A Novel Liquid Water Content Retrieval Method Based on Mass Absorption for Single-Wavelength Cloud Radar

Low-level clouds (LLCs), mainly composed of liquid water droplets, cool the climate system by strongly reflecting solar radiation back to space and thus play an important role in the Earth energy budget. However, the LLC properties and their radiative effects are poorly represented in climate models, leading to the largest source of uncertainty in climate prediction. Liquid water content (LWC) is a key property of LLC determining cloud extinction characteristics and is a fundamental parameter in the radiative transfer model. To improve the understanding of LWC properties, algorithms have been proposed to retrieve LWC based on millimeter-wavelength radar. However, the traditional retrieval relies on preconstructed empirical relationship between reflectivity and LWC and has noticeable limitations. In particular, the retrieval uncertainty is strongly dependent on the assumed particle size distribution, the existence of drizzle particle, and on the accuracy of reflectivity measurement. In this study, we develop a new self-consistent algorithm to retrieve LWC by constraining radar reflectivity factor and attenuation in the whole liquid cloud layer. A relationship between the radar measured reflectivity, LWC, and intrinsic reflectivity is first constructed based on the radiative transfer theory under the Rayleigh scattering regime. A nonlinear least-square regression technique is then applied to derive the optimal parameters in the retrieval equations to obtain the LWC. Comparison with the microwave radiometer (MWR) derived liquid water path (LWP) indicates that our proposed method retrieves more accurate LWC products than that from the traditional empirical algorithms.

Automated summary

Low-level clouds composed of liquid water droplets cool the climate system by reflecting solar radiation back to space, but their properties and radiative effects are poorly represented in climate models, leading to uncertainty in climate prediction. To improve understanding, an algorithm has been developed to retrieve liquid water content (LWC) based on millimeter-wavelength radar. However, the traditional retrieval method has limitations, such as uncertainty dependent on particle size distribution, drizzle particle presence, and reflectivity measurement accuracy. This study proposes a new self-consistent algorithm that constrains radar reflectivity factor and attenuation to retrieve accurate LWC products.