Reply on RC1

Abstract

The nowcast of rainfall storms at fine temporal and spatial resolutions is quite challenging due to the erratic nature of rainfall at such scales. Typically, rainfall storms are recognized by radar data, and extrapolated in the future by the Lagrangian persistence. However, storm evolution is much more dynamic and complex than the Lagrangian persistence, leading to short forecast horizons especially for convective events. Thus, the aim of this paper is to investigate the improvement that past similar storms can introduce to the object-oriented radar based nowcast. Here we propose a nearest neighbour approach that measures first the similarity between the “to-be-nowcasted” storm and past observed storms, and later uses the behaviour of the past most similar storms to issue either a single nowcast (by averaging the 4 most similar storm-responses) or an ensemble nowcast (by considering 30 most similar storm-responses). Three questions are tackled here: i) what features should be used to describe storms in order to check for similarity? ii) how to measure similarity between past storms? and iii) is this similarity useful for storm oriented nowcast? For this purpose, individual storms from 110 events in the period 2000–2018 recognized within the Hannover Radar Range (R~115 km2), Germany, were used as a basis for investigation. A “leave-one-event-out” cross-validation is employed to train and validate the nearest neighbour approach for the prediction of the area, mean intensity, the x and y velocity components of the “to-be-nowcasted” storm for lead times up to +3 hours. Prior to the training, two importance analyses methods (Pearson correlation and partial information correlation) are employed to identify the most important predictors. The results indicate that most of storms behave similarly, and the knowledge obtained from such similar past storms can improve considerably the storm nowcast compared to the Lagrangian persistence especially for convective events (storms shorter than 3 hours) and longer lead times (from 1 to 3 hours). The nearest neighbour approach seems promising, nevertheless there is still room for improvement by either increasing the sample size or employing more suitable methods for the predictor identification.