Applying bias correction for merging rain gauge and radar data

Rabiei, Ehsan; Haberlandt, Uwe

doi:10.1016/j.jhydrol.2015.01.020

Cited by 61 publications

(41 citation statements)

References 24 publications

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“…The interpolation can be performed by many different spatial interpolation methods, e.g. variations of Kriging (Krajewski, 1987;Todini, 2001;Sinclair and Pegram, 2005;Haberlandt, 2007;Goudenhoofdt and Delobbe, 2009;Velasco-Forero et al, 2009;He et al, 2011;Berndt et al, 2014;Rabiei and Haberlandt, 2015), or by inverse distance weighting or Thiessen polygon weighting Haberlandt, 2007). The Krigingbased methods rely on variograms for describing the spatial dependence in rainfall fields and are in general more computationally demanding than weighting methods.…”

Section: Bias Adjustment Against Ground Observationsmentioning

confidence: 99%

Weather radar rainfall data in urban hydrology

Thorndahl

Einfalt

Willems

et al. 2017

Hydrol. Earth Syst. Sci.

159

108

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Abstract. Application of weather radar data in urban hydrological applications has evolved significantly during the past decade as an alternative to traditional rainfall observations with rain gauges. Advances in radar hardware, data processing, numerical models, and emerging fields within urban hydrology necessitate an updated review of the state of the art in such radar rainfall data and applications. Three key areas with significant advances over the past decade have been identified: (1) temporal and spatial resolution of rainfall data required for different types of hydrological applications, (2) rainfall estimation, radar data adjustment and data quality, and (3) nowcasting of radar rainfall and real-time applications. Based on these three fields of research, the paper provides recommendations based on an updated overview of shortcomings, gains, and novel developments in relation to urban hydrological applications. The paper also reviews how the focus in urban hydrology research has shifted over the last decade to fields such as climate change impacts, resilience of urban areas to hydrological extremes, and online prediction/warning systems. It is discussed how radar rainfall data can add value to the aforementioned emerging fields in current and future applications, but also to the analysis of integrated water systems.

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Section: Bias Adjustment Against Ground Observationsmentioning

confidence: 99%

Weather radar rainfall data in urban hydrology

Thorndahl

Einfalt

Willems

et al. 2017

Hydrol. Earth Syst. Sci.

159

108

View full text Add to dashboard Cite

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“…For this reason, a cut-off threshold (TH) approach has been commonly applied to adjust the wet-day frequency in the bias correction for daily precipitation using 10 different criteria (Jakob Themeßl et al, 2011;Kim et al, 2015aKim et al, , 2015bNyunt et al, 2016;Piani et al, 2010;Rabiei and Haberlandt, 2015;Schmidli et al, 2006;Volosciuk et al, 2017). For example, Piani et al(2010) and Volosciuk et al (2017) applied 0.1 mm/day as the threshold, whereas the wet-day frequency of simulated precipitation was set equal to that of the observed (Kim et al, 2015a(Kim et al, , 2015bNyunt et al, 2016).…”

Section: Wet-day Frequency Correction Schemementioning

confidence: 99%

“…For example, Piani et al(2010) and Volosciuk et al (2017) applied 0.1 mm/day as the threshold, whereas the wet-day frequency of simulated precipitation was set equal to that of the observed (Kim et al, 2015a(Kim et al, , 2015bNyunt et al, 2016). Rabiei and Haberlandt (2015) compared five different thresholds (0 mm/hr, 0.02 mm/h, 0.05 mm/h, 15 0.07 mm/h, 0.1 mm/h) for spatial bias correction of hourly radar data and concluded that the threshold 0.05 mm/h performed the best among the five in terms of the reduction of biases.…”

Section: Wet-day Frequency Correction Schemementioning

confidence: 99%

“…More generally, cumulative distribution functions (CDFs) of the modelled data are mapped to that of the 10 observed, which is considered "true" (Rabiei and Haberlandt, 2015;Teutschbein and Seibert, 2012). In other words, the distribution of simulated values is fitted to the true distribution, the relationship of which is established in the advanced stages of bias correction.…”

Section: Statistical Bias Correction Model: Qm With a Composite Distrmentioning

confidence: 99%

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Exploring the Long-Term Reanalysis of Precipitation and the Contribution of Bias Correction to the Reduction of Uncertainty over South Korea: A Composite Gamma-Pareto Distribution Approach to the Bias Correction

