2016
DOI: 10.5194/hess-20-1719-2016
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Assessment of small-scale variability of rainfall and multi-satellite precipitation estimates using measurements from a dense rain gauge network in Southeast India

Abstract: Abstract. This paper describes the establishment of a dense rain gauge network and small-scale variability in rain events (both in space and time) over a complex hilly terrain in Southeast India. Three years of high-resolution gauge measurements are used to validate 3-hourly rainfall and subdaily variations of four widely used multi-satellite precipitation estimates (MPEs). The network, established as part of the Megha-Tropiques validation program, consists of 36 rain gauges arranged in a near-square grid area… Show more

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Cited by 25 publications
(27 citation statements)
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References 58 publications
(92 reference statements)
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“…The evaluation based on qualitative indices such as FBIAS, POD, FAR, SEDI, ETS, and IMERG shows clear dependence on seasons. Comparisons with other evaluation results conclude remarkable improvement of low FAR (Sunilkumar et al, , ). Combined effect of three algorithms CMORPH‐MKF, PERSIANN‐CCS, and updated GPROF‐2014 may be attributed for improving IR‐based precipitation estimates with low FAR scores.…”
Section: Discussionmentioning
confidence: 51%
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“…The evaluation based on qualitative indices such as FBIAS, POD, FAR, SEDI, ETS, and IMERG shows clear dependence on seasons. Comparisons with other evaluation results conclude remarkable improvement of low FAR (Sunilkumar et al, , ). Combined effect of three algorithms CMORPH‐MKF, PERSIANN‐CCS, and updated GPROF‐2014 may be attributed for improving IR‐based precipitation estimates with low FAR scores.…”
Section: Discussionmentioning
confidence: 51%
“…Several such previous studies performed evaluation of new era GPM‐IMERG product on accounting various aspects such as influence of topography, geographical features on accuracy of precipitation product, and seasonal characteristics of error metrics (Asong et al, ; Sungmin et al, ; Gebregiorgis et al, ; Kim et al, ; Krishna et al, ; Sharifi et al, ; Sunilkumar et al, , ; Tang et al, ; Xu et al, ). Subsequently, some of these studies put special emphasis on improvement of GPM‐IMERG product over its predecessor TRMM‐3B42 in several aspects (Gebregiorgis et al, ; Kim et al, ).…”
Section: Introductionmentioning
confidence: 99%
“…Usually, gauge data are deterministically or geostatistically interpolated (Dirks et al, 1998;Lebel & Laborde, 1988;Ly et al, 2011;Syed et al, 2003;Verworn & Haberlandt, 2011). Spatial correlation measures of gauge observations help estimating area precipitation (Ciach & Krajewski, 2006;Sivapalan & Blöschl, 1998;Sunilkumar et al, 2016;Tokay et al, 2014;Villarini et al, 2008). On small scales, however, this is a challenge because usually very few data pairs exist at small station separation distances, weakening the robustness of the estimated correlogram, semivariogram, or covariance function (Lebel & Laborde, 1988).…”
Section: Introductionmentioning
confidence: 99%
“…Precipitation is one of most critical input variables for accurate hydrological simulation [1,2]. Studies on the precipitation input impacts on the performance of hydrological models are fewer, compared to the attention paid to sophisticated rainfall-runoff modeling approaches [3].…”
Section: Introductionmentioning
confidence: 99%