Hydrologic Data Assimilation with the Ensemble Kalman Filter

By Rolf H. Reichle, Dennis McLaughlin, Dara Entekhabi. 2002

870 citations recorded by the source corpus.

Subfields: Atmospheric Science, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/ef5c6468c70033295a2d0216eb6d9cc868d459ed

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Citations in the corpus

Ensemble Kalman filtering (2005, background): “The socalled ensemble Kalman filter (EnKF) (Evensen 1994) is now under development or in use for hydrologic data assimilation (Reichle et al. 2002), ocean data assimilation (Keppenne and Rienecker 2002), retrospective atmospheric analysis (Whitaker et al. 2004), convective-scale atmospheric data…”
Sampling Errors in Ensemble Kalman Filtering. Part I: Theory (2008, background): “This version has shown efficiency in various contexts (Houtekamer and Mitchell 1998; Hansen and Smith 2000; Reichle et al. 2002; Snyder and Zhang 2003).”
Data assimilation for nonlinear problems by ensemble Kalman filter with reparameterization (2009, methodology): “…used in oceanography (Keppenne and Rienecker, 2002; Bertino et al., 2003), meteorology (Hamill et al., 2000; Houtekamer et al., 2005), hydrology (Reichle et al., 2002; Chen and Zhang, 2006), and petroleum engineering (Nævdal et al., 2005; Gu and Oliver, 2005; Lorentzen et al., 2005; Liu and…”
Ensemble smoother with multiple data assimilation (2013, background): “, 2005), hydrology (Reichle et al., 2002; Chen and Zhang, 2006; Liu et al., 2008) and petroleum reservoir history matching (Aanonsen et al.”
Assimilation of ice concentration in a coupled ice–ocean model, using the Ensemble Kalman filter (2003, methodology): “…Kalman filter have been widely used in geophysical applications, including oceanographic (Evensen 1994; Haugen and Evensen 2002; Keppenne and Rienecker 2002), biochemical (Natvik and Evensen 2003), hydrologic (Reichle et al. 2002) and atmospheric (Houtekamer and Mitchell 1998) applications.”
Real‐time groundwater flow modeling with the Ensemble Kalman Filter: Joint estimation of states and parameters and the filter inbreeding problem (2008, methodology): “More recently, for land-surface models data assimilation algorithms like the Ensemble Kalman Filter are proposed to assimilate soil moisture data (for instance from remote sensing) [e.g., Reichle et al., 2002].”
A Non-Gaussian Ensemble Filter Update for Data Assimilation (2010, background): “The prior and posterior distributions for x are no longer Gaussian (Reichle et al. 2002) and the Kalman ﬁlter is no longer optimal.”
Evaluating the Utility of Remotely Sensed Soil Moisture Retrievals for Operational Agricultural Drought Monitoring (2010, background): “filter first demonstrated by [19] and has been successfully applied to land surface forecasting problems [20].”
Assessing the Performance of the Ensemble Kalman Filter for Land Surface Data Assimilation (2006, background): “In particular, the ensemble Kalman filter has recently been suggested as a practical option for real-time estimation of land–atmosphere fluxes from remote sensing data (Reichle et al. 2002; Margulis et al. 2002; Crow and Wood 2003; Reichle and Koster 2003; Entekhabi et al. 2004).”
Optimal multiscale Kalman filter for assimilation of near-surface soil moisture into land surface models (2004, background): “In this context, several recent studies have focused on assimilation of radiobrightness temperature or near-surface soil moisture into land surface schemes [e.g., Houser et al., 1998; Reichle et al., 2002a, 2002b; Walker and Houser, 2001; Margulis et al., 2002; Crow and Wood, 2003; Montaldo and Al”
Uncertainty in hydrologic modeling: Toward an integrated data assimilation framework (2007, methodology): “…to nonlinear problems and easy implementation of the EnKF method has led to extensive applications of this DA technique in hydrology, meteorology, and other fields [e.g., Burgers et al., 1998; Margulis et al., 2002; Reichle et al., 2002a, 2002b; Moradkhani et al., 2005a; Vrugt et al., 2005].”
The Ensemble Kalman Filter: theoretical formulation and practical implementation (2003, background): “This was illustrated in an example by Reichle et al. (2002).”
A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling (2007, background): “Several studies have successfully demonstrated the usefulness of the ensemble Kalman filter for data assimilation (Crow and Wood, 2003; Reichle et al., 2002).”
Ensemble‐based assimilation of discharge into rainfall‐runoff models: A comparison of approaches to mapping observational information to state space (2009, methodology): “Therefore, a different methodology to calculate Kk is proposed, which has been used before to deal with highly nonlinear forward models for satellite observations [ Reichle et al., 2002a ].”
State and bias estimation for soil moisture profiles by an ensemble Kalman filter: Effect of assimilation depth and frequency (2007, methodology): “…Kalman filter (ENKF) [Evensen, 2003] was found to be an effective and efficient tool for state estimation in nonlinear models, such as demonstrated in synthetical studies [Reichle et al., 2002a, 2002b; Crow, 2003] and field studies [Margulis et al., 2002] on higher dimensional assimilation.”
Data Assimilation for Estimating the Terrestrial Water Budget Using a Constrained Ensemble Kalman Filter (2006, methodology): “In hydrologic studies, data assimilation has not been fully utilized, especially with remotely sensed data (McLaughlin, 2002), though a number of assimilation studies have been performed (Reichle et al., 2001, 2002; Crow and Wood, 2003; Crow, 2003).”