[1] This paper reports on the results of an experiment designed to measure the spatial variability of rain drop size distribution (RDSD) at kilometer scale. Eight dual instruments (16 Parsivel disdrometers) were used to record the RDSD from October 2009 to January 2010. The spatial variability of the RDSD in terms of cross-correlation and changes in the reflectivity-rainfall (Z-R) relationship was calculated. The results provide an estimate of the variability range at spatial scales relevant for spatial radars such as TRMM-PR and GPM-DPR. It was found that the spatial variability of the RDSD in a single episode can exceed the inter-episode variability. This implies that estimates of the RDSD using a few disdrometers are not enough to capture the evolution of the RDSD, and that more detailed areal estimates are needed in order to fully analyze the RDSD.Citation: Tapiador, F. J., R. Checa, and M. de Castro (2010), An experiment to measure the spatial variability of rain drop size distribution using sixteen laser disdrometers, Geophys. Res. Lett.,

Hydroelectric plants require precise and timely estimates of rain, snow and other hydrometeors for operations. However, it is far from being a trivial task to measure and predict precipitation. This paper presents the linkages between precipitation science and hydroelectricity, and in doing so it provides insight into current research directions that are relevant for this renewable energy. Methods described include radars, disdrometers, satellites and numerical models. Two recent advances that have the potential of being highly beneficial for hydropower operations are featured: the Global Precipitation Measuring (GPM) mission, which represents an important leap forward in precipitation observations from space, and high performance computing (HPC) and grid technology, that allows building ensembles of numerical weather and climate models.

This paper presents a maximum entropy approach to Rain Drop Size Distribution (RDSD) modelling. It is shown that this approach allows (1) to use a physically consistent rationale to select a particular probability density function (pdf) (2) to provide an alternative method for parameter estimation based on expectations of the population instead of sample moments and (3) to develop a progressive method of modelling by updating the pdf as new empirical information becomes available. The method is illustrated with both synthetic and real RDSD data, the latest coming from a laser disdrometer network specifically designed to measure the spatial variability of the RDSD.