Comparing Approaches to Deal With Non‐Gaussianity of Rainfall Data in Kriging‐Based Radar‐Gauge Rainfall Merging

Cecinati, Francesca; Wani, Omar; Rico‐Ramirez, Miguel A.

doi:10.1002/2016wr020330

Cited by 37 publications

(30 citation statements)

References 54 publications

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“…The relatively poor performances of the MF_DM 1 and MF_DM 2 results in Figure 4 and Table 4 are likely due to the assuming of Gaussian rainfall and kriging error distributions by the dynamic merging approach (section 2.3) taken to generate the results. This assumption is invalid (Cecinati et al, 2017;Wang et al, 2015) even in our case of simulated synthetic rainfall values. Thus, in view of this, to improve the MF_DM 1 and MF_DM 2 results, we have attempted logarithmically transforming the raw rainfall observations before applying our methods, as rainfall have been reported to follow more closely a lognormal distribution (Seo et al, 1990;Sinclair & Pegram, 2005).…”

Section: Water Resources Researchmentioning

confidence: 80%

“…Their results showed the blended rainfall fields performed significantly better than satellite-derived estimates and were competitive in their ability to represent wet events. Other past efforts to merge rain gauge data with radar and satellite data include works by Sinclair and Pegram (2005), Hasan et al (2016), Wang et al (2015), and Cecinati et al (2017). In these and other studies, kriging-based methods were the most widely used class of merging algorithms, in particular kriging with external drift (KED) that, in many instances, has been found superior over other algorithms (Boudevillain et al, 2016;Goudenhoofdt & Delobbe, 2009;Jewell & Gaussiat, 2015).…”

Section: Introductionmentioning

confidence: 99%

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Fast Bayesian Regression Kriging Method for Real‐Time Merging of Radar, Rain Gauge, and Crowdsourced Rainfall Data

Yang

2019

Water Resources Research

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Crowdsourcing of rainfall measurements incorporating common citizens as a rich source of data is an emerging concept with huge potential to provide valuable high spatiotemporal resolution rainfall observations. Here we investigate the merging of crowdsourced rainfall data with traditional radar and rain gauge data to maximize their utility. For this purpose, we develop a tailored fast Bayesian regression kriging (FBRK) method combining regression kriging and Laplace approximation in a Bayesian framework. A strength of the FBRK method lies in its ability to capture the differences between rain gauge and crowdsourced measurement errors. Another lies in its fast yet reasonably accurate approximation of the Bayesian posterior, making it suitable to use in real time. We conduct synthetic computer simulations to evaluate the FBRK method alongside three other merging methods. In the simulations, we compare the accuracies of their resulting rainfall estimates, as well as the skill of those estimates as input to a storm water flow forecasting model. In both aspects, we observe the FBRK method to lead to more accurate results and truer representations of the associated uncertainties. However, we also observe the performance of the FBRK method to be sensitive to the choice of the Bayesian prior under certain conditions. Finally, from the synthetic simulations, we find merging crowdsourced data with traditional data to lead to more accurate estimation of the ground truth rainfall field and, subsequently, more accurate flow forecasts (though only when an adequate merging method, e.g., the FBRK method, is used), and the results to be fairly robust to bias in the input crowdsourced data.

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Section: Water Resources Researchmentioning

confidence: 80%

Section: Introductionmentioning

confidence: 99%

Fast Bayesian Regression Kriging Method for Real‐Time Merging of Radar, Rain Gauge, and Crowdsourced Rainfall Data

Yang

2019

Water Resources Research

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“…However, this is not the case in our case study, as we know that the probability distribution of rainfall is not well described by the kriging Gaussian probability distribution. The fact that the Gaussian approximation is not a good representation of rainfall probability distribution is a known limitation of kriging methods [34,42]. However, the rank histograms can still be informative.…”

Section: Case Study Results and Discussionmentioning

confidence: 99%

“…Kriging with External Drift with radar rainfall as covariate-KED 4. Kriging with External Drift for uncertain data with radar rainfall as covariate-KEDUD For the formulations of ordinary kriging (OK) and kriging with external drift (KED), the reader is addressed to Cecinati et al [34].…”

