@article{PAR00018751,
  title = {{N}ew statistical methods for precipitation bias correction applied to {WR}f model simulations in the {A}ntisana {R}egion, {E}cuador},
  author = {{H}eredia, {M}. {B}. and {J}unquas, {C}l{\'e}mentine and {P}rieur, {C}. and {C}ondom, {T}homas},
  editor = {},
  language = {{ENG}},
  abstract = {{T}he {E}cuadorian {A}ndes are characterized by a complex spatiotemporal variability of precipitation. {G}lobal circulation models do not have sufficient horizontal resolution to realistically simulate the complex {A}ndean climate and in situ meteorological data are sparse; thus, a high-resolution gridded precipitation product is needed for hydrological purposes. {T}he region of interest is situated in the center of {E}cuador and covers three climatic influences: the {A}mazon basin, the {A}ndes, and the {P}acific coast. {T}herefore, regional climate models are essential tools to simulate the local climate with high spatiotemporal resolution; this study is based on simulations from the {W}eather {R}esearch and {F}orecasting ({WRF}) {M}odel. {T}he {WRF} {M}odel is able to reproduce a realistic precipitation variability in terms of the diurnal cycle and seasonal cycle compared to observations and satellite products; however, it generated some nonnegligible bias in the region of interest. {W}e propose two new methods for precipitation bias correction of the {WRF} precipitation simulations based on in situ observations. {O}ne method consists of modeling the precipitation bias with a {G}aussian process metamodel. {T}he other method is a spatial adaptation of the cumulative distribution function transform approach, called {CDF}-t, based on {V}oronoi diagrams. {T}he methods are compared in terms of precipitation occurrence and intensity criteria using a cross-validation leave-one-out framework. {I}n terms of both criteria, the {G}aussian process metamodel approach yields better results. {H}owever, in the upper parts of the {A}ndes (>2000 m), the spatial {CDF}-t method seems to better preserve the spatial {WRF} physical patterns.},
  keywords = {{P}recipitation ; {S}tatistical techniques ; {M}odel errors ; {R}egional models ; {M}ountain meteorology ; {EQUATEUR}},
  booktitle = {},
  journal = {{J}ournal of {H}ydrometeorology},
  volume = {19},
  numero = {12},
  pages = {2021--2040},
  ISSN = {1525-755{X}},
  year = {2018},
  DOI = {10.1175/jhm-d-18-0032.1},
  URL = {https://www.documentation.ird.fr/hor/{PAR}00018751},
}