Eulerian and Lagrangian persistence of precipitation patterns derived from continental scale radar composite images is used as a measure of predictability and for nowcasting. A three-step procedure is proposed: First, the motion field of precipitation is determined by variational radar echo tracking. Second, radar echo patterns are advected following the motion field in order to obtain Lagrangian persistence forecasts. Third, the Eulerian and Lagrangian persistence forecasts are compared to observations, and the lifetime and other measures of predictability are calculated. The procedure is repeated with images that have been decomposed according to scales to describe the scale-dependence of predictability. The methodology has been developed and initially tested with radar composite images of relatively flat eastern North-America. It is now applied in a more complex environment: the European Alps. Here, the steep orography influences both evolution and motion of precipitation systems and the quality of radar data.

The analysis has a threefold application: i) determine the scale-dependence of predictability, ii) set a scale-dependent standard against which the skill for quantitative precipitation forecasting by numerical modeling can be evaluated, and iii) extended, partly probabilistic nowcasting by optimal extrapolation of radar precipitation patterns. Lagrangian persistence on large scales was found to have significant forecast skill up to lead times of several hours.