Graphical Abstract

Barreyat, M., P. Chambon, J.-F. Mahfouf, G. Faure, and Y. Ikuta, 2021: A 1D Bayesian inversion applied to GPM Microwave Imager observations: Sensitivity studies. J. Meteor. Soc. Japan, 99,
Special Edition on Global Precipitation Measurement (GPM): 5th Anniversary,
https://doi.org/10.2151/jmsj.2021-050.
Early Online ReleaseGraphical Abstract NEW GA

 

Plain Language Summary: The assimilation of cloudy and rainy microwave observations is under investigation at Météo- France with a method called ’1D-Bay+3D/4D-Var’. This method consists of two steps: (i) a Bayesian inversion of microwave observations and (ii) the assimilation of the retrieved relative humidity profiles in a 3D/4D-Var framework. In this paper, two estimators for the Bayesian inversion are used: either a weighted average (WA) or the maximum likelihood (ML) of a kernel density function. Sensitivity studies over the first step of the method are conducted for different degrees of freedom: the observation error, the channel selection and the scattering properties of frozen hydrometeors in the observation operator. Observations over a two-month period of the Global Precipitation Measurement (GPM) Microwave Imager (GMI) on-board the GPM-Core satellite and forecasts of the convective scale model Application of Research to Operations at Mesoscale (AROME) have been chosen to conduct these studies.

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