Kim

Kwon

Han

2018

Preprint

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Abstract. The long-term record of precipitation data plays an important role in climate impact studies.The local observation is often considered to be "the truth" in regional-scale analyses, but the long-term 10 meteorological record for a given catchment is very limited. Recently, ERA-20c, a century-long reanalysis of the data has been published by the European Centre for Medium-Range Weather Forecasts (ECMWF), which includes daily precipitation over the whole 20 th century with high spatial resolution of 0.125°× 0.125°. Preliminary studies have already indicated that the ERA-20c can reproduce the mean reasonably well, but rainfall intensity was underestimated and wet-day frequency was overestimated. The primary 15 focus of this study was to expand our sample size significantly for extreme rainfall analysis. Thus, we first adopted a relatively simple approach to adjust the frequency of wet-days by imposing an optimal lower threshold. We found that the systematic errors are fairly well captured by the conventional quantile mapping method with a gamma distribution, but the extremes in daily precipitation are still somewhat underestimated. In such a context, we introduced a quantile mapping approach based on a composite 20 distribution of a generalized Pareto distribution for the upper tail (e.g. 95 th and 99 th percentile), and a gamma distribution for the interior part of the distribution. The proposed composite distributions provide a significant reduction of the biases compared with that of the conventional method for the extremes. We suggest a new interpolation method based on the parameter contour map for bias correction in ungauged catchments. The strength of this approach is that one can easily produce the bias-corrected daily 25 precipitation in ungauged or poorly gauged catchments. A comparison of the corrected datasets using contour maps shows that the proposed modelling scheme can reliably reduce the systematic bias at a grid 2 point that is not used in the process of parameter estimation. In particular, the contour map with the 99 th percentile shows a more accurate representation of the observed daily rainfall than other combinations.The findings in this study suggest that the proposed approach can provide a useful alternative to readers who consider the bias correction of a regional-scale modelled data with a limited network of rain gauges.Although the study has been carried out in South Korea, the methodology has its potential to be applied 5 in other parts of the world.

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“…Merging [21] have been used to transform the radar data according to gauge data, Box-Cox transformation, Q-Q transformation [30]. In this study, the Mean…”

Section: Correction Of Radar Datamentioning

confidence: 99%

The Influence of Rain Gauge Network Density on the Performance of a Hydrological Model

Andiego¹,

Waseem²,

Usman³

et al. 2018

CWEEE

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Rain gauge data suffers from spatial errors because of precipitation variability within short distances and due to sparse or irregular network. Use of interpolation is often unreliable to evaluate due to the aforementioned irregular sparse networks. This study is carried out in the Nette River catchment of Lower Saxony to alleviate the problem of using gauge data to measure the performance of interpolation. Radar precipitation data was extracted in the positions of 53 rain gauge stations, which are distributed throughout the range of the weather surveillance radar (WSR). Since radar data traditionally suffers from temporal errors, it was corrected using the Mean Field Bias (MFB) method by utilizing the rain gauge data and then further used as the reference precipitation in the study. The performances of Inverse Distance Weighting (IDW) and Ordinary Kriging (OK) interpolation methods by means of cross validation were assessed. Evaluation of the effect of the gauge densities on HBV-IWW hydrological model was achieved by comparing the simulated discharges for the two interpolation methods and corresponding densities against the simulated discharge of the reference precipitation data. Interpolation performance in winter was much better than summer for both interpolation methods. Furthermore, Ordinary Kriging performed marginally better than Inverse Distance Weighting in both seasons. In case of areal precipitation, progressive improvement in performance with increase in gauge density for both interpolation methods was observed, but Inverse Distance Weighting was found more consistent up to higher densities. Comparison showed that Ordinary Kriging outperformed Inverse Distance Weighting only up to 70% density, beyond which the performance is equal. The hydrological modelling results are similar to that of areal precipitation except that for both methods, there was no improvement in performance beyond the 50% gauge density.How to cite this paper: Andiego, G., Waseem, M., Usman, M. and Mani, N. (2018) The Influence of Rain Gauge Network Density on the Performance of a Hydrological Model. Computational Water, Energy, and Environmental Engineering, 7, 27-50.

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Applying bias correction for merging rain gauge and radar data

Cited by 61 publications

References 24 publications

Weather radar rainfall data in urban hydrology

Weather radar rainfall data in urban hydrology

Exploring the Long-Term Reanalysis of Precipitation and the Contribution of Bias Correction to the Reduction of Uncertainty over South Korea: A Composite Gamma-Pareto Distribution Approach to the Bias Correction

The Influence of Rain Gauge Network Density on the Performance of a Hydrological Model

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