Distribution of Rainfall Parameters near the Coasts of France and Senegal

Nzeukou, Armand; Sauvageot, Henri

doi:10.1175/1520-0450(2002)041<0069:dorpnt>2.0.co;2

Cited by 28 publications

(22 citation statements)

References 24 publications

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“…Up to now, radar maxima of reflectivity in rain fields have never been shown to be significantly influenced by the radar resolution. In fact, the statistical study of the rain field characteristics from radar data does not show any dependency on the distance from the radar, neither for the rain rate parameters (notably R max ) nor for the rain cell size distribution [e.g., Nzeukou and Sauvageot, 2002] (among many others).…”

Section: Excell Modelmentioning

confidence: 99%

HYCELL—A new hybrid model of the rain horizontal distribution for propagation studies: 1. Modeling of the rain cell

et al. 2003

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[1] From radar observations of rain fields at midlatitudes, a new physical model of rain cells is proposed. It strives to describe optimally the rain rate horizontal distribution within rain cells down to 1 mm h À1 . The approach is similar to that of the well-known EXCELL model. The mathematical definition of the model lies in the combination of a gaussian function and an exponential one, the cells having an elliptic horizontal cross section. Due to its hybrid structure, the new model has been named HYCELL. From a conceptual point of view, the gaussian component describes the convective-like high rain rate core of the cell, while the exponential component accounts for the surrounding stratiform-like low rain rate spreading down to 1 mm h À1 . The modeling of a rain cell with HYCELL then requires the determination of seven parameters. The latter is obtained, cell by cell, by solving a set of five fit-forcing equations completed by two continuity equations. The fit-forcing equations involve radar parameters of integral nature which refer not only to the rain cell geometry (area, ellipticity) but also to the rain rate R distribution inside the cell (mean and root mean square values of R and gradient of R). Their analytical expressions are derived from the model definition, while their values are forced to be those derived from radar measurements. Using this method, thousands of rain cells identified from radar observations in the regions of Bordeaux (southwestern France) and Karlsruhe (southwestern Germany) have been modeled. Though both sites are at midlatitude, the climatic contexts differ: oceanic for Bordeaux and continental for Karlsruhe. Results of rain rate horizontal distribution modeling within cells using HYCELL and EXCELL are compared. It is then suggested that the HYCELL model is a new tool which deserves to be considered by system designers to compute propagation parameters.

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Section: Excell Modelmentioning

confidence: 99%

HYCELL—A new hybrid model of the rain horizontal distribution for propagation studies: 1. Modeling of the rain cell

et al. 2003

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“…2) on the top of a tower of 20-m height (i.e., the radar is 70 m MSL). The distribution of rainfall parameters over the Bordeaux area has been analyzed in detail by Nzeukou and Sauvageot (2002) from the radar data of 1996. Most of the rainy events are zonal frontal cyclonic disturbances moving eastward, from sea toward land, with the general circulation at midlatitude.…”

Section: Datamentioning

confidence: 99%

Climatology of Anomalous Propagation Radar Echoes in a Coastal Area

Mesnard

Sauvageot

2010

Journal of Applied Meteorology and Climatology

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International audienceAnomalous propagation (AP) of ground-based radar beam results in the detection of ground echoes beyond the horizon. One year of data gathered with an S-band meteorological radar located on the coast in southwest France is used to analyze the spatial distribution of AP ground echoes (APE). The APE distributions of duration and reflectivity in the radar-observed area are found to be strongly related to the main feature of the regional orography and topography up to the farthest distance (250 km) observed by the radar, notably the nature of the surface, the topographic orientation with respect to the radar beam direction, and the altitude. The distribution of APE in the studied area is found to be strongly anisotropic around the radar, with wide differences between land and sea. Rain accumulation equivalent to the APE is, in certain places, of the same order or higher than the real rain depth. The distribution of the ground surfaces, as calculated from a ground numerical model, compares qualitatively well with the APE radar reflectivity distribution

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“…Surface rain rate distributions have been commonly parameterized by lognormal functions (Houze and Cheng 1977;Kedem and Chiu 1987;Kedem et al 1990Kedem et al , 1997Sauvageot 1994;Nzeukou and Sauvageot 2002), although some observations showed departures from the lognormal distribution (Jameson and Kostinski 1999) and some suggested that rain rates followed gamma distributions (Ison et al 197 1 ; Swift and Schreuder 198 1 ; Wilks and Eggleston 1992). Since Cho et al (2004) found that both lognormal and gamma distributions were able to characterize the PDF of rain rates from TRMM data, we used lognormal functions as the prior distribution.…”

Section: B the Prior Distribution Of Rain Ratementioning

confidence: 99%

Bayesian Retrieval of Complete Posterior PDFs of Oceanic Rain Rate from Microwave Observations

Petty

2006

Journal of Applied Meteorology and Climatology

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This paper presents a new Bayesian algorithm for retrieving surface rain rate from Tropical Rainfall Measurements Mission (TRMM) Microwave Imager (TMI) over the ocean, along with validations against estimates from the TRMM Precipitation Radar (PR). The Bayesian approach offers a rigorous basis for optimally combining multichannel observations with prior knowledge.While other rain rate algorithms have been published that are based at least partly on Bayesian reasoning, this is believed to be the first self-contained algorithm that fully exploits Bayes' Theorem to yield not just a single rain rate, but rather a continuous posterior probability distribution of rain rate.To advance our understanding of theoretical benefits of the Bayesian approach, we have conducted sensitivity analyses based on two synthetic datasets for which the "true" conditional and prior distribution are known. Results demonstrate that even when the prior and conditional likelihoods are specified perfectly, biased retrievals may occur at high rain rates. This bias is not the result of a defect of the Bayesian formalism but rather represents the expected outcome when the physical constraint imposed by the radiometric observations is weak, due to saturation effects. It is also suggested that the choice of the estimators and the prior information are both crucial to the retrieval. In addition, the performance of our Bayesian algorithm is found to be comparable to that of other benchmark algorithms in real-world applications, while having the additional advantage of providing a complete continuous posterior probability distribution of surface rain rate.2

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Distribution of Rainfall Parameters near the Coasts of France and Senegal

Cited by 28 publications

References 24 publications

HYCELL—A new hybrid model of the rain horizontal distribution for propagation studies: 1. Modeling of the rain cell

HYCELL—A new hybrid model of the rain horizontal distribution for propagation studies: 1. Modeling of the rain cell

Climatology of Anomalous Propagation Radar Echoes in a Coastal Area

Bayesian Retrieval of Complete Posterior PDFs of Oceanic Rain Rate from Microwave Observations

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