The Retrieval of Stratus Cloud Droplet Effective Radius with Cloud Radars

Abstract

In situ samples of cloud droplets by aircraft in Oklahoma in 1997, the Surface Heat Budget of the Arctic Ocean (SHEBA)/First ISCCP Regional Experiment (FIRE)-Arctic Cloud Experiment (ACE) in 1998, and various other locations around the world were used to evaluate a ground-based remote sensing technique for retrieving profiles of cloud droplet effective radius. The technique is based on vertically pointing measurements from highsensitivity millimeter-wavelength radar and produces height-resolved estimates of cloud particle effective radius. Although most meteorological radars lack the sensitivity to detect small cloud droplets, millimeter-wavelength cloud radars provide opportunities for remotely monitoring the properties of nonprecipitating clouds. These highsensitivity radars reveal detailed reflectivity structure of most clouds that are within several kilometers range. In order to turn reflectivity into usable microphysical quantities, relationships between the measured quantities and the desired quantities must be developed. This can be done through theoretical analysis, modeling, or empirical measurements. Then the uncertainty of each procedure must be determined in order to know which ones to use. In this study, two related techniques are examined for the retrieval of the effective radius. One method uses both radar reflectivity and integrated liquid water through the clouds obtained from a microwave radiometer; the second uses the radar reflectivity and an assumption that continental stratus clouds have a concentration of 200 drops per cubic centimeter and marine stratus 100 cm Ϫ3. Using in situ measurements of marine and continental stratus, the error analysis herein shows that the error in these techniques would be about 15%. In comparing the techniques with in situ aircraft measurements of effective radius, it is found that the radar radiometer retrieval was not quite as good as the technique using radar reflectivity alone. The radar reflectivity alone gave a 13% standard deviation with the in situ comparison, while the radar-radiometer retrieval gave a 19% standard deviation.

The HD(CP)<sup>2</sup> Observational Prototype Experiment (HOPE) – an overview

“…As a first step towards an evaluation dataset for numerical weather forecasts, it was decided to apply the retrieval technique of Frisch et al (2002) to the LACROS observations by implementing it into the processing framework of Cloudnet. The technique is based on vertically pointing measurements from a millimetre-wavelength cloud radar and a microwave radiometer and produces height-resolved estimates of cloud particle effective radius and liquid water content.…”

“…The implemented Frisch-2002(Frisch et al, 2002 retrieval of cloud droplet effective radius and the Cloudnet retrieval of the adiabatically scaled LWC were evaluated against in situ observations of ACTOS for a stratocumulus deck observed simultaneously by ACTOS and LACROS during the HOPE-Melpitz campaign on 22 September 2013 (IOP 22) from 09:59 to 10:16 UTC, as is shown in Fig. 12.…”

“…Considering the applied retrieval of Eq. (5) in Frisch et al (2002), the assumption of a certain shape of the size distribution and of a cloud droplet number concentration can introduce biases. The application of the co-located observations of ACTOS and LACROS for the evaluation of ground-based retrievals will be discussed in an upcoming publication .…”

G band atmospheric radars: new frontiers in cloud physics

“…This is mainly because the liquid water content (LWC, proportional to the third moment of the droplet spectrum) of BL clouds is dominated by small (diameter < 40 µm) cloud droplets, whereas the radar backscatter signal (Z, proportional to the sixth moment of the droplet spectrum) is dominated by drizzle when present (Fox and Illingworth, 1997). Nevertheless, several retrieval techniques have been developed that use millimetre radar-only measurements or combine radar with microwave radiometer measurements (Atlas, 1954;Frisch et al, 1998Frisch et al, , 2002Williams and Vivekanandan, 2007;Brandau et al, 2010;Ellis and Vivekanandan, 2011) to retrieve LWC and/or cloud effective radius. Most of these retrievals assume monomodal size distributions, which are usually described through a log-normal or a modified gamma distribution.…”

STORMVEX: The Storm Peak Lab Cloud Property Validation Experiment Science and Operations Plan

“…Unfortunately, the bulk cloud and precipitation properties that are needed to meet the core ARM Facility objectives are difficult to derive accurately from groundbased remote sensing instrumentation. Because of the importance of these data, the retrieval algorithms used to derive the cloud and precipitation properties continue to be a subject of intense research within ARM (e.g., Frisch et al 2002, Shupe 2007, Matrosov et al 2006, Shupe et al 2005, Matrosov et al 2003, Mace et al 2006b, Dong and Mace 2003, Turner 2005. While experiments have been undertaken to provide direct in situ aircraft-based measurements, these measurements are expensive and, at present, the database of independent measurements needed to both validate existing algorithms and to derive new ones is insufficient.…”