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Exploitation des mesures "vapeur d'eau" du satellite Megha-Tropiques pour l'élaboration d'un algorithme de restitution de profils associés aux fonctions de densité de probabilité de l'erreur conditionnelle

Ramses Sivira 1
LATMOS - Laboratoire Atmosphères, Milieux, Observations Spatiales
Abstract : Water vapor has a central role in climatic systems: in a global scope, water vapor is important to energy distribution from tropical zones to polar regions via circulation cells; at mesoscale it participates to cloud systems development, precipitating or not, and in the lowest scale wet thermodynamic laws are the kernel of the clouds microphysics. Finally, water vapor is the most abundant greenhouse gas which is the key in the positive feedback phenomenon. The Megha-Tropiques mission was conceived to ameliorate the tropical water vapor cycle documentation and also the energy budget, through its three instruments: two microwave radiometers (MADRAS, an imager and SAPHIR, a sounder) dedicated to rain (liquid and iced ones) and atmospheric water vapor observations respectively; and a multispectral radiometer (ScaRaB)dedicated to radiative flux measurements at the top of the atmosphere with the aim to tropical water vapor and energy budget to describe this tropical systems evolution, it is composed by two microwaves radiometers. The payload characteristics allow, theoretically, an enhanced resolution around 183 GHz of microwave spectra, and soundings in presence of convective clouds. With the aim to build a learning database with correlated and also representative to problem data, an important tropical clear sky radiosoundings database was built for the 1990-2008 period to be coupled to a radiative transfer model to obtain synthetic brightness temperatures of two radiometers. We designed a methodology that allows us to develop a water vapor profile restitution algorithm from SAPHIR and MADRAS observations, and specially to quantify the restitution of conditional uncertainties. The approach was oriented to purely statistic restitution methods with the aim to extract the maximum information, without complementary information of the atmosphere thermodynamic structure or a priori profiles, to focus on inverse problem restrictions. Three statistical models were optimized using this learning database to estimate seven layers tropospheric water vapor profiles, a neural network (MLP), the generalized additive model and the support vector machines, and two conditional error pdf modeling hypothesis were tested, a Gaussian hypothesis (HG) and a two mixed Gaussian model (M2G). The optimized models are shown similar behaviors, which lead us to conclude that we obtain a model-independency restitution accuracy and this accuracy is directly related to physical constraints. Also, maximal precision was achieved in mid-tropospheric layers (maximal bias: 2.2% and maximal correlation coefficient: 0.87 in errors restitutions) while extreme layers show degraded precision values (at surface and the top of the troposphere, maximal bias: 6.92 associated to a fort dispersion with correlation coefficient: 0.58), this behavior could be explained by instrumental information contents. From conditional error probability functions, knowing observed brightness temperatures, humidity confidence intervals were estimated by each layer. The two hypotheses were tested and we obtained better results from the Gaussian Hypothesis. This methodology was tested using real data from Megha-Tropiques "water vapor" validation campaign in summer 2012 at Ouagadougou, which gave us radiosoundings measurements colocalized with satellite observations. Taking into account the incidence angle, SAPHIR calibration uncertainties and in-situ associated errors from measurement, results are consistent with the learning database with better accuracy (bias: 4.55% and correlation coefficient: 0.874 for error estimations) at mid-tropospheric layers, degrading it to extreme layers (bias: -4.81% and correlation coefficient: 0.491). Systematic application to SAPHIR observations could lead to tropical water vapor variability studies using theirs associated intervals confidence.
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Contributor : Ramsés Sivira <>
Submitted on : Friday, July 18, 2014 - 11:45:53 AM
Last modification on : Saturday, December 12, 2020 - 12:10:03 PM
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  • HAL Id : tel-01025685, version 1


Ramses Sivira. Exploitation des mesures "vapeur d'eau" du satellite Megha-Tropiques pour l'élaboration d'un algorithme de restitution de profils associés aux fonctions de densité de probabilité de l'erreur conditionnelle. Analyse numérique [cs.NA]. Université Pierre et Marie Curie - Paris VI, 2013. Français. ⟨tel-01025685⟩



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