Assimilation variationnelle d'observations multi-échelles : Application à la fusion de données hétérogènes pour l'étude de la dynamique micro et macrophysique des systèmes précipitants

Abstract : On the one hand, the instruments designed to measure rainfall (rain gages, radars, etc.) perform measurements at different scales and of different natures. Their data are hard to compare. On the other hand, models simulating the evolution of rainfall are complex. It is not an easy task to parameterize and to validate them. In this thesis, we use data assimilation in order to couple heterogeneous observations of rainfall and models for studying rain and its spatiotemporal variability at different scales (macrophysical scale, which is interested in rain cells, as well as microphysical scale, which is interested in the drop size distribution – DSD). First, we develop an algorithm able to retrieve rain maps from measurements of attenuation of waves coming from TV satellites due to rainfall. Our retrievals are validated by comparison with radar and rain gages data for a case study in south of France. Second, we retrieve – again with data assimilation – vertical profiles of DSD and vertical winds from measurements of rain drop fluxes on the ground (using a disdrometer) and of Doppler spectra aloft (using a radar). We use these retrievals for 3 case studies to study the physical phenomena acting on rain drops during their fall and to evaluate the parameterization of these phenomena in models.
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François Mercier. Assimilation variationnelle d'observations multi-échelles : Application à la fusion de données hétérogènes pour l'étude de la dynamique micro et macrophysique des systèmes précipitants. Climatologie. Université Paris-Saclay, 2016. Français. ⟨NNT : 2016SACLV046⟩. ⟨tel-01466267⟩

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