Spatial variability of rainfall: Variations within a single radar pixel

Jensen, N. E.; Pedersen, Lars Haastrup

doi:10.1016/j.atmosres.2004.10.029

Cited by 60 publications

(44 citation statements)

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“…The PERSIANN algorithm is an adaptive, multi-platform precipitation estimation system that uses ANN 1 technology to merge high-quality, sparsely sampled data from NASA, 2 NOAA, 3 Considerable efforts have been made in the development of gridded precipitation datasets based on satellite data or satellite-based merged analyses [14][15] . There has been an increasing demand for accurate, highresolution, rain gauge-based precipitation products over land for the validation of simulation products of numerical models and satellite-based, high-resolution precipitation products 16 .…”

Section: Methodsmentioning

confidence: 99%

“…Rainfall is a highly dynamic process, constantly changing in form and intensity over a given area. Traditionally, rainfall is measured by use of rain a gauge, an instrument that accurately measures the actual amount of rain that falls into it 2 . However, even in well-populated regions with sophisticated infrastructure, simple point-based measurements by the use of rain-gauges may not be sufficient for characterizing the spatial patterns of precipitations.…”

Section: Introductionmentioning

confidence: 99%

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Validation of Satellite-Based PERSIANN Rainfall Estimates Using Surface-Based APHRODITE Data over Iran

Jamli¹

2015

EARTH

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Abstract:Surface-based precipitation measurements with high accuracy on different spatial-temporal scales have a crucial importance in different land-use planning sectors, especially in arid and semi-arid regions, such as Iran. Because the density of spatial distribution of rain-gauges is not uniform throughout the country, satellite sensor technology is considered useful for precipitation monitoring over the study area. In this study, PERSIANN satellite-based rainfall data were validated through comparison with the APHRODITE surface-based precipitation data. The validation was carried out for annual and seasonal precipitation, as well as an inter-annual comparison. Our analysis was based on a visual comparison and a statistical approach, including linear regression and spatial correlation between APHRODITE and PERSIANN datasets for each 0.25°×0.25° grid cell in the entire country, in the Caspian Sea region, and in the Zagros Mountains, indicating spatial correlation coefficients of 0.62, 0.62, 0.47, respectively. Both APHRODITE data and PERSIANN data showed that spatial distribution of mean annual and seasonal precipitation over Iran has two main patterns: along the Caspian Sea and along the Zagros Mountain chain. In general, PERSIANN underestimates high rainfall rates by 5.5 mm/day in winter but overestimates the low rainfalls in annual and seasonal scales by 0.9 mm/day in summer.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Validation of Satellite-Based PERSIANN Rainfall Estimates Using Surface-Based APHRODITE Data over Iran

Jamli¹

2015

EARTH

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“…Gebremichael and Krajewski (2004) used both TEFLUN-B and TRMM-LBA networks to estimate the radar's ability to characterize the small-scale spatial variability of rainfall by comparing the correlation function of the gauge and the radar. A network consisting of nine optical rain gauges within 500 × 500 m was deployed in Denmark by Jensen and Pedersen (2005) to explore the radar subpixel-scale rainfall variation. Pedersen et al (2010) used the same network to determine the coefficient of variation and the spatial correlation of the rainfall field.…”

Section: Introductionmentioning

confidence: 99%

Radar subpixel-scale rainfall variability and uncertainty: lessons learned from observations of a dense rain-gauge network

Peleg

Ben-Asher

Morin

2013

Hydrol. Earth Syst. Sci.

