@article{fdi:010080411,
  title = {{I}dentifying lateral boundary conditions for the {M}2 tide in a coastal model using a stochastic gradient descent algorithm},
  author = {{K}oenig, {G}. and {A}ldebert, {C}. and {C}hevalier, {C}rist{\`e}le and {D}evenon, {J}. {L}.},
  editor = {},
  language = {{ENG}},
  abstract = {{W}hile lateral boundary conditions are crucial for the physical modeling of ocean dynamics, their estimation may lack accuracy in coastal regions. {D}ata-assimilation has long been used to improve accuracy, but most of the widely-used methods are difficult to implement. {W}e tried a new and an easy-to-implement method to estimate boundary conditions. {T}his method uses data assimilation with a stochastic gradient descent and successive approximations of the boundary conditions. {W}e tested it with twin experiments and a more realistic setting on a tidal model in the lagoon of {O}uano, in {N}ew-{C}aledonia. {T}he method proved successful and provided good estimation of the boundary conditions with various settings of subsampling and noise for the pseudo-data in the twin experiments, but there were important oscillations in the experiments with more realistic settings. {H}ere we present those results and discuss the use of our new and easy-to-implement method.},
  keywords = {{P}arameters identification ; {T}idal modeling ; {S}tochastic algorithms ; {D}ata assimilation ; {PACIFIQUE} ; {NOUVELLE} {CALEDONIE} ; {OUANO} {LAGON}},
  booktitle = {},
  journal = {{O}cean {M}odelling},
  volume = {156},
  numero = {},
  pages = {101709 [14 ]},
  ISSN = {1463-5003},
  year = {2020},
  URL = {https://www.documentation.ird.fr/hor/fdi:010080411},
}