@article{fdi:010075166,
  title = {{S}tochastic generation of precipitation fraction at high resolution with a multiscale constraint from satellite observations},
  author = {{G}uilloteau, {C}. and {R}oca, {R}. and {G}osset, {M}arielle and {V}enugopal, {V}.},
  editor = {},
  language = {{ENG}},
  abstract = {{I}n this work, we propose a method to generate an ensemble of equiprobable fields of rain occurrence at high resolution (1 degrees/16 and 30 min) using a satellite observational constraint. {S}atellite observations are used to constrain the spatio-temporal variations of the precipitation fraction at various scales. {S}patio-temporal averages at scales coarser than 1 degrees and 8 h are deterministically derived from the satellite observations. {A}t finer scales, variations are partially stochastically generated by perturbation of wavelet coefficients obtained through a three-dimensional discrete {H}aar wavelet orthogonal decomposition. {T}he proposed method can be viewed either as stochastic weather generation or as stochastic downscaling with a multiscale observational constraint. {T}he observational constraint used here is a high-resolution precipitation index derived from infrared cloud top temperature. {A}s a proof of concept, the method is used here to generate a 300-member annual ensemble covering a 12,000 km(2) area in {B}urkina {F}aso in {W}est {A}frica, with a parametrization derived from ground radar observations. {T}he stochastically generated fields aim at reproducing the multiscale statistical properties of the true precipitation field (as observed by a ground radar). {T}he ensemble mean is an optimal - in terms of mean squared error - estimation of the true precipitation fraction, with the uncertainty quantified by the ensemble dispersion.},
  keywords = {detection ; ensemble generation ; multiscale ; precipitation ; satellite ; wavelets ; {BURKINA} {FASO}},
  booktitle = {},
  journal = {{Q}uarterly {J}ournal of the {R}oyal {M}eteorological {S}ociety},
  volume = {144},
  numero = {1},
  pages = {176--190},
  ISSN = {0035-9009},
  year = {2018},
  DOI = {10.1002/qj.3314},
  URL = {https://www.documentation.ird.fr/hor/fdi:010075166},
}