Observed relations between snowfall microphysics and triple‐frequency radar measurements

Kneifel, Stefan; Lerber, Annakaisa von; Tiira, Jussi; Moisseev, Dmitri; Kollias, Pavlos; Leinonen, Jussi

doi:10.1002/2015jd023156

Cited by 154 publications

(297 citation statements)

References 52 publications

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“…For a detailed description of the measurement site setup as well as the in situ and remote sensing instrumentation and data processing please refer to Kneifel et al (2015). In addition to the mentioned instrumentation, radiosondes were launched four times daily for profiling of the atmospheric state variables.…”

Section: Instrumentationmentioning

confidence: 99%

Fingerprints of a riming event on cloud radar Doppler spectra: observations and modeling

Kalesse¹,

Szyrmer²,

Kneifel³

et al. 2016

Atmos. Chem. Phys.

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Abstract. Radar Doppler spectra measurements are exploited to study a riming event when precipitating ice from a seeder cloud sediment through a supercooled liquid water (SLW) layer. The focus is on the "golden sample" case study for this type of analysis based on observations collected during the deployment of the Atmospheric Radiation Measurement Program's (ARM) mobile facility AMF2 at Hyytiälä, Finland, during the Biogenic Aerosols -Effects on Clouds and Climate (BAECC) field campaign. The presented analysis of the height evolution of the radar Doppler spectra is a state-of-the-art retrieval with profiling cloud radars in SLW layers beyond the traditional use of spectral moments. Dynamical effects are considered by following the particle population evolution along slanted tracks that are caused by horizontal advection of the cloud under wind shear conditions. In the SLW layer, the identified liquid peak is used as an air motion tracer to correct the Doppler spectra for vertical air motion and the ice peak is used to study the radar profiles of rimed particles. A 1-D steady-state bin microphysical model is constrained using the SLW and air motion profiles and cloud top radar observations. The observed radar moment profiles of the rimed snow can be simulated reasonably well by the model, but not without making several assumptions about the ice particle concentration and the relative role of deposition and aggregation. This suggests that in situ observations of key ice properties are needed to complement the profiling radar observations before process-oriented studies can effectively evaluate ice microphysical parameterizations.

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Section: Instrumentationmentioning

confidence: 99%

Fingerprints of a riming event on cloud radar Doppler spectra: observations and modeling

Kalesse¹,

Szyrmer²,

Kneifel³

et al. 2016

Atmos. Chem. Phys.

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“…Tong and Xue (2008), Dolan and Rutledge (2009), Matrosov et al (2009), Huang et al (2010 and Zhang et al (2011) used mean snow density-median volume diameter relations for characterizing winter precipitation microphysics by radar. Kneifel et al (2015) showed a connection between mean snow density and multi-frequency radar observations. Thompson et al (2008) used the density relation by B07, and Iguchi et al (2012) applied a similar density retrieval method to improve parametrization of snow microphysics in NWP models, for example.…”

Section: Introductionmentioning

confidence: 99%

Ensemble mean density and its connection to other microphysical properties of falling snow as observed in Southern Finland

et al. 2016

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Abstract. In this study measurements collected during winters 2013/2014 and 2014/2015 at the University of Helsinki measurement station in Hyytiälä are used to investigate connections between ensemble mean snow density, particle fall velocity and parameters of the particle size distribution (PSD). The density of snow is derived from measurements of particle fall velocity and PSD, provided by a particle video imager, and weighing gauge measurements of precipitation rate. Validity of the retrieved density values is checked against snow depth measurements. A relation retrieved for the ensemble mean snow density and median volume diameter is in general agreement with previous studies, but it is observed to vary significantly from one winter to the other. From these observations, characteristic massdimensional relations of snow are retrieved. For snow rates more than 0.2 mm h −1 , a correlation between the intercept parameter of normalized gamma PSD and median volume diameter was observed.

