The WSR-88D Rainfall Algorithm

Fulton, Richard; Breidenbach, Jay P.; Seo, Dong Jun; Miller, Dennis; O'Bannon, Timothy

doi:10.1175/1520-0434(1998)013<0377:twra>2.0.co;2

Cited by 837 publications

(673 citation statements)

References 24 publications

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“…For situations over the USA, hourly NEXt-generation RADar (NEXRAD) precipitation data have been used for additional validation. These data come from NCEP Stage IV radar and hourly accumulated gauge precipitation analyses at 4 km resolution (Baldwin and Mitchell, 1996;Fulton et al, 1998).…”

Section: Observationsmentioning

confidence: 99%

Experimental 1D + 4D‐Var assimilation of CloudSat observations

Janisková

Lopez

Bauer

2011

Quart J Royal Meteoro Soc

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Observations providing three-dimensional information on clouds from space-borne active instruments on board CloudSat and CALIPSO are already available and new satellites, such as EarthCARE, should appear in the near future. This opens new possibilities for exploring the usefulness of this kind of observation, not only for improving model parametrizations but also for investigating their usage in data assimilation to extract information from the data so as to improve the initial atmospheric state. In this study, a 1D + 4D-Var technique has been selected to study the impact of observations related to clouds on 4D-Var analyses and subsequent forecasts. Using this two-step approach, temperature and specific humidity profiles retrieved from 1D-Var assimilation of CloudSat observations have been included in the 4D-Var system. Several experiments have been run for a couple of selected meteorological situations.

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Section: Observationsmentioning

confidence: 99%

Experimental 1D + 4D‐Var assimilation of CloudSat observations

Janisková

Lopez

Bauer

2011

Quart J Royal Meteoro Soc

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“…The radar distance zone in this expression is fully determined by coordinates x and y. In the application discussed in this study, the radar maps, R R (x, y), are given as the original Digital Precipitation Array (DPA) products generated by the Precipitation Processing System module of the NEXRAD (Next Generation Weather Radar) system [e.g., Fulton et al, 1998]. By using the power law form in (2), the areas with zero radar rainfall values are preserved by the model (15) because of its multiplicative structure.…”

Section: Ensemble Generatormentioning

confidence: 99%

Product‐error‐driven generator of probable rainfall conditioned on WSR‐88D precipitation estimates

Villarini

Krajewski

Ciach

et al. 2009

Water Resources Research

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[1] The existence of large errors in precipitation products delivered by the network of Weather Surveillance Radar, 1988 Doppler (WSR-88D) radars is broadly recognized. However, their quantitative characteristics remain poorly understood. Recently, the authors developed a functional-statistical model that quantifies the relation between radar rainfall and the corresponding true rainfall in a way that is applicable to the probabilistic quantitative precipitation estimation planned for future use by the U.S. National Weather Service. The model consists of a deterministic distortion function and a random uncertainty factor, both conditioned on given radar rainfall values. It also accounts for the spatiotemporal correlations in the random uncertainty factor. The model components were estimated on the basis of a 6-year-long data sample that considers the effects of seasons, range from radar, and time scales. In this study, the authors present two different applications of the aforementioned uncertainty model: (1) the estimation of rainfall probability maps and (2) the generation of radar rainfall ensembles. In the former, maps of the rainfall exceedance probability for any threshold are produced, given a radar rainfall map. We also present the analytical derivation of the exceedance probability maps at coarser spatial scales. In the latter, the users can generate ensembles of probable true rainfall fields that are consistent with the observed radar rainfall and its error structure. Simulation of the random component is based on the Cholesky decomposition method. Finally, the authors discuss possible uses of these applications in hydrology and hydroclimatology.

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“…They use various statistics derived from radar measurements that highlight the characteristics of clutter echoes that best differentiate them from precipitation (mainly shallow vertical extent, a high degree of spatial variability, and velocities close to zero). Most of these algorithms are based on a pixel-by-pixel analysis [with the notable exception of the Weather Surveillance Radar-1988 Doppler (WSR-88D) algorithm (Fulton et al 1998), which rejects the first plan position indicator (PPI) for the purposes of retrieving precipitation when a significantly higher number of echoes affect it than the second tilt, on the assumption that it is affected by AP clutter]. These algorithms can be grouped into the following two classes: 1) those based on decision trees, and 2) those that use more complex techniques based on probabilistic analysis, fuzzy logic, or neural networks [these concepts are reviewed in Kosko (1992)].…”

Section: Introductionmentioning

confidence: 99%

A Fuzzy Logic Technique for Identifying Nonprecipitating Echoes in Radar Scans

Berenguer

Sempere‐Torres

Corral

et al. 2006

Journal of Atmospheric and Oceanic Technology

118

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Because echoes caused by nonmeteorological targets significantly affect radar scans, contaminated bins must be identified and eliminated before precipitation can be quantitatively estimated from radar measurements.Under mean propagation conditions, clutter echoes (mainly caused by targets such as mountains or large buildings) can be found in almost fixed locations. However, in anomalous propagation conditions, new clutter echoes may appear (sometimes over the sea), and they may be difficult to distinguish from precipitation returns. Therefore, an automatic algorithm is needed to identify clutter on radar scans, especially for operational uses of radar information (such as real-time hydrology).In this study, a new algorithm is presented based on fuzzy logic, using volumetric data. It uses some statistics to highlight clutter characteristics (namely, shallow vertical extent, high spatial variability, and low radial velocities) to output a value that quantifies the possibility of each bin being affected by clutter (in order to remove those in which this factor exceeds a certain threshold).The performance of this algorithm was compared against that of simply removing mean clutter echoes. Satisfactory results were obtained from an exhaustive evaluation of this algorithm, especially in those cases in which anomalous propagation played an important role.

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The WSR-88D Rainfall Algorithm

Cited by 837 publications

References 24 publications

Experimental 1D + 4D‐Var assimilation of CloudSat observations

Experimental 1D + 4D‐Var assimilation of CloudSat observations

Product‐error‐driven generator of probable rainfall conditioned on WSR‐88D precipitation estimates

A Fuzzy Logic Technique for Identifying Nonprecipitating Echoes in Radar Scans

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