GPMマイクロ波イメージャーの観測に適用された1D Bayesian inversion：感度研究

Barreyat, Marylis; Chambon, Philippe; Mahfouf, Jean‐François; Faure, Ghislain; Ikuta, Yasutaka

doi:10.2151/jmsj.2021-050

Cited by 5 publications

(4 citation statements)

References 42 publications

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“…A 1D Bayesian inversion has been developed at Météo‐France to retrieve atmospheric profiles from cloudy microwave Bts (Barreyat et al, 2021; Duruisseau et al, 2019; Guerbette et al, 2016). The retrieved profiles are derived from a weighted average of profiles x i in the neighborhood of a given observation y .…”

Section: Methodsmentioning

confidence: 99%

“…The resulting bulk properties (single scattering albedo, extinction, asymmetry, backscattering) are then stored in look‐up tables as a function of hydrometeor content and temperature; these look‐up tables are then to be further used within assimilation systems. Since high‐frequency microwave radiances are sensitive to snowfall which can have a wide range of shapes, sizes, and densities, for example, an accurate SSP specification is crucial for optimal exploitation (Barreyat et al, 2021; Geer & Baordo, 2014; Kulie et al, 2010; Ringerud et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

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A 1D Bayesian inversion of microwave radiances using several radiative properties of solid hydrometeors

Barreyat

Chambon

Mahfouf

et al. 2023

Atmospheric Science Letters

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Numerical weather prediction centers increasingly make use of cloudy and rainy microwave radiances. Currently, the high microwave frequencies are simulated using simplified assumptions regarding the radiative properties of frozen hydrometeors. In particular, one single particle shape is often used for all precipitating frozen particles, all over the globe, and for all cloud types. In this paper, a multi-SSP (single scattering properties) approach for 1D Bayesian inversions is examined. Two experiments were set up: (1) one with three SSPs and (2) one with the previous SSPs plus one which leads to very cold brightness temperature distributions. For that purpose, we used observations from the GPM Microwave Imager radiometer over 2 months period and forecasts from the Météo-France convective scale AROME model. The results showed that mixtures of SSP are chosen by the inversion method for meteorological conditions with low scattering and that a single particle is chosen for those with high scattering to perform the inversions. Despite the fact that no specific weather scenes were found to be associated with a particular SSP the most efficient scattering particles can be favored for some of them.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A 1D Bayesian inversion of microwave radiances using several radiative properties of solid hydrometeors

Barreyat

Chambon

Mahfouf

et al. 2023

Atmospheric Science Letters

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“…In this study, an inversion algorithm is used to perform retrievals of frozen hydrometeors. This inversion method is taken as the same Bayesian algorithm which is used to assimilate microwave cloudy and rainy observations operationally within the ARPÈGE model using retrievals of relative humidity profiles (Guerbette et al, 2016;Duruisseau et al, 2019;Barreyat et al, 2021). Within this framework, each observation is collocated with a First Guess and a surrounding neighborhood (210 km in diameter).…”

Section: Bayesian Inversionmentioning

confidence: 99%

“…Regarding perturbations of the RT model in the IR, the use of the two schemes of Baran (Baran et al, 2014) and Baum (Baum et al, 2011) certainly do not encompass the complex variability of ice crystals in nature. A similar comment can be made regarding the perturbations in the MW simulations for which single particle shapes have been used in each simulation (Barreyat et al, 2021).…”

Section: Framework Limitationsmentioning

confidence: 99%

Synergistic approach of hydrometeor retrievals: considerations on radiative transfer and model uncertainties in a simulated framework

Villeneuve

Chambon

Fourrié

2023

Preprint

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Abstract. In cloudy situations, infrared and microwave observations are complementary, with infrared observations being sensitive to the small cloud droplets and ice particles and microwave observations sensitive to precipitation. This complementarity can lead to fruitful synergies in precipitation science (e.g. Kidd and Levizzani, 2022). However, several sources of errors do exist in the treatment of infrared and microwave data that could prevent such synergy. This paper studies several of these sources to estimate their impact on retrievals. To do so, simulations from the radiative transfer model RTTOV v13 are used to build simulated observations. Indeed, we make use of a fully simulated framework to explain the impacts of the identified errors. A combination of infrared and microwave frequencies is built within a Bayesian inversion framework. Synergy is studied using different experiments: (i) with several sources of errors eliminated; (ii) with only one source of errors considered at a time; (iii) with all sources of errors together. The derived retrievals of frozen hydrometeors for each experiment are examined in a statistical study of fifteen days in summer and fifteen days in winter over the Atlantic ocean. One of the main outcomes of the study is that the combination of infrared and microwave frequencies takes advantage of both spectral range strengths leading to accurate retrievals. Each source of error has more or less impact depending on the type of hydrometeor. Another outcome of the study is that even though the errors may decrease the magnitude of benefits generated by the combination of infrared and microwave frequencies, in all cases explored, their combination remains beneficial.

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Improvement of the Cloud Microphysics Scheme of the Mesoscale Model at the Japan Meteorological Agency Using Spaceborne Radar and Microwave Imager of the Global Precipitation Measurement as Reference

Ikuta

Satoh

Sawada

et al. 2021

Monthly Weather Review

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In this study, the single-moment 6-class bulk cloud microphysics scheme used in the operational numerical weather prediction system at the Japan Meteorological Agency was improved using the observations of the Global Precipitation Measurement (GPM) core satellite as reference values. The original cloud microphysics scheme has the following biases: underestimation of cloud ice compared to the brightness temperature of the GPM Microwave Imager (GMI) and underestimation of the lower troposphere rain compared to the reflectivity of GPM Dual-frequency Precipitation Radar (DPR). Furthermore, validation of the dual-frequency rate of reflectivity revealed that the dominant particles in the solid phase of simulation were graupel and deviated from DPR observation. The causes of these issues were investigated using a single-column kinematic model. The underestimation of cloud ice was caused by a high ice-to-snow conversion rate, and the underestimation of precipitation in the lower layers was caused by an excessive number of small-diameter rain particles. The parameterization of microphysics scheme is improved to eliminate the biases in the single-column model. In the forecast obtained using the improved scheme, the underestimation of cloud ice and rain is reduced. Consequently, the prediction errors of hydrometeors were reduced against the GPM satellite observations, and the atmospheric profiles and precipitation forecasts were improved.

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GPMマイクロ波イメージャーの観測に適用された1D Bayesian inversion：感度研究

Cited by 5 publications

References 42 publications

A 1D Bayesian inversion of microwave radiances using several radiative properties of solid hydrometeors

A 1D Bayesian inversion of microwave radiances using several radiative properties of solid hydrometeors

Synergistic approach of hydrometeor retrievals: considerations on radiative transfer and model uncertainties in a simulated framework

Improvement of the Cloud Microphysics Scheme of the Mesoscale Model at the Japan Meteorological Agency Using Spaceborne Radar and Microwave Imager of the Global Precipitation Measurement as Reference

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