Water Resources Research

A network of disdrometers to quantify the small-scale variability of the raindrop size distribution

Authors

  • Joël Jaffrain,

    1. Laboratoire de Télédétection Environnementale, School of Architecture, Civil and Environmental Engineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
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  • André Studzinski,

    1. Laboratoire de Télédétection Environnementale, School of Architecture, Civil and Environmental Engineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
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  • Alexis Berne

    1. Laboratoire de Télédétection Environnementale, School of Architecture, Civil and Environmental Engineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
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Abstract

[1] Insight into the spatial variability of the (rain) drop size distribution (DSD), and hence rainfall, is of primary importance for various environmental applications like cloud/precipitation microphysical processes, numerical weather modeling, and estimation of rainfall using remote sensing techniques. In order to quantify the small-scale variability of the DSD, a network of 16 optical disdrometers has been designed and deployed over a typical operational weather radar pixel (about 1 × 1 km2) in Lausanne, Switzerland. This network is fully autonomous in terms of power supply as well as data transmission and storage. The combination of General Radio Packet Service and radio communication allows a real-time access to the DSD measurements. The network is sampling at a temporal resolution of 30 s. A period representative of frontal precipitation is analyzed to illustrate the measurement capabilities of the network. The spatial variability is quantified by the coefficient of variation of the total concentration of drops, the mass-weighted diameter, and the rain rate between the 16 stations of the network. The sampling uncertainty associated with disdrometer measurements is taken into account, and the analysis of a 1.5 month rainy period shows a significant variability of these quantities, which cannot be explained by the sampling uncertainty alone, even at such a small scale.

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