TY - JOUR

T1 - Improving radar-based rainfall nowcasting by a nearest-neighbour approach

T2 - Part 1: Storm characteristics

AU - Shehu, Bora

AU - Haberlandt, Uwe

N1 - Funding Information: Financial support. This research was funded by the German Federal Ministry of Education and Research (BMBF) (grant no. 03G0846B). Funding Information: Acknowledgements. The results presented in this study are part of the research project “Real-time prediction of pluvial floods and induced water contamination in urban areas (EVUS)” funded by the German Federal Ministry of Education and Research (Bundesmin-isterium für Bildung und Forschung – BMBF), who are gratefully thanked. We are also thankful for the provision and right to use the data from the German National Weather Service (Deutscher Wet-terdienst – DWD) and the HyRaTrac program from Stefan Krämer. Lastly, we are also grateful for the useful comments from the three reviewers Seppo Pulkkinen, Ruben Imhoff, and Georgy Ayzel that improved the final version of the manuscript.

PY - 2022/3/25

Y1 - 2022/3/25

N2 - The nowcast of rainfall storms at fine temporal and spatial resolutions is quite challenging due to the unpredictable nature of rainfall at such scales. Typically, rainfall storms are recognized by weather radar 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 the 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 should similarity between past storms be measured? (iii) Is this similarity useful for object-oriented nowcast? For this purpose, individual storms from 110 events in the period 2000-2018 recognized within the Hanover Radar Range (R ∼ km2), Germany, are used as a basis for investigation. A "leave-one-event-out"cross-validation is employed to test the nearest-neighbour approach for the prediction of the area, mean intensity, the x and y velocity components, and the total lifetime of the to-be-nowcasted storm for lead times from + 5 min up to + 3 h. Prior to the application, two importance analysis methods (Pearson correlation and partial information correlation) are employed to identify the most important predictors. The results indicate that most of the storms behave similarly, and the knowledge obtained from such similar past storms helps to capture better the storm dissipation and improves the nowcast compared to the Lagrangian persistence, especially for convective events (storms shorter than 3 h) and longer lead times (from 1 to 3 h). The main advantage of the nearest-neighbour approach is seen when applied in a probabilistic way (with the 30 closest neighbours as ensembles) rather than in a deterministic way (averaging the response from the four closest neighbours). The probabilistic approach seems promising, especially for convective storms, and it can be further improved by either increasing the sample size, employing more suitable methods for the predictor identification, or selecting physical predictors.

AB - The nowcast of rainfall storms at fine temporal and spatial resolutions is quite challenging due to the unpredictable nature of rainfall at such scales. Typically, rainfall storms are recognized by weather radar 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 the 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 should similarity between past storms be measured? (iii) Is this similarity useful for object-oriented nowcast? For this purpose, individual storms from 110 events in the period 2000-2018 recognized within the Hanover Radar Range (R ∼ km2), Germany, are used as a basis for investigation. A "leave-one-event-out"cross-validation is employed to test the nearest-neighbour approach for the prediction of the area, mean intensity, the x and y velocity components, and the total lifetime of the to-be-nowcasted storm for lead times from + 5 min up to + 3 h. Prior to the application, two importance analysis methods (Pearson correlation and partial information correlation) are employed to identify the most important predictors. The results indicate that most of the storms behave similarly, and the knowledge obtained from such similar past storms helps to capture better the storm dissipation and improves the nowcast compared to the Lagrangian persistence, especially for convective events (storms shorter than 3 h) and longer lead times (from 1 to 3 h). The main advantage of the nearest-neighbour approach is seen when applied in a probabilistic way (with the 30 closest neighbours as ensembles) rather than in a deterministic way (averaging the response from the four closest neighbours). The probabilistic approach seems promising, especially for convective storms, and it can be further improved by either increasing the sample size, employing more suitable methods for the predictor identification, or selecting physical predictors.

UR - http://www.scopus.com/inward/record.url?scp=85127821432&partnerID=8YFLogxK

U2 - 10.5194/hess-26-1631-2022

DO - 10.5194/hess-26-1631-2022

M3 - Article

AN - SCOPUS:85127821432

VL - 26

SP - 1631

EP - 1658

JO - Hydrology and Earth System Sciences

JF - Hydrology and Earth System Sciences

SN - 1027-5606

IS - 6

ER -