A Bayesian technique for conditioning radar precipitation estimates to rain-gauge measurements

Todini, E.

doi:10.5194/hess-5-187-2001

Cited by 147 publications

(94 citation statements)

References 28 publications

(26 reference statements)

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“…Besides, other contributions oriented to hydrological modelling have merged radar data with raingauge observations making use of geostatistical techniques to clean ground clutter or AP echoes from radar images (see, for example, Todini, 2001;or Wesson and Pegram, 2004).…”

Section: Introductionmentioning

confidence: 99%

Enhanced radar precipitation estimates using a combined clutter and beam blockage correction technique

Fornasiero

Bech

Alberoni³

2006

Nat. Hazards Earth Syst. Sci.

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Abstract. Weather radar observations are currently the most reliable method for remote sensing of precipitation. However, a number of factors affect the quality of radar observations and may limit seriously automated quantitative applications of radar precipitation estimates such as those required in Numerical Weather Prediction (NWP) data assimilation or in hydrological models. In this paper, a technique to correct two different problems typically present in radar data is presented and evaluated. The aspects dealt with are non-precipitating echoes -caused either by permanent ground clutter or by anomalous propagation of the radar beam (anaprop echoes) -and also topographical beam blockage. The correction technique is based in the computation of realistic beam propagation trajectories based upon recent radiosonde observations instead of assuming standard radio propagation conditions. The correction consists of three different steps: 1) calculation of a Dynamic Elevation Map which provides the minimum clutter-free antenna elevation for each pixel within the radar coverage; 2) correction for residual anaprop, checking the vertical reflectivity gradients within the radar volume; and 3) topographical beam blockage estimation and correction using a geometric optics approach. The technique is evaluated with four case studies in the region of the Po Valley (N Italy) using a C-band Doppler radar and a network of raingauges providing hourly precipitation measurements. The case studies cover different seasons, different radio propagation conditions and also stratiform and convective precipitation type events. After applying the proposed correction, a comparison of the radar precipitation estimates with raingauges indicates a general reduction in both the root mean squared error and the fractional error variance indicating the efficiency and robustness of the proceCorrespondence to: P. P. Alberoni (palberoni@arpa.emr.it) dure. Moreover, the technique presented is not computationally expensive so it seems well suited to be implemented in an operational environment.

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Section: Introductionmentioning

confidence: 99%

Enhanced radar precipitation estimates using a combined clutter and beam blockage correction technique

Fornasiero

Bech

Alberoni³

2006

Nat. Hazards Earth Syst. Sci.

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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

“…2.1 연속수정법 (Successive Correction Method; SCM) Bergthórsson and Döös(1955), Cressman(1957) (Seo, 1991;Todini, 2001). …”

Section: 배경 이론mentioning

confidence: 99%

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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“…However, with today's widespread production and high level of access to (real-time) data from the various sources, the possibility of improving forecasting by blending data from several sources is apparent (e.g. Todini, 2001). Each source has its own advantages but also errors, which may be reflected in both systematical biases and random deviations.…”

Section: Introductionmentioning

confidence: 99%

Spatio-temporal precipitation error propagation in runoff modelling: a case study in central Sweden

Olsson

2006

Nat. Hazards Earth Syst. Sci.

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Abstract. The propagation of spatio-temporal errors in precipitation estimates to runoff errors in the output from the conceptual hydrological HBV model was investigated. The study region was the Gimån catchment in central Sweden, and the period year 2002. Five precipitation sources were considered: NWP model (H22), weather radar (RAD), precipitation gauges (PTH), and two versions of a mesoscale analysis system (M11, M22). To define the baseline estimates of precipitation and runoff, used to define seasonal precipitation and runoff biases, the mesoscale climate analysis M11 was used. The main precipitation biases were a systematic overestimation of precipitation by H22, in particular during winter and early spring, and a pronounced local overestimation by RAD during autumn, in the western part of the catchment. These overestimations in some cases exceeded 50% in terms of seasonal subcatchment relative accumulated volume bias, but generally the bias was within ±20%. The precipitation data from the different sources were used to drive the HBV model, set up and calibrated for two stations in Gimån, both for continuous simulation during 2002 and for forecasting of the spring flood peak. In summer, autumn and winter all sources agreed well. In spring H22 overestimated the accumulated runoff volume by ∼50% and peak discharge by almost 100%, owing to both overestimated snow depth and precipitation during the spring flood. PTH overestimated spring runoff volumes by ∼15% owing to overestimated winter precipitation. The results demonstrate how biases in precipitation estimates may exhibit a substantial space-time variability, and may further become either magnified or reduced when applied for hydrological purposes, depending on both temporal and spatial variations in the catchment. Thus, the uncertainty in precipitation estimates should preferably be specified as a function of both time and space.

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A Bayesian technique for conditioning radar precipitation estimates to rain-gauge measurements

Cited by 147 publications

References 28 publications

Enhanced radar precipitation estimates using a combined clutter and beam blockage correction technique

Enhanced radar precipitation estimates using a combined clutter and beam blockage correction technique

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

Spatio-temporal precipitation error propagation in runoff modelling: a case study in central Sweden

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