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Two data assimilation (DA) methods are compared for their ability toproduce an accurate soil moisture analysis using theMétéo-France land surface model: (i) SEKF, a simplifiedextended Kalman filter, which uses a climatological background-errorcovariance, and (ii) EnSRF, the ensemble square root filter, which uses anensemble background-error covariance and approximates random rainfallerrors stochastically. In situ soil moisture observations at5 cm depth are assimilated into the surface layer and 30 cm deep observationsare used to evaluate the root-zone analysis on 12 sites in south-western France(SMOSMANIA network). These sites differ in terms of climate and soil texture.The two methods perform similarly and improve on the open loop.Both methods suffer from incorrectlinear assumptions which are particularly degrading to the analysisduring water-stressed conditions: the EnSRF by a dry bias and the SEKFby an over-sensitivity of the model Jacobian between the surface andthe root-zone layers. These problems are less severe for thesites with wetter climates. A simple bias correctiontechnique is tested on the EnSRF. Although this reduces the bias,it modifies the soil moisture fluxes andsuppresses the ensemble spread, which degrades the analysisperformance. However, the EnSRF flow-dependent background-errorcovariance evidently captures seasonal variability in the soilmoisture errors and should exploit planned improvements in the modelphysics.  Synthetic twin experiments demonstrate that when there isonly a random component in the precipitation forcing errors, thecorrect stochastic representation of these errors enables the EnSRF toperform better than the SEKF. It might therefore be possible for the EnSRF to perform better than theSEKF with real data, if the rainfall uncertainty was accurately captured. However, the simple rainfall error modelis not advantageous in our real experiments. More realistic rainfall error models are suggested

Comparing the ensemble and extended Kalman filters for in situ soil moisture assimilation with contrasting conditions