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“…According to this method, the top of the melting layer is at a peak in the slope (-dDFR/dZ), a DFR peak occurs within the melting layer due to Mie scattering by large aggregates, and as these collapse into raindrops a local minimum in the DFR signifies the bottom of the melting layer before differential attenuation begins to increase in the rain layer. Within the ice phase, multi-frequency measurements are useful not only for determining mean particle size, but with three frequencies some indication of particle shape can also be discerned (Leinonen et al, 2012;Kulie et al, 2014;Kneifel et al, 2015). The basis for this can be seen when the DFR curves in Figure 9 are plotted against each other in Ku-Ka vs. Ka-W space (Figure 11).…”

Section: Different Values For μ (Ice) or σM' (Rain) Are Indicated By mentioning

confidence: 99%

Remote Sensing of Precipitation from Airborne and Spaceborne Radar

Munchak

2018

Remote Sensing of Aerosols, Clouds, and Precipitation

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Weather radar measurements from airborne or satellite platforms can be an effective remote sensing tool for examining the three-dimensional structures of clouds and precipitation. This chapter describes some fundamental properties of radar measurements and their dependence on the particle size distribution (PSD) and radar frequency. The inverse problem of solving for the vertical profile of PSD from a profile of measured reflectivity is stated as an optimal estimation problem for single-and multi-frequency measurements. Phenomena that can change the measured reflectivity Zm from its intrinsic value Ze, namely attenuation, non-uniform beam filling, and multiple scattering, are described and mitigation of these effects in the context of the optimal estimation framework is discussed. Finally, some techniques involving the use of passive microwave measurements to further constrain the retrieval of the PSD are presented. [[H1]] IntroductionThe ability of weather radar to measure the location and intensity of precipitation was rapidly realized in the late 1940s following World War II. However, ground-based radars are limited in their ability to directly detect precipitation close to the ground far from the radar site due to ground clutter, refraction of the radar beam, and the curvature of the earth. Beam blockage by terrain also poses problems for radar coverage in mountainous areas. Coverage over oceans and other remote areas, where maintaining a ground radar would be difficult and costly, is also impractical, yet the precipitation that falls in these regions has important impacts on the global https://ntrs.nasa.gov/search.jsp?R=20180000191 2018-05-12T19:37:19+00:00Z atmospheric circulations via latent heating (e.g., Hoskins and Karoly, 1981, Hartmann et al., 1984, Matthews et al., 2004 and can have a profound influence on weather patterns thousands of kilometers away. Likewise, knowledge of precipitation over land, particularly in the form of snow, is a crucial component of the mass balance equation for glaciers and ice sheets, which must be properly characterized for realistic climate simulations (Shepherd et al., 2012).Airborne radar systems can provide high sensitivity and finely resolved vertical profiles to characterize precipitation microphysics for the benefit of model parameterizations and process understanding (e.g., Reinhardt et al, 2010, Heymsfield et al., 2013, Rauber et al., 2016.However, in order to achieve true global coverage, it had been proposed from nearly the beginning of the space age to put a weather radar in space (Kreigler and Kawitz, 1960), and efforts to do so began in earnest in the late 1970s and 1980s with the planning of the Tropical Rainfall Measuring Mission satellite (TRMM; Simpson et al., 1987, Okamoto et al., 1988 increased accuracy, sensitivity, and extension to higher latitudes. Both the TRMM PR and GPM DPR were intended not only to estimate precipitation directly from the radar data but also to construct a database of precipitation profiles to unify precipitation retri...

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Observed relations between snowfall microphysics and triple‐frequency radar measurements

Cited by 154 publications

References 52 publications

Fingerprints of a riming event on cloud radar Doppler spectra: observations and modeling

Fingerprints of a riming event on cloud radar Doppler spectra: observations and modeling

Ensemble mean density and its connection to other microphysical properties of falling snow as observed in Southern Finland

Remote Sensing of Precipitation from Airborne and Spaceborne Radar

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