Spatial variability of extreme rainfall at radar subpixel scale

Peleg, Nadav; Marra, Francesco; Fatichi, Simone; Paschalis, Alec; Molnár, Péter; Burlando, Paolo

doi:10.1016/j.jhydrol.2016.05.033

Cited by 105 publications

(111 citation statements)

References 64 publications

(2 reference statements)

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“…Conversely, the more arid the climate is, the more skewed the extreme values distribution is, with long returnperiod intensities for arid areas being higher than the corresponding values for semiarid and Mediterranean areas, especially for short durations (Ben-Zvi, 2009;Marra and Morin, 2015). Morin et al, 1995Morin et al, , 2001Morin et al, , 2009Morin and Gabella, 2007;Peleg and Morin, 2012;Peleg et al, 2013Peleg et al, , 2017bYakir and Morin, 2011).…”

Section: Mediterraneanmentioning

confidence: 98%

“…The small-scale representativeness of rain gauge measurements makes them not suitable for a large-scale quantitative assessment of remote sensing products (Gires et al, 2014;Peleg et al, 2017b). Here, we take advantage of their long records to empirically quantify the uncertainty in rainfall frequency analysis related to the use of short records in different climatic conditions.…”

Section: Rain Gauge Datamentioning

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%

“…Regionalization and interpolation techniques continue assuming spatial homogeneity to derive IDF curves for ungauged locations (Ceresetti et al, 2012). However, homogeneity of rainfall extremes is a weak assumption when wide areas are considered or when, even at the small scales, the data record length is limited, especially in the case of short durations (Peleg et al, 2017b).…”

Section: Introductionmentioning

confidence: 99%

“…It is therefore useful to analyse the characteristics of IDF curves derived from non-adjusted rainfall data, which are expected to represent the frequencies of near-real-time estimates. Second, areal IDFs provided by remote sensing instruments are expected to differ from point IDFs (Peleg et al, 2017b), and the use of different records (i.e. different samples of the climate) introduces further differences.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Mediterraneanmentioning

confidence: 98%

Section: Rain Gauge Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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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An advanced stochastic weather generator for simulating 2‐D high‐resolution climate variables

Peleg

Fatichi

Paschalis

et al. 2017

J Adv Model Earth Syst

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121

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A new stochastic weather generator, Advanced WEather GENerator for a two‐dimensional grid (AWE‐GEN‐2d) is presented. The model combines physical and stochastic approaches to simulate key meteorological variables at high spatial and temporal resolution: 2 km × 2 km and 5 min for precipitation and cloud cover and 100 m × 100 m and 1 h for near‐surface air temperature, solar radiation, vapor pressure, atmospheric pressure, and near‐surface wind. The model requires spatially distributed data for the calibration process, which can nowadays be obtained by remote sensing devices (weather radar and satellites), reanalysis data sets and ground stations. AWE‐GEN‐2d is parsimonious in terms of computational demand and therefore is particularly suitable for studies where exploring internal climatic variability at multiple spatial and temporal scales is fundamental. Applications of the model include models of environmental systems, such as hydrological and geomorphological models, where high‐resolution spatial and temporal meteorological forcing is crucial. The weather generator was calibrated and validated for the Engelberg region, an area with complex topography in the Swiss Alps. Model test shows that the climate variables are generated by AWE‐GEN‐2d with a level of accuracy that is sufficient for many practical applications.

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Simulating Small‐Scale Rainfall Fields Conditioned by Weather State and Elevation: A Data‐Driven Approach Based on Rainfall Radar Images

Oriani

Ohana‐Levi

Marra

et al. 2017

Water Resources Research

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The quantification of spatial rainfall is critical for distributed hydrological modeling. Rainfall spatial patterns generated by similar weather conditions can be extremely diverse. This variability can have a significant impact on hydrological processes. Stochastic simulation allows generating multiple realizations of spatial rainfall or filling missing data. The simulated data can then be used as input for numerical models to study the uncertainty on hydrological forecasts. In this paper, we use the direct sampling technique to generate stochastic simulations of high‐resolution (1 km) daily rainfall fields, conditioned by elevation and weather state. The technique associates historical radar estimates to variables describing the daily weather conditions, such as the rainfall type and mean intensity, and selects radar images accordingly to form a conditional training image set of each day. Rainfall fields are then generated by resampling pixels from these images. The simulation at each location is conditioned by neighbor patterns of rainfall amount and elevation. The technique is tested on the simulation of daily rainfall amount for the eastern Mediterranean. The results show that it can generate realistic rainfall fields for different weather types, preserving the temporal weather pattern, the spatial features, and the complex relation with elevation. The concept of conditional training image provides added value to multiple‐point simulation techniques dealing with extremely nonstationary heterogeneities and extensive data sets.

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Spatial variability of extreme rainfall at radar subpixel scale

Cited by 105 publications

References 64 publications

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

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

An advanced stochastic weather generator for simulating 2‐D high‐resolution climate variables

Simulating Small‐Scale Rainfall Fields Conditioned by Weather State and Elevation: A Data‐Driven Approach Based on Rainfall Radar Images

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