An evaluation of NEXRAD precipitation estimates in complex terrain

Young, C. Bryan; Nelson, Brian R.; Bradley, A. Allen; Smith, James A.; Peters‐Lidard, Christa D.; Kruger, Anton; Baeck, Mary Lynn

doi:10.1029/1999jd900123

Cited by 128 publications

(86 citation statements)

References 23 publications

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“…The resulting product is known operationally at NWS as hourly digital precipitation (HDP). An evaluation of HDP products has been carried out by Smith et al (1996) for the southern plains and by Young et al (1999) for the complex mountainous terrain of the northern Appalachians.…”

Section: A Nexrad Hourly Digital Productsmentioning

confidence: 99%

See 1 more Smart Citation

Evaluating NEXRAD Multisensor Precipitation Estimates for Operational Hydrologic Forecasting

Young¹,

Bradley²,

Krajewski³

et al. 2000

J. Hydrometeor

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154

129

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Next-Generation Weather Radar (NEXRAD) multisensor precipitation estimates will be used for a host of applications that include operational streamflow forecasting at the National Weather Service River Forecast Centers (RFCs) and nonoperational purposes such as studies of weather, climate, and hydrology. Given these expanding applications, it is important to understand the quality and error characteristics of NEXRAD multisensor products. In this paper, the issues involved in evaluating these products are examined through an assessment of a 5.5-yr record of multisensor estimates from the Arkansas-Red Basin RFC. The objectives were to examine how known radar biases manifest themselves in the multisensor product and to quantify precipitation estimation errors. Analyses included comparisons of multisensor estimates based on different processing algorithms, comparisons with gauge observations from the Oklahoma Mesonet and the Agricultural Research Service Micronet, and the application of a validation framework to quantify error characteristics. This study reveals several complications to such an analysis, including a paucity of independent gauge data. These obstacles are discussed and recommendations are made to help to facilitate routine verification of NEXRAD products.

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Section: A Nexrad Hourly Digital Productsmentioning

confidence: 99%

“…Two needs motivated this evaluation. The first was to examine the effect of known biases in radar estimates of hourly precipitation on the multisensor products (Smith et al 1996;Young et al 1999). The second was to evaluate the uncertainty in multisensor precipitation estimates.…”

Section: Introductionmentioning

confidence: 99%

Evaluating NEXRAD Multisensor Precipitation Estimates for Operational Hydrologic Forecasting

Young¹,

Bradley²,

Krajewski³

et al. 2000

J. Hydrometeor

Self Cite

154

129

View full text Add to dashboard Cite

show abstract

“…지상 레이더로부터의 강우 변동성 추정 은 조밀하지 않은 레이더 탐지범위와 지형차단(terrain blocking) 효과, 반사도(reflectivity)와 강우량 관계에서의 변동성, 혼합위상(mixed-phase) 및 고체강수(solid precipitation) 추정과 관련하여 어려움이 따른다 (Westrick et al, 1999;Young et al, 1999;Dinku et al, 2002). 그리 고 대기모델(atmospheric model)로부터 강우의 변동성에 대한 정량적 추정은 모델의 수평해상도, 미세물리적 매개 변수화 기법(microphysical parameterization scheme)의 부족 및 강우과정에 대한 불충분한 이해 때문에 제한을 받는다 (Colle et al, 2000;Schultz et al, 2002;Stoelinga et al, 2003).…”

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Uncertainty Analysis of Spatial Distribution of Probability Rainfall: Comparison of CEM and SGS Methods

Seo¹,

Yeo²,

Lee³

et al. 2010

Journal of Korea Water Resources Association

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This study compares the CEM and SGS methods which are geostatistical stochastic simulation methods for assessing the uncertainty by spatial variablility in the estimation of the spatial distribution of probability rainfall. In the stochastic simulations using CEM and SGS, two methods show almost similar results for the reproduction of spatial correlation structure, the statistics (standard deviation, coefficient of variation, interquartile range, and range) of realizations as uncertainty measures, and the uncertainty distribution of basin mean rainfall. However, the CEM is superior to SGS in aspect of simulation efficiency.

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“…However, radar data have important disadvantages because they are an indirect measure of rainfall (Uijlenhoet and Pomeroy, 2001) and with worse results of rainfall depth in a given location compared to rain gauge data (Hunter, 1996;Young et al, 1999). Many studies, therefore, have been realized to minimize radar errors (Chumchean et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

Analysis of predicted and observed accumulated convective precipitation in the area with frequent split storms

Ćurić

Janc

2011

Hydrol. Earth Syst. Sci.

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Abstract. Convective clouds generate extreme rainfall events and flash floods in small areas with both large spatial and temporal variability. For this reason, the monitoring of the total accumulated precipitation fields at the surface with rain gauges and meteorological radars has both strengths and weakness. Alternatively, a numerical cloud model may be a useful tool to simulate convective precipitation for various analyses and predictions. The main objective of this paper is to show that the cloud-resolving model reproduces well the accumulated convective precipitation obtained from the rain gauge network data in the area with frequent split storms. We perform comparisons between observations and model samples of the areal accumulated convective precipitation for a 15-year period over treated area. Twenty-seven convective events have been selected. Statistical analyses reveal that the model areal accumulated convective precipitation closely match their observed values with a correlation coefficient of 0.80.

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An evaluation of NEXRAD precipitation estimates in complex terrain

Cited by 128 publications

References 23 publications

Evaluating NEXRAD Multisensor Precipitation Estimates for Operational Hydrologic Forecasting

Evaluating NEXRAD Multisensor Precipitation Estimates for Operational Hydrologic Forecasting

Uncertainty Analysis of Spatial Distribution of Probability Rainfall: Comparison of CEM and SGS Methods

Analysis of predicted and observed accumulated convective precipitation in the area with frequent split storms

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