The value of weather radar data for the estimation of design storms – an  analysis for the Hannover region

Haberlandt, Uwe; Berndt, Christian

doi:10.5194/piahs-373-81-2016

Cited by 8 publications

(7 citation statements)

References 10 publications

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“…Yet, considerable uncertainties remain in radar-derived quantitative estimates of precipitation intensities at surface level (Berne & Krajewski, 2013). In convective events and on subhourly to hourly time scales, extreme intensities are often severely underestimated (Bárdossy & Pegram, 2017;Haberlandt & Berndt, 2016;Kann et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

“…Precipitation at very small scales below 1 km can be studied in confined, campaign-type study settings (Goodrich et al, 1995;Pedersen et al, 2010;Peleg et al, 2013), and at large scales, uncertainties generally decrease. But on the 1 to 10 km scale, just below most operational rain gauge network densities and in the gray zone of climate models, extreme convective surface area precipitation is still subject to large uncertainties (Lind et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

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Strong Dependence of Extreme Convective Precipitation Intensities on Gauge Network Density

Schroeer

Kirchengast

Sungmin

2018

Geophysical Research Letters

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Extreme convective precipitation on subhourly scales is notoriously misrepresented in rain gauge‐based observations, but uncertainties are weakly quantified at the 1 to 30 km scale. We employ a unique observing network, the high‐density WegenerNet and surrounding operational rain gauge network in southeastern Austria, to sample convective precipitation extremes at these scales. By systematically constructing lower‐density networks, we explore how estimated maximum area precipitation depends on observing station density. Using subhourly to hourly temporal resolution, we find a d−0.5(±0.1) power law decay of the event maximum area precipitation over distances d from 1 to 30 km, showing that operational gauge networks underrate extreme convective precipitation falling over small areas. Furthermore, extremes at point scale are found underestimated by operational networks by about 20%. We consider the dependencies representative for short‐duration convective events over similar regions at midlatitudes and the results valuable for high‐resolution climate model evaluation.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Strong Dependence of Extreme Convective Precipitation Intensities on Gauge Network Density

Schroeer

Kirchengast

Sungmin

2018

Geophysical Research Letters

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“…They found for Louisiana (USA) that the relatively short period (13 years) explains the high uncertainty of the analysis, that the index flood method is recommended and that a systematic underestimation is associated with the radar products (its spatial resolution is 4 × 4 km). Haberlandt and Berndt (2016) found that the operational DWD product is only suitable for studies on longer durations after bias correction. Using a 10-year high-resolution radar rainfall dataset, Wright et al (2014b) performed a RFA using stochastic storm transposition.…”

Section: Introductionmentioning

confidence: 99%

Regional frequency analysis of extreme rainfall in Belgium based on radar estimates

Goudenhoofdt

Delobbe

Willems

2017

Hydrol. Earth Syst. Sci.

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Abstract. In Belgium, only rain gauge time series have been used so far to study extreme rainfall at a given location. In this paper, the potential of a 12-year quantitative precipitation estimation (QPE) from a single weather radar is evaluated. For the period 2005-2016, 1 and 24 h rainfall extremes from automatic rain gauges and collocated radar estimates are compared. The peak intensities are fitted to the exponential distribution using regression in Q-Q plots with a threshold rank which minimises the mean squared error. A basic radar product used as reference exhibits unrealistic high extremes and is not suitable for extreme value analysis. For 24 h rainfall extremes, which occur partly in winter, the radar-based QPE needs a bias correction. A few missing events are caused by the wind drift associated with convective cells and strong radar signal attenuation. Differences between radar and gauge rainfall values are caused by spatial and temporal sampling, gauge underestimations and radar errors. Nonetheless the fit to the QPE data is within the confidence interval of the gauge fit, which remains large due to the short study period. A regional frequency analysis for 1 h duration is performed at the locations of four gauges with 1965-2008 records using the spatially independent QPE data in a circle of 20 km. The confidence interval of the radar fit, which is small due to the sample size, contains the gauge fit for the two closest stations from the radar. In Brussels, the radar extremes are significantly higher than the gauge rainfall extremes, but similar to those observed by an automatic gauge during the same period. The extreme statistics exhibit slight variations related to topography. The radar-based extreme value analysis can be extended to other durations.

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“…Apart from station data, temporally and spatially homogenized and station-adjusted precipitation data from weather radar are becoming increasingly available and have been used in the analysis of design storms (e.g., Overeem et al, 2009;Haberlandt and Berndt, 2016;Panziera et al, 2016;Pöschmann et al, 2021). The main advantage of using weather radar data is the provision of a spatially complete picture of storm events on various temporal and spatial scales, as many short-term and small-scale storm events are not captured by the typical network of precipitation gauges (Lengfeld et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Enhancing the usability of weather radar data for the statistical analysis of extreme precipitation events

Haensler

Weiler²

2022

Hydrol. Earth Syst. Sci.

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Abstract. Spatially explicit quantification on design storms is essential for flood risk assessment and planning. Due to the limited temporal data availability from weather radar data, design storms are usually estimated on the basis of rainfall records of a few precipitation stations only that have a substantially long time coverage. To achieve a regional picture, these station-based estimates are spatially interpolated, incorporating a large source of uncertainty due to the typical low station density, in particular for short event durations. In this study we present a method to estimate spatially explicit design storms with a return period of up to 100 years on the basis of statistically extended weather radar precipitation estimates, based on the ideas of regional frequency analyses and subsequent bias correction. Associated uncertainties are quantified using an ensemble-sampling approach and event-based bootstrapping. With the resulting dataset, we compile spatially explicit design storms for various return periods and event durations for the federal state of Baden Württemberg, Germany. We compare our findings with two reference datasets based on interpolated station estimates. We find that the transition in the spatial patterns of the design storms from a rather random (short-duration events, 15 min) to a more structured, orographically influenced pattern (long-duration events, 24 h) seems to be much more realistic in the weather-radar-based product. However, the absolute magnitude of the design storms, although bias-corrected, is still generally lower in the weather radar product, which should be addressed in future studies in more detail.

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The value of weather radar data for the estimation of design storms – an analysis for the Hannover region

Cited by 8 publications

References 10 publications

Strong Dependence of Extreme Convective Precipitation Intensities on Gauge Network Density

Strong Dependence of Extreme Convective Precipitation Intensities on Gauge Network Density

Regional frequency analysis of extreme rainfall in Belgium based on radar estimates

Enhancing the usability of weather radar data for the statistical analysis of extreme precipitation events

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