Preprints
https://doi.org/10.5194/hess-2021-248
https://doi.org/10.5194/hess-2021-248

  10 May 2021

10 May 2021

Review status: this preprint is currently under review for the journal HESS.

Improving object-oriented radar based nowcast by a nearest neighbour approach

Bora Shehu and Uwe Haberlandt Bora Shehu and Uwe Haberlandt
  • Institute for Hydrology and Water Resources Management, Leibniz University Hannover, Germany

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.

Bora Shehu and Uwe Haberlandt

Status: open (until 14 Jul 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2021-248', Ruben Imhoff, 27 May 2021 reply

Bora Shehu and Uwe Haberlandt

Bora Shehu and Uwe Haberlandt

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Short summary
In this paper we investigate if similar storms behave similarly and if the information obtained from past similar storms can improve the storm nowcast based on radar data. Here a nearest neighbour approach is employed to first identify similar storms and later to issue either a single or an ensemble nowcast based on k-most similar past storms. The results indicate that the information obtained from similar storms can reduce the errors considerably especially for convective storms nowcast.