Extreme value modeling of areal rainfall from weather radar

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

doi:10.1029/2009wr008517

Cited by 78 publications

(97 citation statements)

References 37 publications

(62 reference statements)

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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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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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“…The GEV distribution has been shown to fit the AMS better than other distributions, such as the Pearson type III or the generalized logistic (Alila, 1999;Kysely and Picek, 2007;Perica et al, 2013;Schaefer, 1990). It is being widely used for rainfall frequency analyses based on remote sensing data (El-dardiry et al, 2015;Marra and Morin, 2015;Overeem et al, 2009;Paixao et al, 2015;Panziera et al, 2016;Peleg et al, 2017b), owing to its ability to include all the three asymptotic extreme value types (Gumbel, Fréchet and Weibull) into one (Katz et al, 2002).…”

Section: Derivation Of Intensity-duration-frequency Curvesmentioning

confidence: 99%

“…Areal reduction factors and design storms assume homogeneity of rainfall extreme climatology to adapt the point-IDF values estimated by rain gauges to wider areas, such as catchments, based either on the climatology of the region (Sivapalan and Blöschl, 1998), on spatial precipitation observations (Bacchi and Ranzi, 1996;Durrans et al, 2002;Allen and DeGaetano, 2005;Lombardo et al, 2006;Overeem et al, 2010;Wright et al, 2014), or stochastic model simulations (Peleg et al, 2017b, a). In principle, areal reduction factors may depend on a number of factors, such as geographic location, characteristics of the examined catchment, analysed duration and period, season, meteorological conditions, and others (Svensson and Jones, 2010).…”

Section: Introductionmentioning

confidence: 99%

Intensity–duration–frequency curves from remote sensing rainfall estimates: comparing satellite and weather radar over the eastern Mediterranean

Marra

Morin

Peleg

et al. 2017

Hydrol. Earth Syst. Sci.

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Abstract. Intensity-duration-frequency (IDF) curves are widely used to quantify the probability of occurrence of rainfall extremes. The usual rain gauge-based approach provides accurate curves for a specific location, but uncertainties arise when ungauged regions are examined or catchment-scale information is required. Remote sensing rainfall records, e.g. from weather radars and satellites, are recently becoming available, providing high-resolution estimates at regional or even global scales; their uncertainty and implications on water resources applications urge to be investigated. This study compares IDF curves from radar and satellite (CMORPH) estimates over the eastern Mediterranean (covering Mediterranean, semiarid, and arid climates) and quantifies the uncertainty related to their limited record on varying climates. We show that radar identifies thicker-tailed distributions than satellite, in particular for short durations, and that the tail of the distributions depends on the spatial and temporal aggregation scales. The spatial correlation between radar IDF and satellite IDF is as high as 0.7 for 2-5-year return period and decreases with longer return periods, especially for short durations. The uncertainty related to the use of short records is important when the record length is comparable to the return period ( ∼ 50, ∼ 100, and ∼ 150 % for Mediterranean, semiarid, and arid climates, respectively). The agreement between IDF curves derived from different sensors on Mediterranean and, to a good extent, semiarid climates, demonstrates the potential of remote sensing datasets and instils confidence on their quantitative use for ungauged areas of the Earth.

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“…Especially for extreme rainfall, climatology of radar data seems very promising (Overeem et al, 2010;Pedersen et al, 2008;Rudolph et al, 2011;Wagner et al, 2006). We also focus on the development of a correction algorithm especially for moderate and heavy precipitation with a profound database.…”

Section: Methodsmentioning

confidence: 99%

Joint statistical correction of clutters, spokes and beam height for a radar derived precipitation climatology in southern Germany

Wagner

Seltmann

Kunstmann

2012

Hydrol. Earth Syst. Sci.

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Abstract. First results of radar derived climatology have emerged over the last years, as datasets of appropriate extent are becoming available. Usually, these statistics are based on time series lasting up to ten years as continuous storage of radar data was often not achieved before. This kind of climatology demands a high level of data quality. Small deviations or minor systematic under-or overestimations in single radar images become a major cause of error in statistical analysis. Extensive corrections of radar data are a crucial prerequisite for radar derived climatology. We present a new statistical post-correction scheme based on a climatological analysis of seven years of radar data of the Munich weather radar (2000)(2001)(2002)(2003)(2004)(2005)(2006) operated by DWD (German Weather Service). Original radar products are used subject only to corrections within the signal processor without any further corrections on single radar images. The aim of this statistical correction is to make up for the average systematic errors caused by clutter, propagation, or measuring effects but to conserve small-scale natural variations in space.The statistical correction is based on a thorough analysis of the different causes of possible errors for the Munich weather radar. This analysis revealed the following basic effects: the decrease of rain amount as a function of height and distance from the radar, clutter effects such as clutter remnants after filtering, holes by eliminated clutter or shading effects from obstacles near the radar, visible as spokes, as well as the influence of the bright band. The correction algorithm is correspondingly based on these results. It consists of three modules. The first one is an altitude correction which minimises measuring effects. The second module corrects clutter effects and disturbances and the third one realises a mean adjustment to selected rain gauges. Two different sets of radar products are used. The statistical analysis as well as module 1 and module 2 of the correction algorithm are based on frequencies of the six reflectivity levels within the so-called PX product. For correction module 3 and for the validation of the correction algorithm, rain amounts are calculated from the 8-bit so-called DX product. The correction algorithm is created to post-correct climatological or statistical analysis of radar data with a temporal resolution larger than one year. The correction algorithm is used for frequencies of occurrence of radar reflectivities which enables its application even for radar products such as DWD's cell-tracking-product CON-RAD.Application (

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Extreme value modeling of areal rainfall from weather radar

Cited by 78 publications

References 37 publications

Country-wide rainfall maps from cellular communication networks

Country-wide rainfall maps from cellular communication networks

Intensity–duration–frequency curves from remote sensing rainfall estimates: comparing satellite and weather radar over the eastern Mediterranean

Joint statistical correction of clutters, spokes and beam height for a radar derived precipitation climatology in southern Germany

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