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

Shehu, Bora; Haberlandt, Uwe

doi:10.1016/j.jhydrol.2020.125931

Cited by 19 publications

(14 citation statements)

References 52 publications

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“…Nevertheless, studies have found that, for fine spatial (1 km 2 ) and temporal (5 min) scales, the Lagrangian persistence can yield reliable results up to 20-30 min lead time, which is also known in the literature as the predictability limit of rainfall at such scales (Grecu and Krajewski, 2000;Kato et al, 2017;Ruzanski et al, 2011). In object-based radar nowcasting, this predictability limit can be extended up to 1 h for stratiform events and up to 30-45 min for convective events if a better radar product (merged with rain-gauge data) is fed into the nowcast model (Shehu and Haberlandt, 2021). Past these lead times, the errors due to the growth/decay and dissipation of the storms dominate.…”

Section: Introductionmentioning

confidence: 92%

Improving radar-based rainfall nowcasting by a nearest-neighbour approach – Part 1: Storm characteristics

Shehu

Haberlandt

2022

Hydrol. Earth Syst. Sci.

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Abstract. 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∼115 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.

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Section: Introductionmentioning

confidence: 92%

Improving radar-based rainfall nowcasting by a nearest-neighbour approach – Part 1: Storm characteristics

Shehu

Haberlandt

2022

Hydrol. Earth Syst. Sci.

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“…Correspondingly, more studies are required to assess the performance of GPM IMERG against other satellite and reanalysis products to find the advantages and limitations of the GPM satellite. Furthermore, other precipitation specific properties such as spatial and temporal heterogeneity, spatial and temporal intermittence, extreme variability and multi-fractal comparisons is necessary to overcome substantial uncertainties rainfall estimations [57][58][59]. Cristiano et al [60] also depicted in their review that the rainfall variability may need wider range of conditions and scenario based in observational datasets for urban hydrological basins, which need proper analysis for development of better hydrological response system with higher sensitivity.…”

Section: Future Studies and Shortcomingsmentioning

confidence: 99%

GPM Annual and Daily Precipitation Data for Real-Time Short-Term Nowcasting: A Pilot Study for a Way Forward in Data Assimilation

Wang

Kong

Yang

et al. 2021

Water

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This study explores the quality of data produced by Global Precipitation Measurement (GPM) and the potential of GPM for real-time short-term nowcasting using MATLAB and the Short-Term Ensemble Prediction System (STEPS). Precipitation data obtained by rain gauges during the period 2015 to 2017 were used in this comparative analysis. The results show that the quality of GPM precipitation has different degrees efficacies at the national scale, which were revealed at the performance analysis stage of the study. After data quality checking, five representative precipitation events were selected for nowcasting evaluation. The GPM estimated precipitation compared to a 30 min forecast using STEPS precipitation nowcast results, showing that the GPM precipitation data performed well in nowcasting between 0 to 120 min. However, the accuracy and quality of nowcasting precipitation significantly reduced with increased lead time. A major finding from the study is that the quality of precipitation data can be improved through blending processes such as kriging with external drift and the double-kernel smoothing method, which enhances the quality of nowcast over longer lead times.

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“…The radar data were corrected from the static clutters and erroneous beams and then converted to Cartesian Coordinate system (1 km 2 and 5 min) as described in Berndt et al (2014). Additionally, following the results from Shehu & Haberlandt (2021), a conditional merging between the radar data and 100 gauge recording with the radar range at 5 min time steps was performed. The period from 2000 to 2018 was used as a basis for this investigation, from which 110 events with different characteristics were extracted (see Shehu & Haberlandt (2021) or Shehu (2020)).…”

Section: Study Area and Datamentioning

confidence: 99%

“…Additionally, following the results from Shehu & Haberlandt (2021), a conditional merging between the radar data and 100 gauge recording with the radar range at 5 min time steps was performed. The period from 2000 to 2018 was used as a basis for this investigation, from which 110 events with different characteristics were extracted (see Shehu & Haberlandt (2021) or Shehu (2020)). These events were selected for urban flood purposes, thus contain mainly convective events and few stratiform events.…”

Section: Study Area and Datamentioning

confidence: 99%

“…For object-oriented radar based nowcast, this predictability limit can be extended up to 1 hour for stratiform events and up to 30-45min for convective events if a better radar product (merged with rain gauge data) is fed into the nowcast model (Shehu & Haberlandt, 2021). Past these lead times, the errors due to the growth/decay and death of the storm govern.…”

Section: Introductionmentioning

confidence: 99%

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

Shehu

Haberlandt

2021

Preprint

Self Cite

View full text Add to dashboard Cite

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.

show abstract

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

Cited by 19 publications

References 52 publications

Improving radar-based rainfall nowcasting by a nearest-neighbour approach – Part 1: Storm characteristics

Improving radar-based rainfall nowcasting by a nearest-neighbour approach – Part 1: Storm characteristics

GPM Annual and Daily Precipitation Data for Real-Time Short-Term Nowcasting: A Pilot Study for a Way Forward in Data Assimilation

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

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