Section: Kriging Methodsmentioning

confidence: 99%

Considering Rain Gauge Uncertainty Using Kriging for Uncertain Data

et al. 2018

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In urban hydrological models, rainfall is the main input and one of the main sources of uncertainty. To reach sufficient spatial coverage and resolution, the integration of several rainfall data sources, including rain gauges and weather radars, is often necessary. The uncertainty associated with rain gauge measurements is dependent on rainfall intensity and on the characteristics of the devices. Common spatial interpolation methods do not account for rain gauge uncertainty variability. Kriging for Uncertain Data (KUD) allows the handling of the uncertainty of each rain gauge independently, modelling space- and time-variant errors. The applications of KUD to rain gauge interpolation and radar-gauge rainfall merging are studied and compared. First, the methodology is studied with synthetic experiments, to evaluate its performance varying rain gauge density, accuracy and rainfall field characteristics. Subsequently, the method is applied to a case study in the Dommel catchment, the Netherlands, where high-quality automatic gauges are complemented by lower-quality tipping-bucket gauges and radar composites. The case study and the synthetic experiments show that considering measurement uncertainty in rain gauge interpolation usually improves rainfall estimations, given a sufficient rain gauge density. Considering measurement uncertainty in radar-gauge merging consistently improved the estimates in the tested cases, thanks to the additional spatial information of radar rainfall data but should still be used cautiously for convective events and low-density rain gauge networks.

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“…Precipitation is a highly skewed, heteroscedastic and intermittent field in nature and therefore frequently contradicts the assumptions of data normality. This forces data transformation using analytical or numerical techniques, which cannot always satisfy the assumptions on normality and homoscedasticity [31]. Geostatistical interpolation methods nonetheless, have broadly been applied in the design, evaluation and monitoring of rain-gauge observational networks.…”

Section: Introductionmentioning

confidence: 99%

Generation of Monthly Precipitation Climatologies for Costa Rica Using Irregular Rain-Gauge Observational Networks

Mendez

Calvo-Valverde

Maathuis

et al. 2019

Water

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Precipitation climatologies for the period 1961–1990 were generated for all climatic regions of Costa Rica using an irregular rain-gauge observational network comprised by 416 rain-gauge stations. Two sub-networks were defined: a high temporal resolution sub-network (HTR), including stations having at least 20 years of continuous records during the study period (157 in total); and a high spatial resolution sub-network (HSR), which includes all HTR-stations plus those stations with less than 20 years of continuous records (416 in total). Results from the kriging variance reduction efficiency (KRE) objective function between the two sub-networks, show that ordinary kriging (OK) is unable to fully explain the spatio-temporal variability of precipitation within most climatic regions if only stations from the HTR sub-network are used. Results also suggests that in most cases, it is beneficial to increase the density of the rain-gauge observational network at the expense of temporal fidelity, by including more stations even though their records may not represent the same time step. Thereafter, precipitation climatologies were generated using seven deterministic (IDW, TS2, TS2PARA, TS2LINEAR, TPS, MQS and NN) and two geostatistical (OK and KED) interpolation methods. Performance of the various interpolation methods was evaluated using cross validation technique, selecting the mean absolute error (MAE) and the root-mean square error (RMSE) as agreement metrics. Results suggest that IDW is marginally superior to OK and KED for most climatic regions. The remaining deterministic methods however, considerably deviate from IDW, which suggests that these methods are incapable of properly capturing the true-nature of spatial precipitation patterns over the considered climatic regions. The final generated IDW climatology was then validated against the Global Precipitation Climatology Centre (GPCC), Climate Research Unit (CRU) and WorldClim datasets, in which overall spatial and temporal coherence is considered satisfactory, giving assurance about the use this new climatology in the development of local climate impact studies.

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Comparing Approaches to Deal With Non‐Gaussianity of Rainfall Data in Kriging‐Based Radar‐Gauge Rainfall Merging

Cited by 37 publications

References 54 publications

Fast Bayesian Regression Kriging Method for Real‐Time Merging of Radar, Rain Gauge, and Crowdsourced Rainfall Data

Fast Bayesian Regression Kriging Method for Real‐Time Merging of Radar, Rain Gauge, and Crowdsourced Rainfall Data

Considering Rain Gauge Uncertainty Using Kriging for Uncertain Data

Generation of Monthly Precipitation Climatologies for Costa Rica Using Irregular Rain-Gauge Observational Networks

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