Abstract
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.
Highlights
Low-level liquid water clouds are known to have a large areal extent (Hartmann et al, 1992) and a strong impact on the Earth’s energy balance (Ramanathan et al, 1989; Slingo, 1990)
Ρw is the density of water and LWCad the adiabatic LWC. ρa and Aad are the density of air and the adiabatic lapse rate of the liquid water content mixing ratio, respectively; both are a function of the temperature and pressure at the cloud base
We developed a method to simultaneously profile cloud and drizzle properties by exploiting the synergy of ground-based radar, lidar, and microwave radiometer measurements
Summary
Low-level liquid water clouds are known to have a large areal extent (Hartmann et al, 1992) and a strong impact on the Earth’s energy balance (Ramanathan et al, 1989; Slingo, 1990). Fielding et al (2015) set a precedent by jointly retrieving cloud and drizzle properties using ground-based radar, lidar, and Sun-photometer observations Their retrieval departs from the assumption that drizzle is present only when the maximum observed reflectivity in a given column exceeds a single threshold value. In this work we develop a retrieval technique that combines ground-based radar, lidar, and microwave radiometer measurements to simultaneously profile the cloud and drizzle properties without placing a priori constraints on the presence of drizzle droplets within the cloud.
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