Professor A. Berne, thesis director, and the whole LTE team, congratulate J. Jaffrain for his thesis on “Experimental quantification of the variability of the raindrop size distribution at small scales".
Reliable quantitative precipitation estimation is crucial to better understand and eventually prevent water-related natural hazards (floods, landslides, avalanches, ...). Because rainfall is highly variable in time and space, precipitation monitoring and forecasting is a complex task. In addition, the variability of rainfall at small scales (for instance within the radar pixel) is still poorly understood. Knowledge of the rain drop size distribution (DSD) is of primary concern for precipitation estimation and in particular weather radar.
To better understand the variability of the DSD at small scales, a network of optical disdrometers (Parsivel) has been designed and set up. The instruments are fully autonomous in term of power supply and data transmission. The network of 16 disdrometers has been deployed over a typical operational weather radar pixel (1x1 km²) in Lausanne, Switzerland, for 16 months collecting DSD data at a high temporal resolution (30 s).
The sampling uncertainty associated with Parsivel measurements has been quantified for different quantities related to the DSD, using a 15-month data set collected by two collocated disdrometers.
Using a geostatistical approach, analyses show a significant spatial structure of the DSD, i.e., DSD fields are organized in time and space and not randomly distributed. The observed spatial structure is significant for temporal resolution below 30 min from which it is difficult to distinguish between the natural variability and the one induced by the sampling process.
Finally, the impact of the observed variability of the DSD on radar rainfall estimators is investigated focusing on two different radar power laws (the classical Z -R law for conventional radar and the R-Kdp law for polarimetric radar). Usually, such power law relationship are parameterized using data collected by a single instrument deployed at the ground which provides data with a limited spatial representativity. Analyses performed at different spatial scales (single disdrometer and average between the 16 disdrometers) show that, on average, the deviation between rain estimations derived at point and network scale is between -5 and 15%. It strongly raise the importance of taking into account the spatial structure fo the DSD when parameterizing relationships that will be used at large scales.