TY - JOUR

T1 - Relevance of merging radar and rainfall gauge data for rainfall nowcasting in urban hydrology

AU - Shehu, Bora

AU - Haberlandt, Uwe

N1 - Funding Information: The authors would like to thank Dr. Stefan Krämer for providing the HyRaTrac program for the identification, and tracking of the rainfall storms based on radar data. 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 (Bundesministerium für Bildung und Forschung BMBF) with funding ID 03G0846B, who are gratefully acknowledged. We are also thankful for the provision and right to use the data from the German National Weather Service (Deutscher Wetterdienst DWD), and the nowcasting algorithms from the open access python library “pysteps”. Lastly, the authors would like to thank A. Seed, two anonymous reviewers and the associate editor for their detailed comments which improved the manuscript.

PY - 2021/3

Y1 - 2021/3

N2 - Accurate Quantitative Precipitation Forecasts (QPF) at high spatial and temporal resolution are a perquisite for urban flood prediction. Commonly Lagrangian extrapolation of the rainfall patterns recognized by radar data, forms the basis of such forecasts for the near future (up to 2 h lead time) – referred here as nowcasting. Nevertheless, due to the intermittent nature of the rainfall at such fine scales, the predictability of storms is limited to about 20 min. The predictability loss is caused mainly by the inability of the radar to capture the true rainfall field and because the Lagrangian Persistence is unable to model the temporal evolution of the rainfall field. In this study we focus on the first problem, on how to extend the predictability limit of rainfall at such scales by improving the rainfall field fed into the nowcast model. To overcome the errors associated with the radar intensities, merging techniques between radar and gauge measurements are advised. Among different employed techniques (mean field bias, kriging with external drift and quantile mapping based correction) the conditional merging between the radar and rainfall-gauge measurements seems to capture at best the spatial and temporal patterns of the rainfall at the desired fine scales (1 km2 and 5 min). Moreover, when fed to two nowcast models, the conditional merging doesn't only increase the predictability of storms from 20 min to longer than 1 h, but as well it improves the agreement of radar based QPF with the gauge measurements. The results are drawn from 110 events observed in the period 2000–2018 by the Hannover Radar (Germany) in an area with a radius of 115 km, where 100 recording gauges were available. As the urban hydrological models are commonly validated on gauge measurements, nowcasting with conditionally merged data, provides a useful tool for urban flood prediction for lead times up to 1 h.

AB - Accurate Quantitative Precipitation Forecasts (QPF) at high spatial and temporal resolution are a perquisite for urban flood prediction. Commonly Lagrangian extrapolation of the rainfall patterns recognized by radar data, forms the basis of such forecasts for the near future (up to 2 h lead time) – referred here as nowcasting. Nevertheless, due to the intermittent nature of the rainfall at such fine scales, the predictability of storms is limited to about 20 min. The predictability loss is caused mainly by the inability of the radar to capture the true rainfall field and because the Lagrangian Persistence is unable to model the temporal evolution of the rainfall field. In this study we focus on the first problem, on how to extend the predictability limit of rainfall at such scales by improving the rainfall field fed into the nowcast model. To overcome the errors associated with the radar intensities, merging techniques between radar and gauge measurements are advised. Among different employed techniques (mean field bias, kriging with external drift and quantile mapping based correction) the conditional merging between the radar and rainfall-gauge measurements seems to capture at best the spatial and temporal patterns of the rainfall at the desired fine scales (1 km2 and 5 min). Moreover, when fed to two nowcast models, the conditional merging doesn't only increase the predictability of storms from 20 min to longer than 1 h, but as well it improves the agreement of radar based QPF with the gauge measurements. The results are drawn from 110 events observed in the period 2000–2018 by the Hannover Radar (Germany) in an area with a radius of 115 km, where 100 recording gauges were available. As the urban hydrological models are commonly validated on gauge measurements, nowcasting with conditionally merged data, provides a useful tool for urban flood prediction for lead times up to 1 h.

KW - Conditional merging

KW - Lagrangian persistence

KW - Radar data

KW - Rainfall nowcast

KW - Urban flood forecast

UR - http://www.scopus.com/inward/record.url?scp=85099438146&partnerID=8YFLogxK

U2 - 10.1016/j.jhydrol.2020.125931

DO - 10.1016/j.jhydrol.2020.125931

M3 - Article

AN - SCOPUS:85099438146

VL - 594

JO - Journal of hydrology

JF - Journal of hydrology

SN - 0022-1694

M1 - 125931

ER -