On the measurement of atmospheric gamma drop‐size distributions

Brawn, Dan; Upton, Graham

doi:10.1002/asl.198

Cited by 20 publications

(16 citation statements)

References 5 publications

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“…Table 1 shows the rain categories, the coefficients of the µ-Λ relations with 95% confidence bounds and Pearson correlation coefficients. The different rain categorizations, i.e., R ≥ 5 mm/hr & drops ≥ 1000 used by Brandes et al [5], z ≥ 30 dBZ & drops ≥ 500 used by Narayana et al [12], and R ≥ 1 mm/hr used by Brawn and Upton [13], are also included in the table for comparison with other published results. As can be seen in Table 1, for the rain categories for which there are more lower rain rates included, such as R < 2 mm/hr and R ≥ 1 mm/hr, the coefficient 'C' of the polynomial fit is not only negative, but very small, with values of −0.004 and −0.005 respectively.…”

Section: µ-λ Relationshipmentioning

confidence: 99%

“…The linear fit is less complex than the polynomial fit, and therefore, the linear fit is recommended. Figure 3 shows the µ-Λ fits of Singapore with different filtering; the three proposed rain categories; R ≥ 5 mm/hr & drops ≥ 1000; [13].…”

Section: µ-λ Relationshipmentioning

confidence: 99%

“…Recently, Brawn and Upton [13] fitted the µ-Λ relation for rain rates greater than 1 mm/hr based on their drop size data measured at Dumfries and Galloway, Scotland, using the Thies and Parsivel distrometers. The procedure described by Brawn and Upton [13] to estimate the values of gamma parameters was used. They filtered all the rain rates less than 1 mm/hr from their drop size data to fit the relation.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, three different categories of rain rates, R < 2 mm/hr (stratiform), 2 mm/hr ≤ R < 10 mm/hr (stratiform, transition and convective type rain) and R ≥ 10 mm/hr (convective type rain), are considered in order to fit the µ-Λ relations for the 996 minutes of drop size data for Singapore. The rain categorization used by [5,12,13] are also considered for the µ-Λ relations fitting purposes, so as to provide a fair comparison of the different types of rains at the different climatic zones.…”

Section: Introductionmentioning

confidence: 99%

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Shape Slope Parameter Distribution Modelling of Electromagnetic Scattering by Rain Drops

Kumar¹,

Lee²,

Ong³

2010

PIER B

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Abstract-Gamma model parameters using 2nd, 3rd and 4th moments are calculated from the drop size data of Singapore. The gamma model is simplified into two parameter model by finding a relation between the shape and slope parameters, µ and Λ. Due to the poor correlation found between µ and Λ, the drop size data is filtered based on their rain rates before a good correlation between the two parameters can be found. The µ-Λ relations are then fitted for the different ranges of rain rate filtering. Scatter plots of µ and Λ are plotted with constant median volume diameter (D 0 ) lines. The µ-Λ relations for the different rain types for the tropical island of Singapore are proposed and compared with the µ-Λ relations from three other countries of different climatic zones. T-Matrix calculations are performed to find the polarimetric variables at S-band by using the gamma DSD calculated from the Singapore's drop size data. The calculated differential reflectivity and horizontal reflectivity are used along with the best µ-Λ relations to find the gamma model parameters. The retrieved rain rate using polarimetric variables is compared with the distrometer's measured rain rate. Results show a good agreement between the retrieved rain rate and the measured rain rate. Therefore, the proposed shape slope relationship is found to be suitable for rain rate retrieval.

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Section: µ-λ Relationshipmentioning

confidence: 99%

Section: µ-λ Relationshipmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Shape Slope Parameter Distribution Modelling of Electromagnetic Scattering by Rain Drops

Kumar¹,

Lee²,

Ong³

2010

PIER B

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“…To our knowledge, however, only the works of Brawn and Upton (2008) and Upton and Brawn (2008) inter-compared the performance of Thies and PARSIVEL disdrometers in terms of the PSVD and precipitation bulk variables, as well as in terms of the fitted gamma distribution parameters. In their studies they used data from an older PARSIVEL version, and no study up to date has focused on comparing the Thies LPM and the PARSIVEL 2 devices.…”

mentioning

confidence: 99%

Comparison of precipitation measurements by Ott Parsivel<sup>2</sup> and Thies LPM optical disdrometers

Angulo‐Martínez¹,

Beguería²,

Latorre³

et al. 2017

Preprint

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Abstract.Optical disdrometers are present weather sensors with the ability of providing detailed information of precipitation such as rain intensity, kinetic energy or radar reflectivity, together with discrete information on the distribution of particle sizes and fall velocities (PSVD) of the hydrometeors. Disdrometers constitute a step forward towards a more complete characterisation of precipitation, being highly useful in several research fields and applications. In this article the performance of the two optical . The results show significant differences between both disdrometer types for all integrated precipitation parameters, which can be explained by differences in the raw particle size and 10 velocity distribution (PSVD). Thies LPM recorded in average double number of particles than PARSIVEL 2 . PSVD percentile comparison showed Thies LPM measuring more small particles than Ott Parsivel 2 , resulting in higher rain rates and totals.These differences increased greatly with rainfall intensity. At rain rates above 10 mm h

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The variability of the rainfall rate as a function of area

Jameson¹,

Larsen

2016

JGR Atmospheres

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Distributions of drop sizes can be expressed as DSD = Nt × PSD, where Nt is the total number of drops in a sample and PSD is the frequency distribution of drop diameters (D). Their discovery permitted remote sensing techniques for rainfall estimation using radars and satellites measuring over large domains of several kilometers. Because these techniques depend heavily on higher moments of the PSD, there has been a bias toward attributing the variability of the intrinsic rainfall rates R over areas (σR) to the variability of the PSDs. While this variability does increase up to a point with increasing domain dimension L, the variability of the rainfall rate R also depends upon the variability in the total number of drops Nt. We show that while the importance of PSDs looms large for small domains used in past studies, it is the variability of Nt that dominates the variability of R as L increases to 1 km and beyond. The PSDs contribute to the variability of R through the relative dispersion of χ = D3Vt, where Vt is the terminal fall speed of drops of diameter D. However, the variability of χ is inherently limited because drop sizes and fall speeds are physically limited. In contrast, it is shown that the variance of Nt continuously increases as the domain expands for physical reasons explained below. Over domains larger than around 1 km, it is shown that Nt dominates the variance of the rainfall rate with increasing L regardless of the PSD.

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On the measurement of atmospheric gamma drop‐size distributions

Cited by 20 publications

References 5 publications

Shape Slope Parameter Distribution Modelling of Electromagnetic Scattering by Rain Drops

Shape Slope Parameter Distribution Modelling of Electromagnetic Scattering by Rain Drops

Comparison of precipitation measurements by Ott Parsivel<sup>2</sup> and Thies LPM optical disdrometers

The variability of the rainfall rate as a function of area

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On the measurement of atmospheric gamma drop‐size distributions

Cited by 20 publications

References 5 publications

Shape Slope Parameter Distribution Modelling of Electromagnetic Scattering by Rain Drops

Shape Slope Parameter Distribution Modelling of Electromagnetic Scattering by Rain Drops

Comparison of precipitation measurements by Ott Parsivel&lt;sup&gt;2&lt;/sup&gt; and Thies LPM optical disdrometers

The variability of the rainfall rate as a function of area

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Comparison of precipitation measurements by Ott Parsivel<sup>2</sup> and Thies LPM optical disdrometers