Accuracy of rainfall estimates by radar, part I: Calibration by telemetering raingauges

Collier, C. G.

doi:10.1016/0022-1694(86)90152-6

Cited by 115 publications

(58 citation statements)

References 6 publications

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“…Assessment Factor Versus Distance, Height of Visibility, and Height of Ground Kitchen and Blackall [1992] illustrate the problems concerning the large scatter of instantaneous values for a given location of the two types of instruments (radars and gages) caused by differences in sampling modes. In many cases, and certainly for hydrological purposes, it is wise to integrate in time [e.g., Collier, 1986;Zawadzki, 1975]. Then the Assessment Factor (AF) for each radar-gage data pair is defined as the ratio …”

Section: Description Of Storm and Instrumentsmentioning

confidence: 99%

Accuracy of rainfall estimates by two radars in the same Alpine environment using gage adjustment

Gabella¹,

Joss²,

Perona³

et al. 2001

J. Geophys. Res.

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Abstract.A technique is analyzed to mitigate the hostile effects of a complex orography on precipitation estimated by radar. Radar observations are adjusted using a network of gages. The corrections of radar estimates are derived through a Weighted Multiple Regression as a function of (1) the distance from the radar, (2) the minimum height a meteorological target must reach to be visible from the radar site, (3) the height of the ground at each pixel. Two C-band radars 140 km apart in an Alpine region are analyzed.About 60 radar-gage data pairs are available for each radar in a 25,000 km 2 area during an extreme Mediterranean event (precipitation measured by the gages during the 40-hour observation period ranges from 36 to 444 mm). In the radar-gage comparison the data pairs are divided into two groups: one is used for training, the other for testing, and vice versa. All the radar-gage data pairs are used for training, when comparing the two radars. Then radar-derived precipitation estimates are compared in a "mutual coverage" area (•10,000 km2). In all cases the correction significantly reduces the bias and the standard deviation of precipitation difference. In spite of the different ages, technologies, and distances from the area of interest, both radars have shown similar behavior, and the proposed procedure can be successfully applied to both.

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Section: Description Of Storm and Instrumentsmentioning

confidence: 99%

Accuracy of rainfall estimates by two radars in the same Alpine environment using gage adjustment

Gabella¹,

Joss²,

Perona³

et al. 2001

J. Geophys. Res.

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“…[2] Information on the amount of rainfall over a river basin is a primary input to most real-time flood forecasting systems [Collier, 1986]. Rain gauge data provide good point accuracy, but offer little information on the spatial distribution of rainstorms, especially in convective type situations.…”

Section: Introductionmentioning

confidence: 99%

“…In reality, however, such fine-scale modeling of semivariograms is rarely (if ever) possible because of the lack of rain gauge measurements [Seo and Breidenbach, 2002]. There are also a number of techniques based on interpolation of local biases, expressed as gaugeto-radar ratios [e.g., Brandes, 1975;Collier, 1986], whose interpolation weights are obtained exclusively on the basis of the spatial distribution of the rain gauges. Despite the latter techniques do not pursue any statistical optimization problem, they are generally well suited for operational realtime use since they are robust and generate results which are more quantitatively useful than unadjusted radar data .…”

Section: Introductionmentioning

confidence: 99%

Rainfall estimation by rain gauge‐radar combination: A concurrent multiplicative‐additive approach

García-Pintado

Barberá

Erena³

et al. 2009

Water Resources Research

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[1] A procedure (concurrent multiplicative-additive objective analysis scheme [CMA-OAS]) is proposed for operational rainfall estimation using rain gauges and radar data. On the basis of a concurrent multiplicative-additive (CMA) decomposition of the spatially nonuniform radar bias, within-storm variability of rainfall and fractional coverage of rainfall are taken into account. Thus both spatially nonuniform radar bias, given that rainfall is detected, and bias in radar detection of rainfall are handled. The interpolation procedure of CMA-OAS is built on Barnes' objective analysis scheme (OAS), whose purpose is to estimate a filtered spatial field of the variable of interest through a successive correction of residuals resulting from a Gaussian kernel smoother applied on spatial samples. The CMA-OAS, first, poses an optimization problem at each gauge-radar support point to obtain both a local multiplicative-additive radar bias decomposition and a regionalization parameter. Second, local biases and regionalization parameters are integrated into an OAS to estimate the multisensor rainfall at the ground level. The procedure is suited to relatively sparse rain gauge networks. To show the procedure, six storms are analyzed at hourly steps over 10,663 km 2 . Results generally indicated an improved quality with respect to other methods evaluated: a standard mean-field bias adjustment, a spatially variable adjustment with multiplicative factors, and ordinary cokriging.

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“…However, because rain gauges themselves are prone to several error sources, the concept of ground truth is questionable: "ground truth is the amount of rain that would have reached the ground if the rain gauge had not been there". Moreover, after adjustment of the radar data using rain gauge measurements (called "calibration" at the time (e.g., Collier, 1986)), several errors and inconsistencies remained which this approach was not able to resolve. As opposed to the largely statistical approach of the 1980s, the more physical approach to radar rainfall retrieval adopted since the 1990s considers the principle of radar measurements and the microstructure of rainfall in quite some detail (e.g., Smith et al, 1996;Andrieu et al, 1997;Creutin et al, 1997;Serrar et al, 2000;Sánchez-Diezma et al, 2000;Berne et al, 2005a,b;Delrieu et al, 2005;Berenguer et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

Stochastic simulation experiment to assess radar rainfall retrieval uncertainties associated with attenuation and its correction

Uijlenhoet

Berne

2008

Hydrol. Earth Syst. Sci.

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Abstract.As rainfall constitutes the main source of water for the terrestrial hydrological processes, accurate and reliable measurement and prediction of its spatial and temporal distribution over a wide range of scales is an important goal for hydrology. We investigate the potential of ground-based weather radar to provide such measurements through a theoretical analysis of some of the associated observation uncertainties. A stochastic model of range profiles of raindrop size distributions is employed in a Monte Carlo simulation experiment to investigate the rainfall retrieval uncertainties associated with weather radars operating at X-, C-, and S-band. We focus in particular on the errors and uncertainties associated with rain-induced signal attenuation and its correction for incoherent, non-polarimetric, single-frequency, operational weather radars. The performance of two attenuation correction schemes, the (forward) Hitschfeld-Bordan algorithm and the (backward) Marzoug-Amayenc algorithm, is analyzed for both moderate (assuming a 50 km path length) and intense Mediterranean rainfall (for a 30 km path). A comparison shows that the backward correction algorithm is more stable and accurate than the forward algorithm (with a bias in the order of a few percent for the former, compared to tens of percent for the latter), provided reliable estimates of the total path-integrated attenuation are available. Moreover, the bias and root mean square error associated with each algorithm are quantified as a function of path-averaged rain rate and distance from the radar in order to provide a plausible order of magnitude for the uncertainty in radar-retrieved rain rates for hydrological applications.

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Accuracy of rainfall estimates by radar, part I: Calibration by telemetering raingauges

Cited by 115 publications

References 6 publications

Accuracy of rainfall estimates by two radars in the same Alpine environment using gage adjustment

Accuracy of rainfall estimates by two radars in the same Alpine environment using gage adjustment

Rainfall estimation by rain gauge‐radar combination: A concurrent multiplicative‐additive approach

Stochastic simulation experiment to assess radar rainfall retrieval uncertainties associated with attenuation and its correction

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