A Methodology to Derive Radar Reflectivity–Liquid Equivalent Snow Rate Relations Using C-Band Radar and a 2D Video Disdrometer

Huang, Gwo-Jong; Bringi, V. N.; Cifelli, Robert; Hudak, David; Petersen, Walt

doi:10.1175/2009jtecha1284.1

Cited by 88 publications

(106 citation statements)

References 18 publications

Supporting

Mentioning

104

Contrasting

Order By: Relevance

“…Brandes et al (2007) used 2-D video disdrometer and snow accumulation observations to estimate snow bulk densities. Huang et al (2010) also used 2-D video disdrometer observations, along with C-band radar reflectivities, to estimate the parameters of snow particle massdimension relations. In analyses such as these, the observed PSDs are used to model radar reflectivity or snowfall accumulation, and the modeled values are then fitted to observed reflectivities or accumulations by adjusting snow microphysical properties.…”

Section: N B Wood Et Al: Characterization Of Disdrometer Uncertainmentioning

confidence: 99%

“…The criteria are based on the vertical extent of the particles, the ratio of the widths observed by the two cameras, the ratio of maximum width to height, and an allowed range of fallspeeds. Huang et al (2010) used similar criteria but applied weights to each criterion, and the best match was chosen based on the image whose weighted sum is a maximum. The 2DVD observations used in this work are the results of the analysis of Huang et al (2010).…”

Section: Snow Particle Observationsmentioning

confidence: 99%

“…Huang et al (2010) used similar criteria but applied weights to each criterion, and the best match was chosen based on the image whose weighted sum is a maximum. The 2DVD observations used in this work are the results of the analysis of Huang et al (2010). The observations report the characteristics of individual particles for which matching succeeded.…”

Section: Snow Particle Observationsmentioning

confidence: 99%

“…Note, however, that if matching does not succeed, the unmatched particle cannot be used in the size distribution calculation since fallspeed is not known and the particle's contribution to the size distribution via Eq. (3) cannot be determined (Hanesch, 1999;Huang et al, 2010). The resulting data loss can lead to errors in the estimated size distribution (Huang et al, 2010).…”

Section: Snow Particle Observationsmentioning

confidence: 99%

“…(3) cannot be determined (Hanesch, 1999;Huang et al, 2010). The resulting data loss can lead to errors in the estimated size distribution (Huang et al, 2010). Because the SVI is not dependent on particle matching, the SVI observations are taken as the primary measure of the snow PSDs for this work.…”

Section: Snow Particle Observationsmentioning

confidence: 99%

See 4 more Smart Citations

Characterization of video disdrometer uncertainties and impacts on estimates of snowfall rate and radar reflectivity

et al. 2013

View full text Add to dashboard Cite

Abstract. Estimates of snow microphysical properties obtained by analyzing collections of individual particles are often limited to short timescales and coarse time resolution. Retrievals using disdrometer observations coincident with bulk measurements such as radar reflectivity and snowfall amounts may overcome these limitations; however, retrieval techniques using such observations require uncertainty estimates not only for the bulk measurements themselves, but also for the simulated measurements modeled from the disdrometer observations. Disdrometer uncertainties arise due to sampling and analytic errors and to the discrete, potentially truncated form of the reported size distributions. Imaging disdrometers such as the Snowflake Video Imager and 2-D Video Disdrometer provide remarkably detailed representations of snow particles, but view limited projections of their three-dimensional shapes. Particle sizes determined by such instruments underestimate the true dimensions of the particles in a way that depends, in the mean, on particle shape, also contributing to uncertainties. An uncertainty model that accounts for these uncertainties is developed and used to establish their contributions to simulated radar reflectivity and snowfall rate. Viewing geometry effects are characterized by a parameter, φ, that relates disdrometer-observed particle size to the true maximum dimension of the particle. Values and uncertainties for φ are estimated using idealized ellipsoidal snow particles. The model is applied to observations from seven snow events from the Canadian CloudSat/CALIPSO Validation Project (C3VP), a mid-latitude cold-season cloud and precipitation field experiment. Typical total uncertainties are 4 dB for reflectivity and 40-60 % for snowfall rate, are highly correlated, and are substantial compared to expected uncertainties for radar and precipitation gauge observations. The dominant sources of errors are viewing geometry effects and the discrete, truncated form of the size distributions. While modeled Ze-S relationships are strongly affected by assumptions about snow particle mass properties, such relationships are only modestly sensitive to φ owing to partially compensating effects on both the reflectivity and snowfall rate.

