The wind-induced loss of thunderstorm precipitation measurements

Chvíla, B.; Sevruk, Boris; Ondrás, M.

doi:10.1016/j.atmosres.2004.11.032

Cited by 23 publications

(12 citation statements)

References 7 publications

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“…As mentioned in section 3a, this comprises 236 rainy PR FOVs, as opposed to only 85 located entirely within the watershed. Second, wind induced undercatch of rainfall by the gauge (e.g., Nespor and Sevruk 1999;Chvíla et al 2005). Therefore, a given overpass is associated with points having the same PR R value, and therefore appears in Fig.…”

Section: Watershed Area-average Rainfall Ratementioning

confidence: 99%

Assessing Satellite-Based Rainfall Estimates in Semiarid Watersheds Using the USDA-ARS Walnut Gulch Gauge Network and TRMM PR

Amitai

Unkrich

Goodrich

et al. 2012

Journal of Hydrometeorology

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The rain gauge network associated with the Walnut Gulch Experimental Watershed (WGEW) in southeastern Arizona provides a unique opportunity for direct comparisons of in situ measurements and satellite-based instantaneous rain rate estimates like those from the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR). The WGEW network is the densest rain gauge network in the PR coverage area for watersheds greater than 10 km 2 . It consists of 88 weighing rain gauges within a 149-km 2 area. On average, approximately 10 gauges can be found in each PR field of view (;5-km diameter). All gauges are very well synchronized with 1-min reporting intervals. This allows generating very-high-temporal-resolution rain rate fields and obtaining accurate estimates of the area-average rain rate for the entire watershed and for a single PR field of view. In this study, instantaneous rain rate fields from the PR and the spatially interpolated gauge measurements (on a 100 m 3 100 m grid, updated every 1 min) are compared for all TRMM overpasses in which the PR recorded rain within the WGEW boundaries (25 overpasses during 1999-2010). The results indicate very good agreement between the fields with low bias values (,10%) and high correlation coefficients, especially for the near-nadir cases (.0.9). The correlation is high at overpass time but the peak occurs several minutes after the overpass, which can be explained by the fact that it takes several minutes for the raindrops to reach the gauge from the time they are observed by the PR. The correlation improves with the new version of the TRMM algorithm (V7). The study includes assessment of the accuracy of the reference products.

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Section: Watershed Area-average Rainfall Ratementioning

confidence: 99%

Assessing Satellite-Based Rainfall Estimates in Semiarid Watersheds Using the USDA-ARS Walnut Gulch Gauge Network and TRMM PR

Amitai

Unkrich

Goodrich

et al. 2012

Journal of Hydrometeorology

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“…Assuming that both instruments work perfectly and accurately, the observed difference between the rainfall amounts measured by the two gauges is due to wind effects. The buried gauge is expected to show a higher rainfall reading than the gauge above ground, with differences potentially as large as 10% or more [e.g., Koschmieder , 1934; Rodda , 1967; Larson and Peck , 1974; Aldridge , 1976; Neff , 1977; Groisman and Legates , 1994; Yang et al , 1998b; Seibert and Moren , 1999; Duchon and Essenberg , 2001; Chang and Harrison , 2005; Chvila et al , 2005]. Wind effects can be even larger for snowfall measurements, with undercatches amounting to 50% or more [e.g., Goodison , 1978; Gunther and Graf , 1991; Allerup et al , 1997; Goodison et al , 1998; Yang et al , 1999, 2001; Bogdanova et al , 2002; Sugiura et al , 2003].…”

Section: Introductionmentioning

confidence: 99%

Challenges in obtaining reliable measurements of point rainfall

Sieck

Burges

Steiner

2007

Water Resources Research

133

119

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[1] Extensive data recorded from storm systems passing over the well-instrumented 21.4 km 2 Goodwin Creek watershed in northern Mississippi are used to highlight uncertainties associated with the measurement of surface rainfall, focusing on data quality control, gauge calibration, out-of-level gauge orifices, and wind effects on rain gauge catch. Assessment of the wind effect on gauge catch is central to the presented analyses, including an in-depth evaluation of a recent technique for estimating the rainfall undercatch by means of wind and raindrop size information. Our findings emphasize that quantification of the wind effect on rain gauge catch is difficult because of uncertainties associated with measuring rainfall, drop size distribution, and the wind at gauge rim height. On the basis of our evaluation the sophisticated wind effect correction technique that makes use of raindrop size and wind information is much less effective than traditional methods based on rainfall rate and wind observations alone.

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“…Convective rain is associated with higher rainfall intensities and is commonly identified from ground synoptic observations of clouds, state of weather and/or rain intensity fluctuations (e.g. Pešice et al, 2003;Chvila et al, 2005;Tremblay, 2005;Llasat et al, 2007;Berg et al, 2013;Ruiz-Leo et al, 2013;Rulfová and Kyselý, 2013). Weather radar provides additional information (e.g.…”

Section: Introductionmentioning

confidence: 99%

Selection of intense rainfall events based on intensity thresholds and lightning data in Switzerland

Gaál

Molnár

Szolgay

2014

Hydrol. Earth Syst. Sci.

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Abstract. This paper presents a method to identify intense warm season storms with convective character based on intensity thresholds and the presence of lightning, and analyzes their statistical properties. Long records of precipitation and lightning data at 4 stations and 10 min resolution in different climatological regions in Switzerland are used. Our premise is that thunderstorms associated with lightning generate bursts of high rainfall intensity. We divided all recorded storms into those accompanied by lightning and those without lightning and found the threshold I * that separates intense events based on peak 10 min intensity I p ≥ I * for a chosen misclassification rate α. The performance and robustness of the selection method was tested by investigating the inter-annual variability of I * and its relation to the frequency of lightning strikes. The probability distributions of the main storm properties (rainfall depth R, event duration D, average storm intensity I a and peak 10 min intensity I p ) for the intense storm subsets show that the event average and peak intensities are significantly different between the stations. Nonparametric correlations between the main storm properties were estimated for intense storms and all storms including stratiform rain. The differences in the correlations between storm subsets are greater than those between stations, which indicates that care must be exercised not to mix events of different origin when they are sampled for multivariate analysis, for example, copula fitting to rainfall data.

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The wind-induced loss of thunderstorm precipitation measurements

Cited by 23 publications

References 7 publications

Assessing Satellite-Based Rainfall Estimates in Semiarid Watersheds Using the USDA-ARS Walnut Gulch Gauge Network and TRMM PR

Assessing Satellite-Based Rainfall Estimates in Semiarid Watersheds Using the USDA-ARS Walnut Gulch Gauge Network and TRMM PR

Challenges in obtaining reliable measurements of point rainfall

Selection of intense rainfall events based on intensity thresholds and lightning data in Switzerland

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