Instrumental Uncertainties inZ–RRelations

Campos, Edwin; Zawadzki, Isztar

doi:10.1175/1520-0450(2000)039<1088:iuizrr>2.0.co;2

Cited by 109 publications

(87 citation statements)

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“…The relations between reflectivity and rainfall-rate are traditionally derived from a linear regression in which the logarithm of rainfall rate is an independent variable. Campos and Zawadski [17] found that the Z-R relationship depends on the regression technique and is therefore highly method dependent. Atlas et al [18] found that the Z-R relationship derived from linear regression produces an accurate representation of the relationship at low rain rates but not necessarily at high rain rates.…”

Section: Measurement Of Rain Integral Parametersmentioning

confidence: 99%

Tropical Rain Classification and Estimation of Rain From Z-R (Reflectivity-Rain Rate) Relationships

Kumar

Lee²,

Yeo³

et al. 2011

PIER B

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Abstract-A Z-R relation is derived using a data set which consists of nine rain events selected from Singapore's drop size distribution. Rain events are separated into convective and stratiform types of rain using two methods: the Gamache-Houze method, a simple threshold technique, and the Atlas-Ulbrich method. In the Atlas-Ulbrich method, the variability of the rain integral parameters R, Z, N w , D 0 and gamma model parameter µ are used for the classification of rain into convective, stratiform and transition. Z-R relations are derived for each type of rain after classification. The changes in the coefficients of the Z-R relations for different rain events are plotted and analyzed. The Z-R relations of the different methods using the Singapore data are compared and analyzed. It is concluded that the coefficient A of the Z-R relation is higher for the convective stage followed by the stratiform and transition stages. The coefficient b values are higher for the transition stage followed by the stratiform and convective stages. Reflectivities are extracted from RADAR data above NTU site for rain events and compared with the reflectivities derived from the distrometer data. Rain rates retrieved from RADAR data using the proposed relations from Singapore's data set are compared with the distrometer rain rates. The RADAR extracted rain rates are found to be constantly lower than the distrometer derived rain rates but matches well.

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Section: Measurement Of Rain Integral Parametersmentioning

confidence: 99%

Tropical Rain Classification and Estimation of Rain From Z-R (Reflectivity-Rain Rate) Relationships

Kumar

Lee²,

Yeo³

et al. 2011

PIER B

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“…If the correlation between Z and R is not close to unity, the values of a and b will vary depending on whether log Z or log R is treated as the independent variable (e.g. Campos and Zawadzki 2000). As we want to minimize errors in rain rate, we consider log Z to be the independent variable in all subsequent linear regressions, and rearrange the above terms to obtain…”

Section: Computation Of Z-r Relationships For Drizzle At Cloud Base Amentioning

confidence: 99%

Reflectivity and rain rate in and below drizzling stratocumulus

Comstock

Wood

Yuter

et al. 2004

Quart J Royal Meteoro Soc

220

304

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SUMMARYShip-based radar measurements obtained during the East Pacific Investigation of Climate 2001 stratocumulus (Sc) cruise are used to derive characteristics of the rainfall from drizzling Sc. Reflectivity to rain rate (Z-R) relationships are determined from shipboard raindrop-size distribution measurements obtained from observations using filter-paper, and compared to Z-R relationships derived from aircraft probe data from below north-east Atlantic drizzling Sc and stratus. A model for the evaporation and sedimentation of drizzle is combined with reflectivity profiles from a millimetre-wavelength cloud radar to derive information on the mean raindrop radius and drizzle drop concentrations at cloud base, and to show how Z-R relationships change with height below the cloud base. The Z-R relationships are used in conjunction with shipborne C-band radar reflectivity data to estimate areal average precipitation with uncertainties at cloud base and at the surface. In the Sc drizzle Z-R relationship, Z = aR b (where a and b are constants), the estimated exponent b = 1.1 to 1.4 is lower than commonly observed in deep convective rain (b = 1.5). Analyses indicate that variations in Sc rain rates and reflectivities are influenced both by fluctuations in drizzle drop concentration and in mean radius, but that number concentration contributes more to the modulation of rain rate in Sc. Rain rates derived using the scanning C-band radar are found to be spatially variable, with much of the accumulation originating from a small fraction of the drizzling area. The observations also suggest that rain rate in marine Sc is strongly dependent on cloud liquidwater path, and inversely correlated with cloud droplet concentration.

