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Stochastic generation of precipitation fraction at high resolution with a multiscale constraint from satellite observations
Author(s) -
Guilloteau Clément,
Roca Rémy,
Gosset Marielle,
Venugopal Vuruputur
Publication year - 2018
Publication title -
quarterly journal of the royal meteorological society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.744
H-Index - 143
eISSN - 1477-870X
pISSN - 0035-9009
DOI - 10.1002/qj.3314
Subject(s) - downscaling , parametrization (atmospheric modeling) , satellite , wavelet , precipitation , mathematics , meteorology , environmental science , remote sensing , computer science , geology , physics , quantum mechanics , astronomy , artificial intelligence , radiative transfer
In this work, we propose a method to generate an ensemble of equiprobable fields of rain occurrence at high resolution (1°/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° 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.