@article{PAR00013491,
  title = {{E}n{OI} optimization for {SMOS} soil moisture over {W}est {A}frica},
  author = {{L}ee, {J}. {H}. and {P}ellarin, {T}. and {K}err, {Y}ann},
  editor = {},
  language = {{ENG}},
  abstract = {{I}n land surface or numerical weather prediction ({NWP}) models, a soil moisture initialization scheme is important not to drift the prognostic variables to errors. {W}e propose a novel approach for a stationary data assimilation scheme of ensemble optimal interpolation ({E}n{OI}) effective for soil moisture and ocean salinity ({SMOS}) soil moisture initialization. {F}or the optimization of {E}n{OI}, the satellite retrieval error specification was conducted rather than ensemble evolution. {A}s combining two ensembles generated from a satellite retrieval and a land surface model, this approach is termed as "two-step {E}n{OI}" in this study: (first step) the {SMOS} soil moisture retrieval ensembles (i.e., errors in brightness temperature, landscape, and geophysical parameters) were merged with {SMOS} {L}3 data; (second step) the data assimilation result from the first step was further used for the observations of the {E}n{OI}. {T}his two-step {E}n{OI} was compared with a sequential ensemble {K}alman filter ({E}n{KF}) evolving model state ensembles over time but assuming global constant a priori random errors for the {SMOS} observations. {T}he point-scale comparison results showed that two-step {E}n{OI} was better matched with the field measurements than the {SMOS} {L}3 data and a sequential ensemble {KF} scheme. {O}n meso-scale, a spatial average of two-step {E}n{OI} reached that of a sequential ensemble {KF} with the significantly reduced ensemble size. {T}hese results suggest that the performance of two-step {E}n{OI} is comparable to a sequential ensemble {KF} but computationally more effective. {F}rom this, it is illustrated that appropriate error specification of satellite retrieval is more important than a sequential evolution of model state ensembles, and brightness temperature ensemble mean can reduce the {SMOS} retrieval biases without sequential evolution.},
  keywords = {{B}rightness temperature errors ; ensemble kalman filter ({E}n{KF}) ; ensemble ; optimal interpolation ({E}n{OI}) ; soil moisture and ocean salinity ({SMOS}) ; soil moisture ; {W}est {A}frica ; {AFRIQUE} {DE} {L}'{OUEST}},
  booktitle = {},
  journal = {{I}eee {J}ournal of {S}elected {T}opics in {A}pplied {E}arth {O}bservations and {R}emote {S}ensing},
  volume = {8},
  numero = {4},
  pages = {1821--1829},
  ISSN = {1939-1404},
  year = {2015},
  DOI = {10.1109/jstars.2015.2402232},
  URL = {https://www.documentation.ird.fr/hor/{PAR}00013491},
}