Derivation of a 10-Year Radar-Based Climatology of Rainfall

Overeem, Aart; Holleman, Iwan; Buishand, Adri

doi:10.1175/2009jamc1954.1

Cited by 139 publications

(133 citation statements)

References 30 publications

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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.

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240

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.

Self Cite

240

274

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“…Rainfall depths, observed at 1.5 km above the ground, are provided with a temporal resolution of 5 min, on a grid of 1 km 2 spatial resolution with a custom geographic projection (Overeem et al, 2009). Information is available through a FTP server.…”

Section: Maximum Rainfall Intensity Datamentioning

confidence: 99%

Opportunities for multivariate analysis of open spatial datasets to characterize urban flooding risks

Gaitan

Veldhuis

2015

Proc. IAHS

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Abstract. Cities worldwide are challenged by increasing urban flood risks. Precise and realistic measures are required to reduce flooding impacts. However, currently implemented sewer and topographic models do not provide realistic predictions of local flooding occurrence during heavy rain events. Assessing other factors such as spatially distributed rainfall, socioeconomic characteristics, and social sensing, may help to explain probability and impacts of urban flooding. Several spatial datasets have been recently made available in the Netherlands, including rainfall-related incident reports made by citizens, spatially distributed rain depths, semidistributed socioeconomic information, and buildings age. Inspecting the potential of this data to explain the occurrence of rainfall related incidents has not been done yet. Multivariate analysis tools for describing communities and environmental patterns have been previously developed and used in the field of study of ecology. The objective of this paper is to outline opportunities for these tools to explore urban flooding risks patterns in the mentioned datasets. To that end, a cluster analysis is performed. Results indicate that incidence of rainfall-related impacts is higher in areas characterized by older infrastructure and higher population density.

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“…A database of C-band weather radar images was used, provided by the Royal Netherlands Meteorological Institute (Table 1). The images are composites based on two C-band Doppler radars, which have been adjusted for various biases using data from manual and automatic rain gauges (Overeem et al, 2009). The rainfall-related variables were obtained using the following steps, as is also described in Spekkers et al (2013b).…”

Section: Rainfall-related Variablesmentioning

confidence: 99%

Decision-tree analysis of factors influencing rainfall-related building structure and content damage

Spekkers

Kok

Clemens

et al. 2014

Nat. Hazards Earth Syst. Sci.

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Abstract. Flood-damage prediction models are essential building blocks in flood risk assessments. So far, little research has been dedicated to damage from small-scale urban floods caused by heavy rainfall, while there is a need for reliable damage models for this flood type among insurers and water authorities.The aim of this paper is to investigate a wide range of damage-influencing factors and their relationships with rainfall-related damage, using decision-tree analysis. For this, district-aggregated claim data from private property insurance companies in the Netherlands were analysed, for the period 1998-2011. The databases include claims of waterrelated damage (for example, damages related to rainwater intrusion through roofs and pluvial flood water entering buildings at ground floor). Response variables being modelled are average claim size and claim frequency, per district, per day. The set of predictors include rainfall-related variables derived from weather radar images, topographic variables from a digital terrain model, building-related variables and socioeconomic indicators of households.Analyses were made separately for property and content damage claim data. Results of decision-tree analysis show that claim frequency is most strongly associated with maximum hourly rainfall intensity, followed by real estate value, ground floor area, household income, season (property data only), buildings age (property data only), a fraction of homeowners (content data only), a and fraction of low-rise buildings (content data only). It was not possible to develop statistically acceptable trees for average claim size. It is recommended to investigate explanations for the failure to derive models. These require the inclusion of other explanatory factors that were not used in the present study, an investigation of the variability in average claim size at different spatial scales, and the collection of more detailed insurance data that allows one to distinguish between the effects of various damage mechanisms to claim size. Cross-validation results show that decision trees were able to predict 22-26 % of variance in claim frequency, which is considerably better compared to results from global multiple regression models (11-18 % of variance explained). Still, a large part of the variance in claim frequency is left unexplained, which is likely to be caused by variations in data at subdistrict scale and missing explanatory variables.

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Derivation of a 10-Year Radar-Based Climatology of Rainfall

Cited by 139 publications

References 30 publications

Country-wide rainfall maps from cellular communication networks

Country-wide rainfall maps from cellular communication networks

Opportunities for multivariate analysis of open spatial datasets to characterize urban flooding risks

Decision-tree analysis of factors influencing rainfall-related building structure and content damage

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