Profiles of Low-Level Stratus Cloud Microphysics Deduced from Ground-Based Measurements

Abstract

The microwave radiometer?derived cloud liquid water path (LWP) and a profile of radar reflectivity are used to derive a profile of cloud liquid water content (LWC). Two methods (M1 and M2) have been developed for inferring the profile of cloud-droplet effective radius (re) in liquid phase or liquid dominant mixed phase stratocumulus clouds. The M1-inferred re profile is proportional to a previously derived layer-mean re and to the ratio of the radar reflectivity to the integrated radar reflectivity. This algorithm is independent of the radar calibration and is applicable to overcast low-level stratus clouds that occur during the day because it is dependent on solar transmission observations. In order to extend the retrieval algorithm to a wider range of conditions, a second method is described that uses an empirical relationship between effective radius and radar reflectivity based on theory and the results of M1. Sensitivity studies show that the surface-retrieved re is more sensitive to the variation of radar reflectivity when the radar reflectivity is large, and the uncertainties of retrieved re related to the assumed vertically constant cloud-droplet number concentration and shape of the size distribution are about 9% and 2%, respectively. For validation, a total of 10 h of aircraft data and 36 h of surface data were collected over the Atmospheric Radiation Measurement (ARM) program's Southern Great Plains (SGP) site during the March 2000 cloud intensive observational period (IOP). More detailed comparisons in two cases quantify the agreement between the aircraft data and the surface retrievals. When the temporal averages of the two datasets increase from 1 min to 30 min, the means and standard deviations of differences between the two datasets decrease from ?2.5% ± 84% to 1.3% ± 42.6% and their corresponding correlation coefficients increase from 0.47 to 0.8 for LWC; and decrease from ?4.8% ± 36.4% to ?3.3% ± 22.5% with increased coefficients from 0.64 to 0.94 for re (both M1 and M2). The agreement between the aircraft and surface data in the 30-min averages suggests that the two platforms are capable of characterizing the cloud microphysics over this temporal scale. On average, the surface retrievals are unbiased relative to the aircraft in situ measurements. However, when only the 1-min averaged aircraft data within 3 km of the surface site were selected, the means and standard deviations of differences between the two datasets are larger (23.4% ± 113% for LWC and 28.3% ± 60.7% for re) and their correlation coefficients are smaller (0.32 for LWC and 0.3 for re) than those from all 1-min samples. This result suggests that restricting the comparison to the samples better matched in space and time between the surface and aircraft data does not result in a better comparison.