Joël Jaffrain

Experimental Quantification of the Sampling Uncertainty Associated with Measurements from PARSIVEL Disdrometers

AbstractThe variability of the (rain)drop size distribution (DSD) in time and space is an intrinsic property of rainfall, which is of primary importance for various environmental fields such as remote sensing of precipitation, for example. DSD observations are usually collected using disdrometers deployed at the ground level. Like any other measurement of a physical process, disdrometer measurements are affected by noise and sampling effects. This uncertainty must be quantified and taken into account in further analyses. This paper addresses this issue for the Particle Size Velocity (PARSIVEL) optical disdrometer by using a large dataset corresponding to light and moderate rainfall and collected from two collocated PARSIVELs deployed during 15 months in Lausanne, Switzerland. The relative sampling uncertainty associated with quantities characterizing the DSD—namely the total concentration of drops Nt and the median-volume diameter D0—is quantified for different temporal resolutions. Similarly, the relativ...

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

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 x 1 km(2)) 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.

Quantification of the Small-Scale Spatial Structure of the Raindrop Size Distribution from a Network of Disdrometers

AbstractThe spatial structure of the raindrop size distribution (DSD) conveys crucial information for reliable quantitative estimation of rainfall using remote sensing techniques. To investigate this question, a network of 16 optical disdrometers has been deployed over a typical weather radar pixel (~1 × 1 km2) in Lausanne, Switzerland. A set of 36 rainfall events has been classified according to three types: convective, transitional, and frontal. In a first step, the spatial structure of the DSD is quantified using spatial correlation for comparison with the literature, showing good agreement with previous studies. The spatial structure of important quantities related to the DSD—namely, the total concentration of drops Nt, the mass-weighted diameter Dm, and the rain rate R—is quantified using variograms. Results clearly highlight that DSD fields are organized and not randomly distributed even at a scale below 1 km. Moreover, convective-type rainfall exhibits larger variability of the DSD than do transiti...

Influence of the Subgrid Variability of the Raindrop Size Distribution on Radar Rainfall Estimators

AbstractThis work aims at quantifying the variability of the parameters of the power laws used for rain-rate estimation from radar data, on the basis of raindrop size distribution measurements over a typical weather radar pixel. Power laws between the rain rate and the reflectivity or the specific differential phase shift are fitted to the measured values, and the variability of the parameters is analyzed. At the point scale, the variability within this radar pixel cannot be solely explained by the sampling uncertainty associated with disdrometer measurements. When parameters derived from point measurements are applied at the radar pixel scale, the resulting error in the rain amount varies between −2% and +15%.

Stochastic Simulation of Intermittent DSD Fields in Time

A method for the stochastic simulation of (rain)drop size distributions (DSDs) in space and time using geostatistics is presented. At each pixel, the raindrop size distribution is described by a Gamma distribution with two or three stochastic parameters. The presence or absence of rainfall is modeled using an indicator field. Separable space-time variograms are used to estimate and reproduce the spatial and temporal structures of all these parameters. A simple and user-oriented procedure for the parameterization of the simulator is proposed. The only data required are DSD time series and radar rain-rate (or reflectivity) measurements. The proposed simulation method is illustrated for both frontal and convective precipitation using real data collected in the vicinity of Lausanne, Switzerland. The spatial and temporal structures of the simulated fields are evaluated and validated using DSD measurements from eight independent disdrometers.

Statistical analysis of rainfall intermittency at small spatial and temporal scales

Rainfall intermittency is analyzed and quantified at small spatial and temporal scales using 2 years of radar and disdrometer data collected in Switzerland. Analytical models are fitted and used to describe the intermittency for spatial scales between 0 and 30 km and temporal resolutions between 30 s and 6 h, providing climatological parameterizations for efficient and accurate upscaling/ downscaling of intermittent rainfall fields. First, the zero rainfall probability is analyzed with respect to the considered spatial resolution. Second, the spatial autocorrelation of rainfall intermittency is quantified with respect to the temporal resolution. Finally, the temporal autocorrelation is analyzed with respect to the spatial resolution. The results show that all these different aspects of rainfall intermittency can be accurately described by a scaled exponential function with a fixed shape parameter and a variable scale parameter. Models describing this variability are provided.