@article{fdi:010064236,
  title = {{O}ptimization of a radiative transfer forward operator for simulating {SMOS} brightness temperatures over the upper {M}ississippi basin},
  author = {{L}ievens, {H}. and {A}l {B}itar, {A}. and {V}erhoest, {N}. {E}. {C}. and {C}abot, {F}. and {D}e {L}annoy, {G}. {J}. {M}. and {D}rusch, {M}. and {D}umedah, {G}. and {F}ranssen, {H}. {J}. {H}. and {K}err, {Y}ann and {T}omer, {S}. {K}. and {M}artens, {B}. and {M}erlin, {O}livier and {P}an, {M}. and van den {B}erg, {M}. {J}. and {V}ereecken, {H}. and {W}alker, {J}. {P}. and {W}ood, {E}. {F}. and {P}auwels, {V}. {R}. {N}.},
  editor = {},
  language = {{ENG}},
  abstract = {{T}he {S}oil {M}oisture {O}cean {S}alinity ({SMOS}) satellite mission routinely provides global multiangular observations of brightness temperature {TB} at both horizontal and vertical polarization with a 3-day repeat period. {T}he assimilation of such data into a land surface model ({LSM}) may improve the skill of operational flood forecasts through an improved estimation of soil moisture {SM}. {T}o accommodate for the direct assimilation of the {SMOS} {TB} data, the {LSM} needs to be coupled with a radiative transfer model ({RTM}), serving as a forward operator for the simulation of multiangular and multipolarization top of the atmosphere {TB}s. {T}his study investigates the use of the {V}ariable {I}nfiltration {C}apacity model coupled with the {C}ommunity {M}icrowave {E}mission {M}odelling {P}latform for simulating {SMOS} {TB} observations over the upper {M}ississippi basin, {U}nited {S}tates. {F}or a period of 2 years (2010-11), a comparison between {SMOS} {TB}s and simulations with literature-based {RTM} parameters reveals a basin-averaged bias of 30 {K}. {T}herefore, time series of {SMOS} {TB} observations are used to investigate ways for mitigating these large biases. {S}pecifically, the study demonstrates the impact of the {LSM} soil moisture climatology in the magnitude of {TB} biases. {A}fter cumulative distribution function matching the {SM} climatology of the {LSM} to {SMOS} retrievals, the average bias decreases from 30 {K} to less than 5 {K}. {F}urther improvements can be made through calibration of {RTM} parameters related to the modeling of surface roughness and vegetation. {C}onsequently, it can be concluded that {SM} rescaling and {RTM} optimization are efficient means for mitigating biases and form a necessary preparatory step for data assimilation.},
  keywords = {{ETATS} {UNIS} ; {MISSISSIPPI} {BASSIN}},
  booktitle = {},
  journal = {{J}ournal of {H}ydrometeorology},
  volume = {16},
  numero = {3},
  pages = {1109--1134},
  ISSN = {1525-755{X}},
  year = {2015},
  DOI = {10.1175/jhm-d-14-0052.1},
  URL = {https://www.documentation.ird.fr/hor/fdi:010064236},
}