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“…They also concluded that taking into account joint fluctuations of these two parameters allowed the wide variability of relations Z-R to be understood. Despite the different uncertainties related to the sampling method and the data chain processing of JW and OSP disdrometers, reliable and consistent information on the microstructure of precipitating systems and mode of variability of the Z-R relationship could be obtained from the combination of drops size and number while the latter relationship is highly dependent on the type of instrument used [31]. Such a result suggests that the microphysical processes responsible for the rainfall production (therefore of the distribution in size and in the number of raindrops) are well observed in the same way through the relations A-α and b-β and this, regardless the measuring instrument.…”

Section: Consistent Character Of Z-r Relation Variabilitymentioning

confidence: 99%

“…In addition, several works, from satellite measurement [30] or from a network of pluviographs at the ground [31] have noted different types of precipitating systems from one climatic zone to another in Africa. This probably involves a variability according to their nature or following the efficiency of microphysical processes, which controls the rain formation.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, the variability in the Z-R relation could derive from uncertainties in DSD measuring instruments. Campos and Zawadzki [31], based on the comparisons of data from three types of collocated disdrometer (JW, OSP and POSS), stressed the instrumental dependence of Z-R relationship coefficients. They noted that the Z-R relationships from the three sensors exhibited differences comparable to those observed in distinct climate regions.…”

Section: Introductionmentioning

confidence: 99%

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Consistency in Z-R Relationship Variability Regardless Precipitating Systems, Climatic Zones Observed from Two Types of Disdrometer

Bamba¹,

Ochou²,

Zahiri³

et al. 2014

ACS

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Data from rain Drop Size Distributions gathered on five sites in Africa as well as those of the pilot site in Kourou (French Guyana, South America), located in different climatic zones, and collected by two types of disdrometer (the impact JW RD-69 disdrometer and the Optical Spectro-Pluviometer, OSP) are used to study the consistency of the reflectivity factor-rain rate at the ground (Z-R) relationship variability. The results clearly confirm that the relationship Z-R knows a large spatial variability, from a type of precipitation to another and within the same precipitation regardless the type of disdrometer used for DSD measurements. Base on the similarity of the relations reflectivity factor-rain rate and ratio median volume diameter over the total number of drops-rain rate, the variability of the Z-R coefficients (A, b) through the simultaneously implication of the size and number of drops which characterize the DSD was exhibited. It was shown that the relationships A-α and b-β designed to understand the involvement of parameters D0 and NT of DSD in the variability of the relationship Z-R are similar regardless the types of disdrometer used. However, the relations A-α in the Sahelian region appear to deviate from those of Guinean, equatorial and Soudanian zones. The plausible reasons were discussed.

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Instrumental Uncertainties inZ–RRelations

Cited by 109 publications

References 0 publications

Tropical Rain Classification and Estimation of Rain From Z-R (Reflectivity-Rain Rate) Relationships

Tropical Rain Classification and Estimation of Rain From Z-R (Reflectivity-Rain Rate) Relationships

Reflectivity and rain rate in and below drizzling stratocumulus

Consistency in <i>Z</i>-<i>R</i> Relationship Variability Regardless Precipitating Systems, Climatic Zones Observed from Two Types of Disdrometer

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Instrumental Uncertainties inZ–RRelations

Cited by 109 publications

References 0 publications

Tropical Rain Classification and Estimation of Rain From Z-R (Reflectivity-Rain Rate) Relationships

Tropical Rain Classification and Estimation of Rain From Z-R (Reflectivity-Rain Rate) Relationships

Reflectivity and rain rate in and below drizzling stratocumulus

Consistency in &lt;i&gt;Z&lt;/i&gt;-&lt;i&gt;R&lt;/i&gt; Relationship Variability Regardless Precipitating Systems, Climatic Zones Observed from Two Types of Disdrometer

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Consistency in <i>Z</i>-<i>R</i> Relationship Variability Regardless Precipitating Systems, Climatic Zones Observed from Two Types of Disdrometer