Real-time estimation of mean field bias in radar rainfall data

Seo, Dong Jun; Breidenbach, Jay P.; Johnson, Edward R.

doi:10.1016/s0022-1694(99)00106-7

Cited by 222 publications

(122 citation statements)

References 15 publications

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“…In hydrology, for example, regional models for hydro-meteorological variables, such as extreme or annual rainfall, are often obtained by coupling a deterministic indicator, based on models, with a spatial model of the residuals measured at gauged sites [19][20][21][22]. A similar approach is also used for extracting the estimated rainfall rate at ground through meteorological radars and rain gauge measurements [23,24]. Several methods have been introduced to this purpose [23][24][25][26][27].…”

Section: Methodsmentioning

confidence: 99%

Extreme Wave Analysis by Integrating Model and Wave Buoy Data

Dentale

Furcolo

Carratelli

et al. 2018

Water

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Abstract:Estimating the extreme values of significant wave height (H S ), generally described by the H S return period T R function H S (T R ) and by its confidence intervals, is a necessity in many branches of coastal science and engineering. The availability of indirect wave data generated by global and regional wind and wave model chains have brought radical changes to the estimation procedures of such probability distribution-weather and wave modeling systems are routinely run all over the world, and H S time series for each grid point are produced and published after assimilation (analysis) of the ground truth. However, while the sources of such indirect data are numerous, and generally of good quality, many aspects of their procedures are hidden to the users, who cannot evaluate the reliability and the limits of the H S (T R ) deriving from such data. In order to provide a simple engineering tool to evaluate the probability of extreme sea-states as well as the quality of such estimates, we propose here a procedure based on integrating H S time series generated by model chains with those recorded by wave buoys in the same area.

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Section: Methodsmentioning

confidence: 99%

Extreme Wave Analysis by Integrating Model and Wave Buoy Data

Dentale

Furcolo

Carratelli

et al. 2018

Water

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“…Generally, the ratio of the means of the rain gauge rain rate and radar rain rate (that is, the G/R ratio) is used for this purpose [32,33]. As the G/R ratio can be estimated easily and in real-time, it has been widely used for the purpose of mean-field bias correction [18,[34][35][36]. In this study, the radar rain rate was once again corrected by applying the G/R ratio that was derived at every point of time and for each altitude zone.…”

Section: Estimation Of Radar Rain Rate Field and G/r Correctionmentioning

confidence: 99%

Calibrating Radar Data in an Orographic Setting: A Case Study for the Typhoon Nakri in the Hallasan Mountain, Korea

Yoo

2017

Atmosphere

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Abstract:The Typhoon Nakri passed the Jeju Island in Korea (1-3 August 2014) and recorded one-day rainfall of 1182 mm at Witse-Oreum (a place name where a small volcanic cone is) of the Hallasan Mountain, Korea. This one-day rainfall amount was the highest rainfall that was ever recorded in Korea. As the altitude of Witse-Oreum is 1673 m, it was believed that the orographic effect enhanced the rainfall depth significantly. It was also argued that the maximum rainfall could be recorded in some other locations in the Hallasan Mountain. In this study, the rainfall event due to the Typhoon Nakri in the Jeju Island was analyzed using the radar and rain gauge data that were collected during this rainfall event. Fortunately, two radars are available on the eastern and western side of the Jeju Island. The entire Hallasan Mountain is covered by these two radars. A total of 23 rain gauges are also available in the Jeju Island. As a first step, independent ground-level Z-R relations were derived for every 250 m interval from the sea-level. Each Z-R relation was then applied to the corresponding-altitude radar reflectivity data to generate the rain rate field over the Jeju Island. Finally, the generated radar rain rate data were examined fully to evaluate the orographic effect in the Hallasan Mountain, also to locate the point where the maximum rain rate was recorded. This result shows that the maximum one-day rainfall amount could be up to 1500 mm, which was about 30% higher than the rain gauge measurement.

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“…Seo et al (1999) call attention to the fact that a single mean field bias cannot generally represent a large area or the entire radar umbrella. However, because of operational constraints and the fact that there are very often large areas with a low density of rain gauges, the single factor approach may be very useful.…”

Section: The Kalman Filter Algorithmmentioning

confidence: 99%

Adjustment of state space models in view of area rainfall estimation

Costa

Alpuim

2010

Environmetrics

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SUMMARYThis paper uses state space models and the Kalman filter to merge weather radar and rain gauge measurements in order to improve area rainfall estimates. Particular attention is given to the estimation of state space model parameters because precipitation data clearly deviates from the normal distribution, and the commonly used maximum likelihood method is difficult to apply and does not perform well. This work is based on 17 storms occurring between September 1998 and November 2000 in an area including part of the Alenquer river hydrographical basin. Based on these data, the work aims to investigate the importance of the parameters estimation method to the accuracy of mean area precipitation estimates. It was possible to conclude that the distribution-free estimation methods produce, in general, better mean area rainfall estimates than the maximum likelihood.

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Real-time estimation of mean field bias in radar rainfall data

Cited by 222 publications

References 15 publications

Extreme Wave Analysis by Integrating Model and Wave Buoy Data

Extreme Wave Analysis by Integrating Model and Wave Buoy Data

Calibrating Radar Data in an Orographic Setting: A Case Study for the Typhoon Nakri in the Hallasan Mountain, Korea

Adjustment of state space models in view of area rainfall estimation

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