Threshold radar reflectivity for drizzling clouds

Liu, Yangang; Geerts, Bart; Miller, Mark A.; Daum, P. H.; McGraw, Robert

doi:10.1029/2007gl031201

Cited by 51 publications

(51 citation statements)

References 26 publications

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“…Furthermore, Wang and Geerts (2003) demonstrate that the value of this threshold varies with altitude within the cloud layer and it can increase from around −25 dBZ near the cloud base to about −12 dBZ close to the cloud top. A theoretical approach by Liu et al (2008) reveals a dependence of the threshold value on the droplet number concentration -a finding that compares favorably with observations. In remote sensing applications, where droplet concentration is one of the unknown variables to be retrieved, setting a single Z threshold value in advance may lead to an unaccounted bias in the retrieval.…”

Section: Introductionsupporting

confidence: 63%

Simultaneous and synergistic profiling of cloud and drizzle properties using ground-based observations

2017

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Abstract. Despite the importance of radar reflectivity (Z) measurements in the retrieval of liquid water cloud properties, it remains nontrivial to interpret Z due to the possible presence of drizzle droplets within the clouds. So far, there has been no published work that utilizes Z to identify the presence of drizzle above the cloud base in an optimized and a physically consistent manner. In this work, we develop a retrieval technique that exploits the synergy of different remote sensing systems to carry out this task and to subsequently profile the microphysical properties of the cloud and drizzle in a unified framework. This is accomplished by using ground-based measurements of Z, lidar attenuated backscatter below as well as above the cloud base, and microwave brightness temperatures. Fast physical forward models coupled to cloud and drizzle structure parameterization are used in an optimal-estimation-type framework in order to retrieve the best estimate for the cloud and drizzle property profiles. The cloud retrieval is first evaluated using synthetic signals generated from large-eddy simulation (LES) output to verify the forward models used in the retrieval procedure and the vertical parameterization of the liquid water content (LWC). From this exercise it is found that, on average, the cloud properties can be retrieved within 5 % of the mean truth. The full cloud-drizzle retrieval method is then applied to a selected ACCEPT (Analysis of the Composition of Clouds with Extended Polarization Techniques) campaign dataset collected in Cabauw, the Netherlands. An assessment of the retrieval products is performed using three independent methods from the literature; each was specifically developed to retrieve only the cloud properties, the drizzle properties below the cloud base, or the drizzle fraction within the cloud. One-to-one comparisons, taking into account the uncertainties or limitations of each retrieval, show that our results are consistent with what is derived using the three independent methods.

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Section: Introductionsupporting

confidence: 63%

Simultaneous and synergistic profiling of cloud and drizzle properties using ground-based observations

2017

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“…As an additional precaution, a maximum threshold for radar reflectivity is used. Earlier studies have employed thresholds, such as −17 dBZ (Frisch et al, 1995;Feingold et al, 1999;Ghate et al, 2010), to delineate drizzle from drizzle-free clouds, but, since our interest is specifically on the vertical velocities at cloud base, where cloud droplets are at their smallest and drizzle drops presumably at their largest, a maximum value of −30 dBZ is used in this study (see Liu et al, 2008;Kollias and Albrecht, 2010). These constraints limit our investigation to a rather small fraction of the total number of observed liquid cloud layers.…”

Section: Observationsmentioning

confidence: 99%

Cloud base vertical velocity statistics: a comparison between an atmospheric mesoscale model and remote sensing observations

et al. 2011

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Abstract. The statistics of cloud base vertical velocity simulated by the non-hydrostatic mesoscale model AROME are compared with Cloudnet remote sensing observations at two locations: the ARM SGP site in central Oklahoma, and the DWD observatory at Lindenberg, Germany. The results show that AROME significantly underestimates the variability of vertical velocity at cloud base compared to observations at their nominal resolution; the standard deviation of vertical velocity in the model is typically 4-8 times smaller than observed, and even more during the winter at Lindenberg. Averaging the observations to the horizontal scale corresponding to the physical grid spacing of AROME (2.5 km) explains 70-80 % of the underestimation by the model. Further averaging of the observations in the horizontal is required to match the model values for the standard deviation in vertical velocity. This indicates an effective horizontal resolution for the AROME model of at least 10 km in the presented case. Adding a TKE-term on the resolved grid-point vertical velocity can compensate for the underestimation, but only for altitudes below approximately the boundary layer top height. The results illustrate the need for a careful consideration of the scales the model is able to accurately resolve, as well as for a special treatment of sub-grid scale variability of vertical velocities in kilometer-scale atmospheric models, if processes such as aerosol-cloud interactions are to be included in the future.

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“…Frisch et al (1998) first introduced the combination of radar-radiometer measurements to retrieve the in-cloud profile of liquid water content. Drizzle occurrence limits the applicability of the technique, and either the use of a radar reflectivity threshold (e.g., Liu et al, 2008) or the absence of radar echoes below the cloud base is used to remove drizzling clouds. In the absence of radiometer measurements, a variety of regressionbased power law relations between the radar reflectivity factor and the liquid water content have been proposed (Atlas, 1954;Sauvageot and Omar, 1987;Sassen and Liao, 1996;Fox and Illingworth, 1997;Wang and Geerts, 2003;Kogan et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

Radar-radiometer retrievals of cloud number concentration and dispersion parameter in nondrizzling marine stratocumulus

2013

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Abstract. The retrieval of cloud microphysical properties from remote sensors is challenging. In the past, ground-based radar-radiometer measurements have been successfully used to retrieve the liquid water content profile in nondrizzling clouds but offer little constraint in retrieving other moments of the cloud particle size distribution (PSD). Here, a microphysical condensational model under steady-state supersaturation conditions is utilized to provide additional constraints to the well-established radar-radiometer retrieval techniques. The coupling of the model with the observations allows the retrieval of the three parameters of a lognormal PSD, with two of them being height dependent. Two periods of stratocumulus from the Azores are used to evaluate the novel technique. The results appear reasonable in two nondrizzling periods: continental-like number concentrations are retrieved, in agreement with the drizzle-free cloud conditions. The cloud optical depth derived from the retrieved distributions compares well in magnitude and variability with the one derived independently from a narrow field of view zenith radiometer. Uncertainties coming from the measurements are propagated to the retrieved quantities to estimate their errors. In general, errors smaller than 20% should be attainable for most parameters, demonstrating the added value of the new technique.

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Threshold radar reflectivity for drizzling clouds

Cited by 51 publications

References 26 publications

Simultaneous and synergistic profiling of cloud and drizzle properties using ground-based observations

Simultaneous and synergistic profiling of cloud and drizzle properties using ground-based observations

Cloud base vertical velocity statistics: a comparison between an atmospheric mesoscale model and remote sensing observations

Radar-radiometer retrievals of cloud number concentration and dispersion parameter in nondrizzling marine stratocumulus

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