error in the model can be a large term in many applications (Buizza et al., 1999; Dee and Todling, 2000).”
Correcting for forecast bias in soil moisture assimilation with the ensemble Kalman filter (2007, methodology): “This method was called on-line forecast-bias estimation and correction with feedback by Dee and da Silva [1998] and applied by Dee and Todling [2000], Keppenne et al. [2005] and Chepurin et al.”
Accounting for the Error due to Unresolved Scales in Ensemble Data Assimilation: A Comparison of Different Approaches (2005, methodology): “A complete description of it and the rationale for its use is provided in Whitaker and Hamill (2002). The underlying prinFIG.”