Considering Rain Gauge Uncertainty Using Kriging for Uncertain Data

Cecinati, Francesca; Moreno-Rodenas, Antonio; Rico‐Ramirez, Miguel A.; Veldhuis, Marie-Claire ten; Langeveld, Jeroen

doi:10.3390/atmos9110446

Cited by 27 publications

(17 citation statements)

References 37 publications

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“…Firstly, most data available are not real-time [14]- [18]. Secondly, these data were not of sufficiently high resolution [11], [28]. The most notable example of the latter is rainfalls being recorded at specific monitoring stations, normally located sparingly.…”

Section: A Meteorological and Hydrological Datamentioning

confidence: 99%

Flood Forecasting System Based on Integrated Big and Crowdsource Data by Using Machine Learning Techniques

Puttinaovarat

Horkaew

2020

IEEE Access

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Flood is one of the most disruptive natural hazards, responsible for loss of lives and damage to properties. A number of cities are subject to monsoons influences and hence face the disaster almost every year. Early notification of flood incident could benefit the authorities and public to devise both short and long terms preventive measures, to prepare evacuation and rescue mission, and to relieve the flood victims. Geographical locations of affected areas and respective severities, for instances, are among the key determinants in most flood administration. Thus far, an effective means of anticipating flood in advance remains lacking. Existing tools were typically based on manually input and prepared data. The processes were tedious and thus prohibitive for real-time and early forecasts. Furthermore, these tools did not fully exploit more comprehensive information available in current big data platforms. Therefore, this paper proposes a novel flood forecasting system based on fusing meteorological, hydrological, geospatial, and crowdsource big data in an adaptive machine learning framework. Data intelligence was driven by stateof-the-art learning strategies. Subjective and objective evaluations indicated that the developed system was able to forecast flood incidents, happening in specific areas and time frames. It was also later revealed by benchmarking experiments that the system configured with an MLP ANN gave the most effective prediction, with correct percentage, Kappa, MAE and RMSE of 97.93, 0.89, 0.01 and 0.10, respectively. INDEX TERMS Flood forecasting system, big data, machine learning, crowdsource, deep learning. PARAMATE HORKAEW received the B.Eng. degree (Hons.

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Section: A Meteorological and Hydrological Datamentioning

confidence: 99%

Flood Forecasting System Based on Integrated Big and Crowdsource Data by Using Machine Learning Techniques

Puttinaovarat

Horkaew

2020

IEEE Access

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“…Water Resources Research also Cecinati et al (2018) and Mazzetti and Todini (2009) for ways to reformulate the variogram in a typical kriging model to consider variations in the errors of the point rainfall data.…”

Section: 1029/2018wr023857mentioning

confidence: 99%

“…For example, see Hasan et al () and Todini () where the authors had interpolated rainfall data of different sources separately and then merged them according to weights based on their different interpolation variances. See also Cecinati et al () and Mazzetti and Todini () for ways to reformulate the variogram in a typical kriging model to consider variations in the errors of the point rainfall data.…”

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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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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“…These inherent systematic errors are challenging to mitigate by improving radar technology alone. Thus, many researchers chose to blend radar with rain gauges and satellite data to improve the performance, for example, Kriging with External Drift (KED) [24,25], Mean Field Bias Correction (MFB) [26]. A few studies investigated the bias of radar rainfall products in excessive rainfall events [17,20,27,28].…”

Section: Introductionmentioning

confidence: 99%

Cross-Examination of Similarity, Difference and Deficiency of Gauge, Radar and Satellite Precipitation Measuring Uncertainties for Extreme Events Using Conventional Metrics and Multiplicative Triple Collocation

Chen

Gao

et al. 2020

Remote Sensing

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Quantifying uncertainties of precipitation estimation, especially in extreme events, could benefit early warning of water-related hazards like flash floods and landslides. Rain gauges, weather radars, and satellites are three mainstream data sources used in measuring precipitation but have their own inherent advantages and deficiencies. With a focus on extremes, the overarching goal of this study is to cross-examine the similarities and differences of three state-of-the-art independent products (Muti-Radar Muti-Sensor Quantitative Precipitation Estimates, MRMS; National Center for Environmental Prediction gridded gauge-only hourly precipitation product, NCEP; Integrated Multi-satellitE Retrievals for GPM, IMERG), with both traditional metrics and the Multiplicative Triple Collection (MTC) method during Hurricane Harvey and multiple Tropical Cyclones. The results reveal that: (a) the consistency of cross-examination results against traditional metrics approves the applicability of MTC in extreme events; (b) the consistency of cross-events of MTC evaluation results also suggests its robustness across individual storms; (c) all products demonstrate their capacity of capturing the spatial and temporal variability of the storm structures while also magnifying respective inherent deficiencies; (d) NCEP and IMERG likely underestimate while MRMS overestimates the storm total accumulation, especially for the 500-year return Hurricane Harvey; (e) both NCEP and IMERG underestimate extreme rainrates (>= 90 mm/h) likely due to device insensitivity or saturation while MRMS maintains robust across the rainrate range; (g) all three show inherent deficiencies in capturing the storm core of Harvey possibly due to device malfunctions with the NCEP gauges, relative low spatiotemporal resolution of IMERG, and the unusual "hot" MRMS radar signals. Given the unknown ground reference assumption of MTC, this study suggests that MRMS has the best overall performance. The similarities, differences, advantages, and deficiencies revealed in this study could guide the users for emergency response and motivate the community not only to improve the respective sensor/algorithm but also innovate multidata merging methods for one best possible product, specifically suitable for extreme storm events.

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Considering Rain Gauge Uncertainty Using Kriging for Uncertain Data

Cited by 27 publications

References 37 publications

Flood Forecasting System Based on Integrated Big and Crowdsource Data by Using Machine Learning Techniques

Flood Forecasting System Based on Integrated Big and Crowdsource Data by Using Machine Learning Techniques

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

Cross-Examination of Similarity, Difference and Deficiency of Gauge, Radar and Satellite Precipitation Measuring Uncertainties for Extreme Events Using Conventional Metrics and Multiplicative Triple Collocation

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