Motivating a Synergistic Mixing-Layer Height Retrieval Method Using Backscatter Lidar Returns and Microwave-Radiometer Temperature Observations

Mixing-layer-height (MLH) retrieval methods using backscattered lidar signals from a ceilometer (Jenoptik CHM -15k Nimbus) and temperature profiles from a microwave radiometer (MWR) and Humidity And Temperature PROfiler (HATPRO) radiometer physics GmbH (RPG) are compared in terms of their complementary capabilities and associated uncertainties. The extended Kalman filter (EKF) is used for MLH retrieval from backscattered lidar signals, and the parcel method is used for MLH retrieval from MWR-derived potential-temperature profiles. The two principal sources of uncertainty in ceilometer-based MLH estimates are: 1) incorrect layer attribution (
$\sim $
hundreds of meters) and 2) noise-induced errors (about 50 m at
$3\sigma $
). MWR MLH uncertainties comprise: 1) the total uncertainty in the retrieved potential temperature profile and 2) ±0.5 K uncertainty in the surface temperature. Ceilometer- and MWR-based MLH estimates are, in turn, compared with reference to MLH estimates from radiosoundings. Twenty-one measurement days from the high definition clouds and precipitation for advancing climate prediction (HD(CP)
²
) Observational Prototype Experiment (HOPE) campaign at Jülich, Germany, are considered. It is shown that the MWR can track the full mixed layer (ML) diurnal cycle (i.e., including morning and evening transitions) with height-increasing error bars. The ceilometer-EKF MLH estimates are much smaller errorbars than those from the MWR under the well-developed clear-sky ML, but the ceilometer-EKF is prone to ambiguous tracking some multilayer scenarios (e.g., the residual layer). We, therefore, introduce the synergistic MLH retrieval approach that combines both ceilometer and MWR estimates in order to optimize the benefits of both.