Retrieval of analogue radar images for ensemble nowcasting of orographic rainfall

An analogue-based approach for nowcasting the spatio-temporal evolution of orographic rainfall at the southern side of the Swiss Alps, NORA, was developed by Panziera etal. (2011). Analogues were found by retrieving a set of similar mesoscale situations and rainfall fields, and the forecast was given by the evolution of the precipitation observed after the analogues. This strategy avoids the explicit space-time rainfall simulation to obtain ensembles that characterize the forecast uncertainty. In this study the choice of the most similar rainfall fields is further explored by means of principal components analysis. The latter is used to represent the sequences of radar images in a phase space constructed with a low number of principal components. The principal components explain the main patterns which characterize the spatial distribution of rainfall, a feature that was not implemented in the original NORA. The alternative version of the nowcasting tool is described and the forecasts are verified in detail. Due to the ability to represent forecast uncertainty, the ensemble prediction system has superior value for probabilistic short-term forecasting compared to Eulerian persistence, which is more suited for deterministic forecasts. It is also demonstrated that retrieving similar sequences of images instead of single images does not improve forecast skill, which leads to the conclusion that the past trend in rainfall evolution is not a good predictor of its future evolution.