show abstract

Section: N B Wood Et Al: Characterization Of Disdrometer Uncertainmentioning

confidence: 99%

Section: Snow Particle Observationsmentioning

confidence: 99%

Section: Snow Particle Observationsmentioning

confidence: 99%

Section: Snow Particle Observationsmentioning

confidence: 99%

Section: Snow Particle Observationsmentioning

confidence: 99%

See 3 more Smart Citations

Characterization of video disdrometer uncertainties and impacts on estimates of snowfall rate and radar reflectivity

et al. 2013

View full text Add to dashboard Cite

show abstract

Estimating snow microphysical properties using collocated multisensor observations

et al. 2014

View full text Add to dashboard Cite

The ability of ground-based in situ and remote sensing observations to constrain microphysical properties for dry snow is examined using a Bayesian optimal estimation retrieval method. Power functions describing the variation of mass and horizontally projected area with particle size and a parameter related to particle shape are retrieved from near-Rayleigh radar reflectivity, particle size distribution, snowfall rate, and size-resolved particle fall speeds. Algorithm performance is explored in the context of instruments deployed during the Canadian CloudSat CALIPSO Validation Project, but the algorithm is adaptable to other similar combinations of sensors. Critical estimates of observational and forward model uncertainties are developed and used to quantify the performance of the method using synthetic cases developed from actual observations of snow events. In addition to illustrating the technique, the results demonstrate that this combination of sensors provides useful constraints on the mass parameters and on the coefficient of the area power function but only weakly constrains the exponent of the area power function and the shape parameter. Information content metrics show that about two independent quantities are measured by the suite of observations and that the method is able to resolve about eight distinct realizations of the state vector containing the mass and area power function parameters. Alternate assumptions about observational and forward model uncertainties reveal that improved modeling of particle fall speeds could contribute substantial improvements to the performance of the method.

show abstract

Radar signatures of snowflake riming: A modeling study

Leinonen

Szyrmer

2015

Earth and Space Science

109

226

View full text Add to dashboard Cite

The capability to detect the state of snowflake riming reliably from remote measurements would greatly expand the understanding of its global role in cloud‐precipitation processes. To investigate the ability of multifrequency radars to detect riming, a three‐dimensional model of snowflake growth was used to generate simulated aggregate and crystal snowflakes with various degrees of riming. Three different growth scenarios, representing different temporal relationships between aggregation and riming, were formulated. The discrete dipole approximation was then used to compute the radar backscattering properties of the snowflakes at frequencies of 9.7, 13.6, 35.6, and 94 GHz. In two of the three growth scenarios, the rimed snowflakes exhibit large differences between the backscattering cross sections of the detailed three‐dimensional models and the equivalent homogeneous spheroidal models, similarly to earlier results for unrimed snowflakes. When three frequencies are used simultaneously, riming appears to be detectable in a robust manner across all three scenarios. In spite of the differences in backscattering cross sections, the triple‐frequency signatures of heavily rimed particles resemble those of the homogeneous spheroids, thus explaining earlier observational results that were compatible with such spheroids.

show abstract

A Methodology to Derive Radar Reflectivity–Liquid Equivalent Snow Rate Relations Using C-Band Radar and a 2D Video Disdrometer

Cited by 88 publications

References 18 publications

Characterization of video disdrometer uncertainties and impacts on estimates of snowfall rate and radar reflectivity

Characterization of video disdrometer uncertainties and impacts on estimates of snowfall rate and radar reflectivity

Estimating snow microphysical properties using collocated multisensor observations

Radar signatures of snowflake riming: A modeling study

Contact Info

Product

Resources

About