Bias adjustment and advection interpolation of long-term high resolution radar rainfall series

Thorndahl, Søren Liedtke; Nielsen, Jesper Ellerbæk; Rasmussen, Michael R.

doi:10.1016/j.jhydrol.2013.10.056

Cited by 68 publications

(75 citation statements)

References 63 publications

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“…During recent years, significant progress was made in measuring and forecasting spatial rainfall fields through radar [3,[13][14][15][16][17], as well as numerical weather predictions (NWP) [14,18] for operational and design purposes in urban hydrology and future developments may lead to the application of satellite observations [19]. The spatial information in such data can be exploited for the purpose of distinguishing between different types of rainfall events [20].…”

Section: Introductionmentioning

confidence: 99%

Objective Classification of Rainfall in Northern Europe for Online Operation of Urban Water Systems Based on Clustering Techniques

Löwe

Madsen

McSharry

2016

Water

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This study evaluated methods for automated classification of rain events into groups of "high" and "low" spatial and temporal variability in offline and online situations. The applied classification techniques are fast and based on rainfall data only, and can thus be applied by, e.g., water system operators to change modes of control of their facilities. A k-means clustering technique was applied to group events retrospectively and was able to distinguish events with clearly different temporal and spatial correlation properties. For online applications, techniques based on k-means clustering and quadratic discriminant analysis both provided a fast and reliable identification of rain events of "high" variability, while the k-means provided the smallest number of rain events falsely identified as being of "high" variability (false hits). A simple classification method based on a threshold for the observed rainfall intensity yielded a large number of false hits and was thus outperformed by the other two methods.

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Section: Introductionmentioning

confidence: 99%

Objective Classification of Rainfall in Northern Europe for Online Operation of Urban Water Systems Based on Clustering Techniques

Löwe

Madsen

McSharry

2016

Water

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“…Therefore, the impact of rainfall data 98 resolution should not be ignored in urban hydrology. Although the spatial and temporal 99 resolution of rainfall inputs are strongly related, a number of studies have suggested that the 100 latter generally constitutes a more critical factor than the former (Ochoa-Rodríguez et al, 101 2015; Singh, 1997; Thorndahl et al, 2014) and that temporal resolutions of ~1-2 min (i.e. 102 below those currently available) are required for urban hydrological applications, while 103 spatial resolutions of ~1 km (i.e.…”

mentioning

confidence: 99%

“…Similar to the method 139 proposed by Nielsen et al (2014), the procedure proposed herein entails two main steps: (1) 140 temporal interpolation of radar images, followed by (2) gauge-based adjustment of radar 141…”

mentioning

confidence: 99%

Enhancement of radar rainfall estimates for urban hydrology through optical flow temporal interpolation and Bayesian gauge-based adjustment

Wang

Ochoa-Rodríguez

Assel

et al. 2015

Journal of Hydrology

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“…최근 강우레이 더는 높은 시공간 해상도로 정량적 강수 추정(quantitative precipitation estimation; QPE)을 수행하는 기술로 주목받고 있다. 넓은 지역에 대해 고해상도의 시공간 분해능을 갖는 레 이더 추정 강수는 산악지역에 대한 돌발홍수 예측이나 홍수 예보의 정확도 향상에 큰 가능성을 지닌 것으로 평가되고 있 으며 (Bae et al, 2012), 그동안 많은 관련 연구가 진행되어 왔 다 (Collier et al, 1986;Seo, 1998;Bringi et al, 2001;Borga, 2002;Thorndahl et al, 2014). 특히 국내에서는 단일 편파 레이더의 오차 및 면적평균환산의 오차, 지상 강우자료 의 합성 및 보정 방법, 레이더 반사도의 수직분포에 대한 보 정 등 레이더 추정 강수의 정확도를 평가하고 향상시키기 위 한 연구 (Bae et al, 2012;Kim et al, , 2009Kim et al, , 2010Kim et al, , 2011Kim and Yoo, 2014;Lee et al, 2014;Yoo et al, 2008Yoo et al, , 2009Yoo et al, , 2011aYoo and Yoon, 2010;Yoon et al, 2014b)와 레이더 추정 강우를 활용한 수문학적 홍수 모의 및 산사태 등 재해 예측 기법을 중심으로 많은 연구 (Ham and Hwang, 2014;Noh et al, 2013Noh et al, , 2014Park and Hur, 2009;Yoon et al, 2014a)가 진행되었다.…”

Section: 서 론unclassified

Comparison of Quantitative Precipitation Estimation Algorithms using Dual Polarization Radar Measurements in Korea

Noh¹,

Lim²,

Choi³

et al. 2014

Journal of Korean Society of Hazard Mitigation

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Accurate estimation of precipitation is essential to predict and prevent natural disasters (e.g. flood, landslide, and heavy snowfall). Recently, weather radars have become a popular tool for quantitative precipitation estimation (QPE) with high spatio-temporal resolution. Especially, in the last decade, QPE performance has been improved by introduction of polarimetric technology to observe multiple hydrometeorological variables at various scales. By being able to measure variables such as the differential reflectivity, specific differential phase, and cross-correlation coefficient, the reliability of estimation has significantly improved as compared to the reflectivity-based method (Ryzhkov et al., 2005a). However, QPEs using dual polarization radar data are still subject to uncertainties resulting from rainfall conversion relationships, combination methods of different parameters, and sampling errors. In this study, we assessed the uncertainty and applicability of conventional QPE algorithms, such as JPOLE (Ryzhkov et al., 2005a) and CSU (Cifelli et al., 2011) algorithms, using the dual polarization radar at Mt. Biseul in the south-eastern part of the Korean Peninsula. Analysis results illustrated that the JPOLE algorithm outperformed the CSU algorithm slightly for daily and hourly rainfall analysis using various metrics. The higher accuracy was found in the stations located within less than 60 km from radar and 100 m in the elevation.

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Bias adjustment and advection interpolation of long-term high resolution radar rainfall series

Cited by 68 publications

References 63 publications

Objective Classification of Rainfall in Northern Europe for Online Operation of Urban Water Systems Based on Clustering Techniques

Objective Classification of Rainfall in Northern Europe for Online Operation of Urban Water Systems Based on Clustering Techniques

Enhancement of radar rainfall estimates for urban hydrology through optical flow temporal interpolation and Bayesian gauge-based adjustment

Comparison of Quantitative Precipitation Estimation Algorithms using Dual Polarization Radar Measurements in Korea

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