Extreme rainfall analysis and estimation of depth‐duration‐frequency curves using weather radar

Overeem, Aart; Buishand, T. A.; Holleman, Iwan

doi:10.1029/2009wr007869

Cited by 154 publications

(179 citation statements)

References 26 publications

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“…For the Netherlands, however, Overeem et al (2008Overeem et al ( , 2009 show that spatial correlations of daily precipitation extremes in rain radar data are very small for distances > 50 km, and that the statistics of extremes with a return period < 100 yr are barely affected by spatial correlation. Data from observations and models are treated in the same way, thus biases due to spatial correlation are expected to be similar in both observations and model results.…”

Section: Methodsmentioning

confidence: 99%

Exploring metrics of extreme daily precipitation in a large ensemble of regional climate model simulations

Lenderink¹

2010

Clim. Res.

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The ability of a large ensemble of 15 state-of-the-art regional climate models (RCMs) to simulate precipitation extremes was investigated. The 99th, 99.9th and 99.99th percentiles of daily precipitation in the models were compared with those in the recently released E-OBS observational database for winter, spring, summer and autumn. The majority of the models overestimated the values of the precipitation extremes compared with E-OBS, on average by approximately 38%, but some models exceeded 50%. To measure model performance, a simple metric is proposed that averages a nonlinear function of the seasonal biases over the European area. The sensitivity of the metric to different assumptions in the construction and the quality of the observational data was explored. Generally, low sensitivities of the metric to spatial and seasonal averaging were found. However, large sensitivities to potential biases in the observational database were found. An alternative metric that measures the spatial pattern of the extremes (which is not sensitive to a potential constant offset in the observational data) was further explored. With this metric, the ranking between the models changed substantially. However, the 2 models with the worst score in the standard metric also displayed the worst scores with this alternative metric. Finally, the regional climate models displayed the largest biases compared with E-OBS in areas where the underlying station density used in E-OBS is low, thus suggesting that data quality is indeed an important issue. In summary, the results show that: (1) there is no metric that guarantees an objective and precise ranking or weighting of the models, (2) by exploring different metrics it nevertheless appears possible to indentify models that perform consistently worse than other models, and (3) the observational data quality should be considered when designing and interpreting metrics.

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Section: Methodsmentioning

confidence: 99%

Exploring metrics of extreme daily precipitation in a large ensemble of regional climate model simulations

Lenderink¹

2010

Clim. Res.

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“…The quality of a similar radar dataset for another period was found to be high, enabling to derive extreme areal rainfall statistics (31)(32)(33). For the calibration, a radar dataset of path-averaged rainfall intensities over each link was constructed (8).…”

Section: Methodsmentioning

confidence: 99%

Country-wide rainfall maps from cellular communication networks

Overeem

Leijnse

Uijlenhoet

2013

Proc. Natl. Acad. Sci. U.S.A.

237

274

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Accurate and timely surface precipitation measurements are crucial for water resources management, agriculture, weather prediction, climate research, as well as ground validation of satellite-based precipitation estimates. However, the majority of the land surface of the earth lacks such data, and in many parts of the world the density of surface precipitation gauging networks is even rapidly declining. This development can potentially be counteracted by using received signal level data from the enormous number of microwave links used worldwide in commercial cellular communication networks. Along such links, radio signals propagate from a transmitting antenna at one base station to a receiving antenna at another base station. Rain-induced attenuation and, subsequently, path-averaged rainfall intensity can be retrieved from the signal's attenuation between transmitter and receiver. Here, we show how one such a network can be used to retrieve the space-time dynamics of rainfall for an entire country (The Netherlands, ∼35,500 km 2 ), based on an unprecedented number of links (∼2,400) and a rainfall retrieval algorithm that can be applied in real time. This demonstrates the potential of such networks for real-time rainfall monitoring, in particular in those parts of the world where networks of dedicated ground-based rainfall sensors are often virtually absent.hydrometeorology | remote sensing

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“…We also adopted L-moments to calculate the summary statistics for probability distributions (Hosking 1990). Recently, the use of L-moments has become more popular than other methods to estimate distribution parameters from the pooled annual maxima, and to calculate the probable precipitation because of the robust estimate for a given amount of data (e.g., Overeem et al 2009;Fujibe 2014).…”

Section: Methodsmentioning

confidence: 99%

“…Although weather radar data generally have limited homogeneity caused by continual improvements to the data processing algorithms (Durrans et al 2002), their marked advantage is their higher spatial resolution (e.g., Overeem et al 2009). Our objective is to produce a preliminary probable hourly precipitation and probable SWI for a 50-yr recurrence interval from the 5-km grid-cell R/A dataset over the Japanese archipelago.…”

Section: Introductionmentioning

confidence: 99%

Probable Hourly Precipitation and Soil Water Index for 50-yr Recurrence Interval over the Japanese Archipelago

Saitô

Matsuyama

2015

SOLA

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We produced a preliminary high-resolution probable hourly precipitation (mm h −1 ) and probable Soil Water Index (SWI) for a 50-yr recurrence interval over the Japanese archipelago from 5-km grid-cell Radar/Raingauge-Analyzed Precipitation (R/A) with a 26-yr time series (1988−2013). To date, the selection and applicability of a probability density function such as the Gumbel distribution (GUM) or the generalized extreme value distribution (GEV) has been investigated mainly from a statistical perspective, whereas this study examines them from the viewpoint of disaster prevention. Results show detailed spatial patterns of the probable hourly precipitation and SWI in Japan with ranges of 17.0−158.0 (±1.1−51.1) mm h −1 and 82.1−638.6 (±3.7−93.0), respectively, by spatial resolution of 5-km grid-cell. The probable hourly precipitation by GUM is less than that of GEV with a shape parameter of less than 0.0. Our results demonstrate that R/A-based probable hourly precipitation and SWI by GUM, which has robust estimation, is appropriate for landslide hazard assessment. There results revealed the potential and limitation of estimating the probable precipitation from the R/A dataset. Further study should verify the R/A dataset and the probable precipitations with accumulation of R/A.(Citation: Saito, H., and H. Matsuyama, 2015: Probable hourly precipitation and Soil Water Index for 50-yr recurrence interval over the Japanese archipelago. SOLA, 11, 118−122,

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Extreme rainfall analysis and estimation of depth‐duration‐frequency curves using weather radar

Cited by 154 publications

References 26 publications

Exploring metrics of extreme daily precipitation in a large ensemble of regional climate model simulations

Exploring metrics of extreme daily precipitation in a large ensemble of regional climate model simulations

Country-wide rainfall maps from cellular communication networks

Probable Hourly Precipitation and Soil Water Index for 50-yr Recurrence Interval over the Japanese Archipelago

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