108

102

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Abstract. Runoff and flash flood generation are very sensitive to rainfall's spatial and temporal variability. The increasing use of radar and satellite data in hydrological applications, due to the sparse distribution of rain gauges over most catchments worldwide, requires furthering our knowledge of the uncertainties of these data. In 2011, a new superdense network of rain gauges containing 14 stations, each with two side-by-side gauges, was installed within a 4 km 2 study area near Kibbutz Galed in northern Israel. This network was established for a detailed exploration of the uncertainties and errors regarding rainfall variability within a common pixel size of data obtained from remote sensing systems for timescales of 1 min to daily. In this paper, we present the analysis of the first year's record collected from this network and from the Shacham weather radar, located 63 km from the study area. The gauge-rainfall spatial correlation and uncertainty were examined along with the estimated radar error. The nugget parameter of the inter-gauge rainfall correlations was high (0.92 on the 1 min scale) and increased as the timescale increased. The variance reduction factor (VRF), representing the uncertainty from averaging a number of rain stations per pixel, ranged from 1.6 % for the 1 min timescale to 0.07 % for the daily scale. It was also found that at least three rain stations are needed to adequately represent the rainfall (VRF < 5 %) on a typical radar pixel scale. The difference between radar and rain gauge rainfall was mainly attributed to radar estimation errors, while the gauge sampling error contributed up to 20 % to the total difference. The ratio of radar rainfall to gauge-areal-averaged rainfall, expressed by the error distribution scatter parameter, decreased from 5.27 dB for 3 min timescale to 3.21 dB for the daily scale. The analysis of the radar errors and uncertainties suggest that a temporal scale of at least 10 min should be used for hydrological applications of the radar data. Rainfall measurements collected with this dense rain gauge network will be used for further examination of small-scale rainfall's spatial and temporal variability in the coming years.

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“…Gauge observations were often not representative for any of the nine radar pixels used. Jensen and Pedersen (2005) analysed the spatial variability of uncorrected gauge measured rainfall data for a single radar pixel (0.25 km 2 ) for eight precipitation events and detected a variation up to 100% between the adjacent rain gauges (over a 4-day period). In the present study no general statistical score characteristic became apparent.…”

Section: Discussionmentioning

confidence: 99%

An approach to combine radar and gauge based rainfall data under consideration of their qualities in low mountain ranges of Saxony

Jatho

Pluntke

Kurbjuhn

et al. 2010

Nat. Hazards Earth Syst. Sci.

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Abstract. An approach to combine gauge and radar data and additional quality information is presented. The development was focused on the improvement of the diagnostic for temporal (one hour) and spatial (1×1 km 2 ) highly resolved precipitation data. The method is embedded in an online tool and was applied to the target area Saxony, Germany. The aim of the tool is to provide accurate spatial rainfall estimates. The results can be used for rainfall run-off modelling, e.g. in a flood management system. Quality information allows a better assessment of the input data and the resulting precipitation field. They are stored in corresponding fields and represent the static and dynamic uncertainties of radar and gauge data. Objective combination of various precipitation and quality fields is realised using a cost function.The findings of cross validation reveal that the proposed combination method merged the benefits and disadvantages of interpolated gauge and radar data and leads to mean estimates. The sampling point validation implies that the presented method slightly overestimated the areal rain as well as the high rain intensities in case of convective and advective events, while the results of pure interpolation method performed better. In general, the use of presented cost function avoids false rainfall amount in areas of low input data quality and improves the reliability in areas of high data quality. It is obvious that the combined product includes the small-scale variability of radar, which is seen as the important benefit of the presented combination approach. Local improvements of the final rain field are possible due to consideration of gauges that were not used for radar calibration, e.g. in topographic distinct regions.

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Spatial variability of rainfall: Variations within a single radar pixel

Cited by 60 publications

References 0 publications

Validation of Satellite-Based PERSIANN Rainfall Estimates Using Surface-Based APHRODITE Data over Iran

Validation of Satellite-Based PERSIANN Rainfall Estimates Using Surface-Based APHRODITE Data over Iran

Radar subpixel-scale rainfall variability and uncertainty: lessons learned from observations of a dense rain-gauge network

An approach to combine radar and gauge based rainfall data under consideration of their qualities in low mountain ranges of Saxony

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