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Comparison Between Radar and Automatic Weather Station Refractivity Variability

Ruben Hallali 1, 2 Francis Dalaudier 3 Jacques Parent Du Châtelet 2 
LATMOS - Laboratoire Atmosphères, Milieux, Observations Spatiales
LATMOS - Laboratoire Atmosphères, Milieux, Observations Spatiales
Abstract : Weather radars measure changes in the refractive index of air in the atmospheric boundary layer. The technique uses the phase of signals from ground targets located around the radar to provide information on atmospheric refractivity related to meteorological quantities such as temperature, pressure and humidity. The approach has been successfully implemented during several field campaigns using operational S-band radars in Canada, UK, USA and France. In order to better characterize the origins of errors, a recent study has simulated temporal variations of refractivity based on Automatic Weather Station (AWS) measurements. This reveals a stronger variability of the refractivity during the summer and in the afternoon when the refractivity is the most sensitive to humidity, probably because of turbulence close to the ground. This raises the possibility of retrieving information on the turbulent state of the atmosphere from the variability in radar refractivity. An analysis based on a 1-year dataset from the operational C-band radar at Trappes (near Paris, France) and AWS refractivity variability measurements was used to measure those temporal and spatial variabilities. Particularly during summer, a negative bias increasing with range is observed between radar and AWS estimations, and is well explained by a model based on Taylor’s hypotheses. The results demonstrate the possibility of establishing, depending on season, a quantitative and qualitative link between radar and AWS refractivity variability that reflects low-level coherent turbulent structures.
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Submitted on : Friday, April 1, 2016 - 8:42:38 AM
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Ruben Hallali, Francis Dalaudier, Jacques Parent Du Châtelet. Comparison Between Radar and Automatic Weather Station Refractivity Variability. Boundary-Layer Meteorology, Springer Verlag, 2016, 160 (2), pp.299-317. ⟨10.1007/s10546-016-0145-x⟩. ⟨insu-01296501⟩



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