Spatiotemporal patterns of rainfall are important characteristics that influence runoff generation and flashflood magnitude and require high-resolution measurements to be adequately represented. This need is further emphasized in arid climates, where rainfall is scarce and highly variable. In this study, 24 years of corrected and gaugeadjusted radar rainfall estimates are used to (i) identify the spatial structure and dynamics of convective rain cells in a dry climate region in the Eastern Mediterranean, (ii) to determine their climatology, and (iii) to understand their relation with the governing synoptic systems and with flashflood generation. Rain cells are extracted using a segmentation method and a tracking algorithm, and are clustered into three synoptic patterns according to atmospheric variables from the ERA-Interim reanalysis. On average, the cells are about 90 km2 in size, move 13ms-1 from west to east, and live for 18 min. The Cyprus low accounts for 30% of the events, the low to the east of the study region for 44 %, and the Active Red Sea Trough for 26 %. The Active Red Sea Trough produces shorter rain events composed of rain cells with higher rain intensities, longer lifetime, smaller area, and lower velocities. The area of rain cells is positively correlated with topographic height. The number of cells is negatively correlated with the distance from the shoreline. Rain-cell intensity is negatively correlated with mean annual precipitation. Flash-flood-related events are dominated by rain cells of large size, low velocity, and long lifetime that move downstream with the main axis of the catchments. These results can be further used for stochastic simulations of convective rain storms and serve as input for hydrological models and for flash-flood nowcasting systems.
Bibliographical noteFunding Information:
Acknowledgements. The study was partially funded by the Dead Sea Drainage Authority, the Israel Water Authority, the Israel Science Foundation (grant no. 1007/15), the NSF-BSF grant (BSF 2016953) and the Lady Davis Fellowship Trust (project: RainFreq). This work is a contribution to the HyMeX program. The authors thank Maya Bartov, Moshe Armon and Uri Dayan for their assistance in validating the automatic classification of the synoptic systems.
© 2017 Author(s).