The Ground-Truth Problem for Satellite Estimates of Rain Rate

North, Gerald R.; Valdés, Juan B.; Ha, Eunho; Shen, Samuel S. P.

doi:10.1175/1520-0426(1994)011<1035:tgtpfs>2.0.co;2

Cited by 33 publications

(28 citation statements)

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“…이상과 같은 레이더의 대표적 문제점은 다양한 연구들을 통해 해결 되어 가고 있다. 예를 들어, 레이더 에코와 레이더 강우 를 연결하는 문제 Ha and North, 1995;Creutin et al, 1997;Anagnostou et al, 1998;Steiner et al, 1999), 레이더 강우와 실제 강우와 의 차이를 보정하는 문제 (유철상 등, 2007;North et al, 1994;Ha et al, 2002;Yoo, 2002;Yoo et al, 2002), 레 이더 강우와 우량계 강우를 합성하는 문제 (정성화, 2005;최규현 등, 2006;AzimiZonooz et al, 1989;Seo, 1998a;Seo, 1998b (Joss and Waldvogel, 1990;Andrieu et al, 1997;Gabella and Perona, 1998;Seed et al, 2002;Bech et al, 2003). …”

Section: 아울러 대기 중의 물방울이 모두 지상의 강우로 전환되 지 않는다는 사실도 레이더 강우를 실제 강우와 차이unclassified

Sampling Error of Areal Average Rainfall due to Radar Partial Coverage

Yoo¹,

Ha²,

Kim³

et al. 2008

Journal of Korea Water Resources Association

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This study estimated the error involved in the areal average rainfall derived from incomplete radar information due to radar partial coverage of a basin or sub-basin. This study considers the Han-River Basin as an application example for the rainfall observation using the Ganghwa rain radar. Among the total of 20 mid-sized sub-basins of the Han-River Basin evaluated in this study, only five sub-basins are fully covered by the radar and three are totally uncovered. Remaining 12 sub-basins are partially covered by the radar to result in incomplete radar information available. When only partial radar information is available, the sampling error decreases proportional to the size of the radar coverage, which also varies depending on the number of clusters. Conditioned that the total area coverage remains the same, the sampling error decreases as the number of clusters increases. This study estimated the sampling error of the areal average rainfall of partially-covered mid-sized sub-basins of the Han-River Basin, and the results show that the sampling error could be at least several % to maximum tens % depending on the relative coverage area.

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Section: 아울러 대기 중의 물방울이 모두 지상의 강우로 전환되 지 않는다는 사실도 레이더 강우를 실제 강우와 차이unclassified

Sampling Error of Areal Average Rainfall due to Radar Partial Coverage

Yoo¹,

Ha²,

Kim³

et al. 2008

Journal of Korea Water Resources Association

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“…이러한 목적으로 지상 우량 관측망을 다시 설계하기도 하며, 보정을 위한 적절한 알고리즘을 개발하기도 한다 (North et al, 1994) 현재 많이 시도되고 있는 방법은 co-Kriging을 이용하 는 것이다 (Seo, 1990a;1990b;1991) (Seo, 1991;Todini, 2001;Sinclair and Pegram;. (Kuo et al, 1993;Errico et al, 2000).…”

Section: 서 론unclassified

Synthesis of Radar Measurements and Ground Measurements using the Successive Correction Method(SCM)

Kim¹,

Choi²,

Yoo³

2008

Journal of Korea Water Resources Association

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This study investigated the application of the successive correction method(SCM), a simple data assimilation method, for synthesizing the radar and rain gauge data. First, the number of iteration and influence radius for the SCM application were decided based on their sensitivity analysis. Also, for the evaluation of synthetic rainfall, the distributed rainfall field using the dense rainfall gauge network was assumed to be the true one. The synthetic rainfall field based on the SCM was also compared quantitatively with the one based on the co-Kriging frequently used nowadays. As the results, the SCM, a simple and economical data assimilation method, was found to secure the accuracy and statistical characteristics of the co-Kriging application.

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“…Many daily precipitation data applications, such as drought monitoring, validation of climate models, and ground truth calibration of satellite borne remote sensing devicestropical rainfall measuring mission (TRMM) 602 A. J. RUPP ET AL. (Simpson et al, 1988;North et al, 1994), require several facets of information. These include precipitation frequency, intensity, spatial and temporal variations, as well as the total precipitation amount over a region in both daily and longer time scales.…”

Section: Introductionmentioning

confidence: 99%

An error analysis for the hybrid gridding of Texas daily precipitation data

Rupp

Bailey

Shen

et al. 2009

Intl Journal of Climatology

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This paper reports the error analysis results for the gridded daily precipitation data over the state of Texas of the United States from January 1, 1901 to December 31, 2000. The Global Daily Climatology Network dataset is used for both the data gridding and error analysis. The station data have been interpolated onto a 0.2°× 0.2°grid which starts at the base point (25°50 N, 106°38 W). The data gridding approach is a hybrid method, which is a blend of two simple methods: inverse distance weighting and nearest station assignment. Our gridding results are compared with those obtained by other gridding methods. The cross-validation method is used for the error analysis. Our error analysis of the interpolated products includes not only the conventional errors, such as the mean bias error, but also the probabilistic distribution of the relative errors of precipitation frequency and the spatial distribution of a major Texas historical storm. The following results have been found: (1) a simple arithmetic average of station data usually overestimates Texas' average precipitation by 2.4 mm per day, (2) the relative error of the precipitation frequency follows a lognormal distribution, and (3) the hybrid gridding data do not have obvious bias and can reasonably display storm-covered areas in Texas. The gridded data and error results are useful for the validation of climate models, calibration of satellite borne remote sensing devices, and numerous agricultural and hydrological applications. The statistical methods of our analysis and some of our results are applicable to other regions of the world.

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The Ground-Truth Problem for Satellite Estimates of Rain Rate

Cited by 33 publications

References 0 publications

Sampling Error of Areal Average Rainfall due to Radar Partial Coverage

Sampling Error of Areal Average Rainfall due to Radar Partial Coverage

Synthesis of Radar Measurements and Ground Measurements using the Successive Correction Method(SCM)

An error analysis for the hybrid gridding of Texas daily precipitation data

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