Scattering of Hydrometeors

Kneifel, Stefan; Leinonen, Jussi; Tyynelä, Jani; Ori, Davide; Battaglia, Alessandro

doi:10.1007/978-3-030-24568-9_15

Cited by 15 publications

(10 citation statements)

References 111 publications

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“…During the past decade, millimeter wavelength (cloud) radars have become essential tools for the observations of clouds and precipitation. Cloud radars provide particular advantages for cloud and precipitation studies due to their narrow beam width, inherent high sensitivity, portability, reduced susceptibility to Bragg scattering, and ground clutter (Kollias et al, 2007). These advantages, however, come with the cost of larger signal attenuation caused by atmospheric gases and hydrometeors, which in general increases with frequency.…”

Section: Hydrometeor Attenuation At Millimeter Wavelengthsmentioning

confidence: 99%

Estimating total attenuation using Rayleigh targets at cloud top: applications in multilayer and mixed-phase clouds observed by ground-based multifrequency radars

2020

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Abstract. At millimeter wavelengths, attenuation by hydrometeors, such as liquid droplets or large snowflakes, is generally not negligible. When using multifrequency ground-based radar measurements, it is common practice to use the Rayleigh targets at cloud top as a reference in order to derive attenuation-corrected reflectivities and meaningful dual-frequency ratios (DFRs). By capitalizing on this idea, this study describes a new quality-controlled approach that aims at identifying regions of cloud where particle growth is negligible. The core of the method is the identification of a “Rayleigh plateau”, i.e., a large enough region near cloud top where the vertical gradient of DFR remains small. By analyzing co-located Ka–W band radar and microwave radiometer (MWR) observations taken at two European sites under various meteorological conditions, it is shown how the resulting estimates of differential path-integrated attenuation (ΔPIA) can be used to characterize hydrometeor properties. When the ΔPIA is predominantly produced by cloud liquid droplets, this technique alone can provide accurate estimates of the liquid water path. When combined with MWR observations, this methodology paves the way towards profiling the cloud liquid water, quality-flagging the MWR retrieval for rain and drizzle contamination, and/or estimating the snow differential attenuation.

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Section: Hydrometeor Attenuation At Millimeter Wavelengthsmentioning

confidence: 99%

Estimating total attenuation using Rayleigh targets at cloud top: applications in multilayer and mixed-phase clouds observed by ground-based multifrequency radars

2020

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“…The SI could be affected by the presence of clouds in different ways. Several authors have demonstrated the sensitivity of the 90 GHz channels to the emission by supercooled cloud water (e.g., Kneifel et al 2010;Panegrossi et al 2017), which tends to increase the TBs. This effect compensates the cooling of the TBs due to the scattering by the falling snow.…”

Section: ) Dependence On Cloud Cover and Atmospheric Humiditymentioning

confidence: 99%

“…The extremely variable snow-cover extent and snow radiative properties in the MW are one of the main issues in the detection and quantification of snowfall by passive microwave observations, which remain among the most challenging tasks in global precipitation retrieval (Bennartz and Bauer 2003;Skofronick-Jackson et al 2004Noh et al 2009;Levizzani et al 2011;Kongoli and Helfrich 2015;Chen et al 2016;You et al 2017;Kulie et al 2016; and many others). On one side, relative to rainfall, the snowfall scattering signal is much weaker (Grody 1991;Kim et al 2008;Kulie et al 2010), is highly dependent on the complex microphysical characteristics of snowfall (Bennartz and Petty 2001;Liu 2008;Kulie et al 2010;Petty and Huang 2010;Skofronick-Jackson and Johnson 2011;Kuo et al 2016;Olson et al 2016;Eriksson et al 2018;Kneifel et al 2020), and tends to be masked by the atmospheric and liquid water emissivity (Kneifel et al 2010;Johnson et al 2016;Liu and Seo 2013;Wang et al 2013;Panegrossi et al 2017). On the other side, the snow-covered surface emissivity is extremely variable due to rapid changes of snow-cover extent, snow accumulation on the ground, and snowpack radiative properties, with significant effects on the snowfall microwave signal (e.g., Laviola et al 2015;Prigent et al 2003;Noh et al 2009;Takbiri et al 2019;M20).…”

Section: Introductionmentioning

confidence: 99%

The Passive microwave Empirical cold Surface Classification Algorithm (PESCA): application to GMI and ATMS

