Ensembles of radar nowcasts and COSMO-DE-EPS for urban flood management

Jasper-Tönnies, Alrun; Hellmers, Sandra; Einfalt, Thomas; Strehz, Alexander; Fröhle, Peter

doi:10.2166/wst.2018.079

Cited by 11 publications

(8 citation statements)

References 14 publications

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“…There is a broad range of literature on merging techniques of various complexity (Golding, ; Atencia et al ., ; Hwang et al ., ; Jasper‐Tönnies et al ., ). For instance, Atencia et al .…”

Section: Methodsmentioning

confidence: 97%

NowPrecip: localized precipitation nowcasting in the complex terrain of Switzerland

Sideris

Foresti

Nerini

et al. 2020

Quart J Royal Meteoro Soc

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The new precipitation nowcasting system “NowPrecip” is introduced and its methodology is described in detail. NowPrecip is an area‐tracking application, able to generate ensembles of precipitation nowcasts. It supports the paradigm of seamless forecasting: nowcasts start from the most recent radar observation and evolve smoothly, through merging, towards the numerical weather prediction (NWP) ensemble members. NowPrecip relies on the following three pillars. (a) A geostatistics‐based optical flow algorithm, named “NowTrack”, able to determine the scales of interest automatically and equipped with an outlier control scheme. Such a scheme is particularly useful in capturing situations like fine‐scale rotation accurately. (b) A localized architecture motivated by the need to address scenarios where distinct rainfall patterns characterize different parts of the domain. This is mostly relevant for complex terrains like Switzerland, but is also useful for areal nowcasting operating on extensive regions. (c) Techniques to compute and incorporate localized growth and decay into the computation of nowcasts. This is especially useful for Alpine terrain, where there is often systematic growth and decay associated with orographic features. The growth and decay factors are based on analysis of the available NWP product, allowing for a more efficient coupling between the nowcasting sequence and the numerical model output. The main scope of this work is to present the methodology of NowPrecip in detail, but a verification of several representative events is also provided, using both deterministic and probabilistic measures.

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Section: Methodsmentioning

confidence: 97%

NowPrecip: localized precipitation nowcasting in the complex terrain of Switzerland

Sideris

Foresti

Nerini

et al. 2020

Quart J Royal Meteoro Soc

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show abstract

“…Unfortunately, the high level of detail leads to long calculation times of several hours or even days. In parallel to this, using the currently available prediction models, convective heavy rainfall events triggering pluvial flash floods can only be predicted with adequate accuracy with lead times of up to two hours [6][7][8]. Due to these facts, hydrodynamic calculation models are currently unsuitable for real-time applications and thus limited to historical event simulation.…”

Section: Introductionmentioning

confidence: 99%

A Temporal Fusion Transformer Model to Forecast Overflow from Sewer Manholes during Pluvial Flash Flood Events

Burrichter,

Koltermann da Silva,

Niemann

et al. 2024

Hydrology

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This study employs a temporal fusion transformer (TFT) for predicting overflow from sewer manholes during heavy rainfall events. The TFT utilised is capable of forecasting overflow hydrographs at the manhole level and was tested on a sewer network with 975 manholes. As part of the investigations, the TFT was compared to other deep learning architectures to evaluate its predictive performance. In addition to precipitation measurements and forecasts, the issue of how the additional consideration of measurements in the sewer network as model inputs impacts forecast accuracy was investigated. A varying number of sensors and different measurement signals were compared. The results indicate high performance for the TFT compared to other model architectures like a long short-term memory (LSTM) network or a dual-stage attention-based recurrent neural network (DA-RNN). Additionally, results suggest that considering a single measuring point at the outlet of the sewer network instead of an entire measuring network yields better forecasts. One possible explanation is the high correlation between measurements, which increases model and training complexity without adding much value.

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“…As already discussed in Bowler et al (2006) and Jensen et al (2015), these initialization errors cause the radar forecast to be used only for short forecast horizons (up to 3 h), and that is why they are typically referred to as nowcasts. For longer lead times a blending between NWP and radar-based nowcasts should be used instead (Codo and Rico-Ramirez, 2018;Foresti et al, 2016;Jasper-Tönnies et al, 2018). Nonetheless, for short forecast horizons up to 2-3 h, the radar nowcast remains the best product for pluvial flood simulations as it outperforms the NWP one (Berenguer et al, 2012;Jensen et al, 2015;Lin et al, 2005;Zahraei et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

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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Ensembles of radar nowcasts and COSMO-DE-EPS for urban flood management

Cited by 11 publications

References 14 publications

NowPrecip: localized precipitation nowcasting in the complex terrain of Switzerland

NowPrecip: localized precipitation nowcasting in the complex terrain of Switzerland

A Temporal Fusion Transformer Model to Forecast Overflow from Sewer Manholes during Pluvial Flash Flood Events

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

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