@article{fdi:010086460,
  title = {{R}egional sub-daily stochastic weather generator based on reanalyses for surface water stress estimation in central {T}unisia},
  author = {{F}arhani, {N}. and {C}arreau, {J}ulie and {K}assouk, {Z}. and {M}ougenot, {B}ernard and {L}e {P}age, {M}ichel and {L}ili-{C}habaane, {Z}. and {Z}itouna-{C}hebbi, {R}. and {B}oulet, {G}illes},
  editor = {},
  language = {{ENG}},
  abstract = {{W}e present {M}et{G}en: a sub-daily multi-variable stochastic weather generator implemented as an {R} library that can be used to perform gap-filling and to extend in time meteorological observation series. {M}et{G}en is tailored to provide surrogate series of air temperature, relative air humidity, global radiation and wind speed needed for surface water stress estimation that requires sub-daily resolution. {M}ultiple gauged stations can be used to increase the calibration data although spatial dependence is not modeled. {T}he approach relies on {G}eneralized {L}inear {M}odels that use, among their covariates, large-scale variables derived from {ERA}5 reanalyses. {M}et{G}en aims at preserving key features of the meteorological variables along with inter-variable dependencies. {W}e illustrate the abilities of {M}et{G}en using a case study with three stations in central {T}unisia. {W}e consider as alternatives a univariate and a multivariate bias correction techniques along with the un-processed large-scale variables.},
  keywords = {stochastic weather generator ; bias correction ; surface water stress ; estimation ; sub -daily resolution ; {ERA}5 reanalyses ; {TUNISIE}},
  booktitle = {},
  journal = {{E}nvironmental {M}odelling and {S}oftware},
  volume = {155},
  numero = {},
  pages = {105448 [16 p.]},
  ISSN = {1364-8152},
  year = {2022},
  DOI = {10.1016/j.envsoft.2022.105448},
  URL = {https://www.documentation.ird.fr/hor/fdi:010086460},
}