Spatial characterization of raincell horizontal profiles from C-band radar measurements at mid-latitude

Montopoli, Mario; Marzano, Frank S.; Vulpiani, G.; Fornasiero, A.; Alberoni, P. P.; Ferraris, Luca; Rebora, Nicola

doi:10.5194/adgeo-7-285-2006

Cited by 11 publications

(11 citation statements)

References 19 publications

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“…The general scheme to accomplish this task includes two steps: first, the reduction of the dimension of the rain-cell observable set exploiting their similarities, and second, the definition of an error criterion (or distance) between the measured and model-pattern observable features [28], [30].…”

Section: B Maximum-likelihood Retrieval Methodsmentioning

confidence: 99%

“…Other approaches define a rain-cell area by means of the minimum rain-rate contour separating two cores [20]. In this paper, we have exploited a new definition of rain cell which has the advantage to be spatially adaptive, as detailed in [28]. By means of this identification algorithm, we have extracted 20 560 rain cells from the 1558 rainfall Cartesian maps.…”

Section: B Rain-cell Adaptive Identification Techniquementioning

confidence: 98%

“…For k = 1, k s = 2, and p = h, the HYCELL model is described [5], whereas for k = 2, k s = 2, and p = d, we obtain the new model introduced in this paper, which is named DEXCELL. Note that R Tl is derived from the rain-cell recognition procedure, as illustrated in [28]. As an example, Fig.…”

Section: A Rain-cell Pattern Models and Spatial Integrationmentioning

confidence: 99%

“…As a result, the measured dataset has been converted into a temporal series of 1558 rain-rate Cartesian maps R m (or frames) of 256 × 256 pixels [28]. Fig.…”

Section: A Radar Measurementsmentioning

confidence: 99%

“…The classification of rain systems can benefit from the use of radar measurements [20], [22]. From the latter, several features can be derived, such as rain-rate peak, average, and root-mean-square value together with rain-cell area, ellipticity ratio, gradient average, and deviation [10], [28], [30].…”

Section: Introductionmentioning

confidence: 99%

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Maximum-Likelihood Retrieval of Modeled Convective Rainfall Patterns from Midlatitude C-Band Weather Radar Data

Montopoli

Marzano

2007

IEEE Trans. Geosci. Remote Sensing

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A spatial characterization of the midlatitude mesoscale rain fields from C-band radar measurements is performed by means of a systematic analysis and modeling of convective rain-cell bidimensional shapes and spatial correlation. A large rainfall dataset that is derived from an operational C-band dual-polarized radar, which is placed in S. Pietro Capofiume near Bologna (Italy), has been collected and analyzed for this purpose. Different models of convective rainy horizontal structures are described and compared. Special attention is devoted to the consolidated unimodal models (or unimodal patterns) like GAUCELL with a Gaussian rain-rate profile and EXCELL with an exponential rain-rate profile, and the hybrid models like HYCELL and DEXCELL based on a proper combination of the previous unimodal models. The new hybrid model DEXELL, which is introduced here, is an extension of the HYCELL model, which is previously proposed in literature. A pixel-by-pixel model numerical integration is carried out in order to perform a homogeneous comparison between the rain-cell model and the measured features such as peak, average, root mean square, gradient average, and gradient deviation of rain rate. A maximum-likelihood algorithm, which is expressed in terms of the principal component of the previous rain-cell features, is introduced to estimate rain-cell pattern parameters from the available radar data. A detailed sensitivity analysis, which is devoted to find the best behavior in terms of root-mean-square error and correlation coefficient between the modeled and measured rain-cell features, is finally carried out.

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Section: B Maximum-likelihood Retrieval Methodsmentioning

confidence: 99%

Section: B Rain-cell Adaptive Identification Techniquementioning

confidence: 98%

Section: A Rain-cell Pattern Models and Spatial Integrationmentioning

confidence: 99%

“…As a result, the measured dataset has been converted into a temporal series of 1558 rain-rate Cartesian maps R m (or frames) of 256 × 256 pixels [28]. Fig.…”

Section: A Radar Measurementsmentioning

confidence: 99%