Estimation of Liquid Water Path in Stratiform Precipitation System using MWR and Radar Measurements

Cloud liquid water path (LWP) is a critical parameter for studying cloud and precipitation, as well as their transition process. In most operational numerical weather prediction models, production of rain by collision and coalescence is parameterized by an auto-conversion rate that depends upon the values of cloud liquid water content/path in the model. Cloud LWPs can be retrieved from microwave radiometer measured brightness temperatures during cloudy and light rain conditions. Meanwhile, it is well known that MWR-retrieved LWPs are questionable under moderate and heavy rain conditions due to wet radome and non-Rayleigh effects. However, no study has been done to provide a threshold of rain rate to distinguish when MWR-retrieved LWPs are reliable and non-reliable. In this study, we use 10 years of DOE ARM collocated MWR-retrieved LWPs and surface disdrometer measured rain rate to provide a statistical result to identify the reliable or non-reliable LWP retrievals from MWR.

Furthermore, when MWR cannot provide a reliable LWP, we use a radar-based technique to provide an estimation of the LWP in stratiform rain region of convective system. During the precipitation process, the LWP consists of both rain liquid water path (RLWP) and cloud liquid water path (CLWP). The total LWP is retrieved using 2.8 GHz (S-band) and 35-GHz (Ka-band) radar observations collected during the Mid-latitude Continental Convective Cloud Experiment (MC3E) held in Northern Oklahoma during April-June 2011. The vertical profiles of rain water content (RWC) below the cloud base are first retrieved using these two side-by-side vertical pointing radars. If the cloud base height (determined by ceilometer) is identical to the height of melting layer (determined by Ka-band radar measurements), LWP is estimated using the integration of RWC in the liquid layer. If the cloud base is lower than the height of melting band, CLWP will be retrieved based on the attenuation of Ka-band radar reflectivity. The partitioned LWP will play an important role in cloud modeling and in space-based retrieval algorithms where empirical assumptions are employed to resolve the unknown cloud-rain partition.