%0 Journal Article
%9 ACL : Articles dans des revues avec comité de lecture répertoriées par l'AERES
%A Guilloteau, C.
%A Roca, R.
%A Gosset, Marielle
%A Venugopal, V.
%T Stochastic generation of precipitation fraction at high resolution with a multiscale constraint from satellite observations
%D 2018
%L fdi:010075166
%G ENG
%J Quarterly Journal of the Royal Meteorological Society
%@ 0035-9009
%K detection ; ensemble generation ; multiscale ; precipitation ; satellite ; wavelets
%K BURKINA FASO
%M ISI:000457053200014
%N 1
%P 176-190
%R 10.1002/qj.3314
%U https://www.documentation.ird.fr/hor/fdi:010075166
%> https://www.documentation.ird.fr/intranet/publi/2019/02/010075166.pdf
%V 144
%W Horizon (IRD)
%X In 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. Satellite observations are used to constrain the spatio-temporal variations of the precipitation fraction at various scales. Spatio-temporal averages at scales coarser than 1 degrees and 8 h are deterministically derived from the satellite observations. At finer scales, variations are partially stochastically generated by perturbation of wavelet coefficients obtained through a three-dimensional discrete Haar wavelet orthogonal decomposition. The proposed method can be viewed either as stochastic weather generation or as stochastic downscaling with a multiscale observational constraint. The observational constraint used here is a high-resolution precipitation index derived from infrared cloud top temperature. As a proof of concept, the method is used here to generate a 300-member annual ensemble covering a 12,000 km(2) area in Burkina Faso in West Africa, with a parametrization derived from ground radar observations. The stochastically generated fields aim at reproducing the multiscale statistical properties of the true precipitation field (as observed by a ground radar). The 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.
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