Camplani

Casella

Sanò

et al. 2021

Journal of Hydrometeorology

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This paper describes a new Passive microwave Empirical cold Surface Classification Algorithm (PESCA) developed for snow cover detection and characterization by using passive microwave satellite measurements. The main goal of PESCA is to support the retrieval of falling snow, as several studies have highlighted the influence of snow cover radiative properties on the falling snow passive microwave signature. The developed methodology is based on the exploitation of the lower frequency channels (< 90 GHz), common to most microwave radiometers. The methodology applied to the conically scanning GMI and the cross-track scanning ATMS is described in this paper. PESCA is based on a decision tree developed using an empirical method and verified using the AutoSnow product built from satellite measurements. The algorithm performance appears to be robust for both sensors in dry conditions (TPW < 10 mm), and for mean surface elevation < 2500 m, independently of the cloud cover. The algorithm shows very good performance for cold temperatures (2 m temperature below 270 K) with a rapid decrease of the detection capabilities between 270 K and 280 K, where 280 K is assumed as the maximum temperature limit for PESCA [overall detection statistics: POD=0.98(0.92), FAR=0.01(0.08), HSS=0.72(0.69) for ATMS(GMI)]. Some inconsistencies found between the snow categories identified with the two radiometers are related to their different viewing geometry, spatial resolution, and temporal sampling. The spectral signatures of the different snow classes appear to be different also at high frequency (>90GHz), indicating potential impact for snowfall retrieval. This method can be applied to other conically and cross track scanning radiometers including the future operational EPS-SG mission microwave radiometers.

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“…Unlike comprehensive radar simulators (Kollias et al, 2011;Oue et al, 2020;Mech et al, 2020), the Doppler spectrum simulator does not take into account radar instrument specifications or dynamical effects, such as instrument noise level, broadening of the spectrum due to air turbulence, finite beam width, and wind shear. The five moments (radar reflectivity Z e [dBZ], the mean Doppler velocity [m s −1 ], the spectrum width [m s −1 ], the skewness, and the kurtosis) of the Doppler spectrum are computed directly from the idealized spectrum.…”

Section: Radar Simulatormentioning

confidence: 99%

snowScatt 1.0: consistent model of microphysical and scattering properties of rimed and unrimed snowflakes based on the self-similar Rayleigh–Gans approximation

et al. 2021

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Abstract. More detailed observational capabilities in the microwave (MW) range and advancements in the details of microphysical schemes for ice and snow demand increasing complexity to be included in scattering databases. The majority of existing databases rely on the discrete dipole approximation (DDA) whose high computational costs limit either the variety of particle types or the range of parameters included, such as frequency, temperature, and particle size. The snowScatt tool is innovative in that it provides consistent microphysical and scattering properties of an ensemble of 50 000 snowflake aggregates generated with different physical particle models. Many diverse snowflake types, including rimed particles and aggregates of different monomer composition, are accounted for. The scattering formulation adopted by snowScatt is based on the self-similar Rayleigh–Gans approximation (SSRGA), which is capable of modeling the scattering properties of large ensembles of particles. Previous comparisons of SSRGA and DDA are extended in this study by including unrimed and rimed aggregates up to centimeter sizes and frequencies up to the sub-millimeter spectrum. The results generally reveal the wide applicability of the SSRGA method for active and passive MW applications. Unlike DDA databases, the set of SSRGA parameters can be used to infer scattering properties at any frequency and refractive index; snowScatt also provides tools to derive the SSRGA parameters for new sets of particle structures, which can be easily included in the library. The flexibility of the snowScatt tool with respect to applications that require continuously changing definitions of snow properties is demonstrated in a forward simulation example based on the output of the predicted particle properties (P3) scheme. The snowScatt tool provides the same level of flexibility as commonly used T-matrix solutions, while the computed scattering properties reach the level of accuracy of detailed discrete dipole approximation calculations.

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Scattering of Hydrometeors

Cited by 15 publications

References 111 publications

Estimating total attenuation using Rayleigh targets at cloud top: applications in multilayer and mixed-phase clouds observed by ground-based multifrequency radars

Estimating total attenuation using Rayleigh targets at cloud top: applications in multilayer and mixed-phase clouds observed by ground-based multifrequency radars

The Passive microwave Empirical cold Surface Classification Algorithm (PESCA): application to GMI and ATMS

snowScatt 1.0: consistent model of microphysical and scattering properties of rimed and unrimed snowflakes based on the self-similar Rayleigh–Gans approximation

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