Long-term analysis of gauge-adjusted radar rainfall accumulations at European scale

Park, Shinju; Berenguer, Marc; Sempere‐Torres, Daniel

doi:10.1016/j.jhydrol.2019.03.093

Cited by 44 publications

(39 citation statements)

References 24 publications

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“…The choice for these periods was based on discharge observation availability and quality. The employed SOBEK RR(-CF) model (Stelling and Duinmeijer, 2003;Stelling and Verwey, 2006;Prinsen et al, 2010) is semi-distributed, and therefore we used subcatchment-averaged rainfall sums from the gridded radar QPE. The four basins with a SOBEK model have the following number of sub-catchments: 7 for Gouwepolder, 1 for Beemster, 25 for Delfland and 23 for Linde.…”

Section: Hydrometeorological Applicationmentioning

confidence: 99%

A climatological benchmark for operational radar rainfall bias reduction

Imhoff

Brauer

Heeringen

et al. 2021

Hydrol. Earth Syst. Sci.

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Abstract. The presence of significant biases in real-time radar quantitative precipitation estimations (QPEs) limits its use in hydrometeorological forecasting systems. Here, we introduce CARROTS (Climatology-based Adjustments for Radar Rainfall in an OperaTional Setting), a set of fixed bias reduction factors, which vary per grid cell and day of the year. The factors are based on a historical set of 10 years of 5 min radar and reference rainfall data for the Netherlands. CARROTS is both operationally available and independent of real-time rain gauge availability and can thereby provide an alternative to current QPE adjustment practice. In addition, it can be used as benchmark for QPE algorithm development. We tested this method on the resulting rainfall estimates and discharge simulations for 12 Dutch catchments and polders. We validated the results against the operational mean field bias (MFB)-adjusted rainfall estimates and a reference dataset. This reference consists of the radar QPE, that combines an hourly MFB adjustment and a daily spatial adjustment using observations from 32 automatic and 319 manual rain gauges. Only the automatic gauges of this network are available in real time for the MFB adjustment. The resulting climatological correction factors show clear spatial and temporal patterns. Factors are higher away from the radars and higher from December through March than in other seasons, which is likely a result of sampling above the melting layer during the winter months. The MFB-adjusted QPE outperforms the CARROTS-corrected QPE when the country-average rainfall estimates are compared to the reference. However, annual rainfall sums from CARROTS are comparable to the reference and outperform the MFB-adjusted rainfall estimates for catchments away from the radars, where the MFB-adjusted QPE generally underestimates the rainfall amounts. This difference is absent for catchments closer to the radars. QPE underestimations are amplified when used in the hydrological model simulations. Discharge simulations using the QPE from CARROTS outperform those with the MFB-adjusted product for all but one basin. Moreover, the proposed factor derivation method is robust. It is hardly sensitive to leaving individual years out of the historical set and to the moving window length, given window sizes of more than a week.

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Section: Hydrometeorological Applicationmentioning

confidence: 99%

A climatological benchmark for operational radar rainfall bias reduction

Imhoff

Brauer

Heeringen

et al. 2021

Hydrol. Earth Syst. Sci.

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“…Park et al [23] have used the OPERA composites to assess flash flood hazard induced by heavy rainfalls in European scales, together with the European Flood Awareness System (EFAS). So far, the quantitative accuracy of the OPERA composites alone has not been high enough for their needs, but they have combined the point accuracy of gauge data and the superior resolution of the radar data.…”

Section: Data For Usersmentioning

confidence: 99%

OPERA the Radar Project

et al. 2019

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The Operational Program on the Exchange of Weather Radar Information (OPERA) has co-ordinated radar co-operation among national weather services in Europe for more than 20 years. It has introduced its own, manufacturer-independent data model, runs its own data center, and produces Pan-European radar composites. The applications using this data vary from data assimilation to flood warnings and the monitoring of animal migration. It has used several approaches to provide a homogeneous combination of disparate raw data and to indicate the reliability of its products. In particular, if a pixel shows no precipitation, it is important to know if that pixel is dry or if the measurement was missing.

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“…Ground rainfall estimates using rain gauges is a traditional way of the most common methods (Brauer et al 2016) that is used to correct the bias of satellite rainfall estimates (Ma et al 2019;Park et al 2019) for water resources application and related studies. For example, it is as the input for hydrological modeling in basin scale (Andersen et al 2001) and for modeling base ow (Becker and Braun 1999).…”

Section: Introductionmentioning

confidence: 99%

Performance of Daily Satellite-Based Rainfall in Groundwater Basin of Merapi Aquifer System, Yogyakarta

Rahmawati

Rahayu²,

Yuliasari³

2021

Preprint

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Evaluation of the performance of daily satellite-based rainfall (CMORPH, CHIRPS, GPM IMERG, and TRMM) was done to obtain applicable satellite rainfall estimates in groundwater basin of Merapi Aquifer System (MAS). Performance of satellite data was assessed by applying descriptive statistics, categorical statistics, and bias decomposition on the basis of daily rainfall intensity classification. This classification is possible to measure the performance of daily satellite-based rainfall in much detail.CM (CMORPH) has larger underestimation compared to other satellite-based rainfall. This satellite-based rainfall also mostly has the largest RMSE, while CHR (CHIRPS) is the lowest. CM has a good performance to detect no rain, while IMR (GPM-IMERG) has the worst performance. IMR and CHR have a good performance to detect light and moderate rain. Both of them have larger H frequencies and lower MB values compared to other satellite products. CHR mostly has a good performance compared to TR (TRMM) especially on wet periods. CM, IMR, and TR mostly have a good performance on dry periods, while CHR on wet periods. CM mostly has the largest MB and lowest AHB values. CM and CHR have better accuracy to estimate rain amount compared to IMR and TR. All in all, all 4 satellite-based rainfall has large discrepancy compared with rain gauge data along mountain range where orographic rainfall usually occurs in wet periods. Hence, it is recommended to evaluate satellite-based rainfall with time series of streamflow simulation in hydrological modeling framework by merging rain gauge data with more than one satellite-based rainfall except to merge both IMR and TR together.

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Long-term analysis of gauge-adjusted radar rainfall accumulations at European scale

Cited by 44 publications

References 24 publications

A climatological benchmark for operational radar rainfall bias reduction

A climatological benchmark for operational radar rainfall bias reduction

OPERA the Radar Project

Performance of Daily Satellite-Based Rainfall in Groundwater Basin of Merapi Aquifer System, Yogyakarta

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