A review of issues in ensemble-based Kalman filtering

By M. Ehrendorfer. 2007

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Estimating surface CO 2 fluxes from space-borne CO 2 dry air mole fraction observations using an ensemble Kalman Filter (2008, methodology): “We have developed an Ensemble Kalman Filter (EnKF) (Evensen, 1994, 2003; Ehrendorfer, 2007) to estimate surface CO2 fluxes from space-borne measurements15 of XCO2 (Sect.”
A review of operational methods of variational and ensemble‐variational data assimilation (2017, methodology): “The consequences of under-sampling are two-fold: P(N) usually underestimates the variance (leading to filter divergence) and P(N) is severely rank deficient; the rank of P b (N) computed with (26) is ≤ (N−1), which leads to the introduction of analysis noise (Houtekamer and Mitchell, 1998; van Leeuw”
A review of forecast error covariance statistics in atmospheric variational data assimilation. II: Modelling the forecast error covariance statistics (2008, background): “Despite its nice theoretical properties, and its ease of implementation, there remain many hurdles to overcome before the EnKF can be used reliably (e.g. Ehrendorfer, 2007) and so VAR remains the workhorse of operational data assimilation.”