This paper tackles synergistic mixed-layer height (MLH) estimation via a combination of microwave radiometer (MWR) and lidar ceilometer (LC)-based estimates. While MLH-MWR estimates rely on potential temperature retrievals, MLH-LC estimates rely on aerosol gradients. The pros and cons of MLH retrievals obtained from MWR via the parcel method and from LC via an extended Kalman filter (EKF)-based method are used to motivate the synergistic algorithm. The synergistic algorithm is introduced as a maximum-likelihood combination of MLH-MWR and MLH-LC. Two case examples from the 2013 HOPE campaign at Jülich, Germany, are used to show the robustness of the synergistic method and the effect of surface temperature measurement error. Doppler wind lidar retrievals and radiosonde reference MLH estimates are used for validation.

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.

A synergetic approach for the estimation of stable boundary layer height (SBLH) using lidar and microwave radiometer (MWR) data is presented. Vertical variance of the backscatter signal from a ceilometer is used as an indicator of the aerosol stratification in the nocturnal stable boundary layer. This hypothesis is supported by a statistical analysis over one month of observations. Thermodynamic information from the MWR-derived potential temperature is incorporated as coarse estimate of the SBLH. Data from the two instruments are adaptively assimilated by using an extended Kalman filter (EKF). A first test of the algorithm is performed by applying it to collocated Vaisala CT25K ceilometer and humidity and temperature profiler MWR data collected during the HD(CP)2 Observational Prototype Experiment (HOPE) campaign at Jülich, Germany. The application of the algorithm to different atmospheric scenarios reveals the superior performance of the EKF compared to a nonlinear least squares estimator, particularly in nonidealized conditions.

A synergetic approach for estimation of the nocturnal stable boundary-layer (SBL) height is presented. Aerosol backscatter from a lidar ceilometer and potential temperature from a microwave radiometer (MWR) are combined by using an extended Kalman filter (EKF). While height-resolved spatial-variance from the ceilometer backscatter signal provides a signature for the stratification of aerosols in the SBL, temperature data from the MWR helps to correctly incorporate information about the thermodynamic stability of atmosphere. The method is applied to data from a Vaisala CT25K ceilometer and a Humidity-and-Temperature Profiler (HAT-PRO) MWR collected during the HD(CP) 2 Observational Prototype Experiment (HOPE) campaign at Jülich, Germany.

A method for the estimation of Stable Boundary Layer Height (SBLH) using curvature of the potential temperature profiles retrieved by a Microwave Radiometer (MWR) is presented. The vertical resolution of the MWR-derived temperature profile decreases with the height. A spline interpolation is carried-out to obtain a uniformly discretized temperature profile. The curvature parameter is calculated from the first and second order derivatives of the interpolated potential temperature profile. The first minima of the curvature parameter signifies the point where the temperature profile starts changing from the stable to the residual conditions. The performance of the method is analyzed by comparing it against physically idealized models of the stable boundary-layer temperature profile available in the literature. There are five models which include stable-mixed, mixed-linear, linear, polynomial and exponential. For a given temperature profile these five models are fitted using the non-linear least-squares approach. The best fitting model is chosen as the one which fits with the minimum root-mean-square error. Comparison of the SBLH estimates from curvature-based method with the physically idealized models shows that the method works qualitatively and quantitatively well with lower variation. Potential application of this approach is the situation where given temperature profiles are significantly deviant from the idealized models. The method is applied to data from a Humidity-and-Temperature Profiler (HATPRO) MWR collected during the HD(CP)(2) Observational Prototype Experiment (HOPE) campaign at Julich, Germany. Radiosonde data, whenever available, is used as the ground-truth.

There are several instruments and methods to retrieve the atmospheric Mixing Layer Height (MLH). However, none of these instruments or methods can measure the development of the MLH under all atmospheric conditions. For example, aerosol signatures measured by backscatter lidars can be used to determine the MLH but this approach is reasonable only when the atmosphere is well-mixed. Microwave Radiometer (MWR) derived profiles have low vertical resolution and cannot resolve fine structures in the boundary layer, especially, at higher altitudes. Here we propose a method which combines data from a ground-based lidar and a MWR, in simulated as well as real measurements scenarios, to overcome these limitations. The method works by fitting an erf-like transition model function to the section of range-corrected lidar backscatter signal. The section of the lidar backscatter signal for fitting the model function is obtained by incorporating the MWR estimates of MLH along with their uncertainties. The fitting is achieved by using an extended Kalman filter (EKF). The proposed approach, by exploiting the synergy between the two instruments, enables to detect MLH with original vertical and temporal resolutions. Test cases combining simulated data for a co-located lidar-ceilometer and a MWR are presented. The simulated data is obtained from the Dutch Atmospheric Large Eddy Simulation (DALES) model for boundary-layer studies. Doppler wind lidar along with radiosondes (whenever available) data is used to assess the quality of the synergetic MLH estimates. Data from the HD(CP)(2) Observational Prototype Experiment (HOPE) campaign at Julich, Germany is used to test the proposed method.

In this paper we present a method for estimating the height of the nocturnal stable boundary layer by using lidar measurements and a single radiosonde for unambiguous initial guess. The method relies on the correlation between aerosol stratifications in the stable boundary layer and minimum variance levels in the attenuated backscatter profile. The method is based on calculating either temporal or spatial variance vertical profiles of the attenuated backscatter and threshold-limited decision. A radiosonde temperature-based estimation is used to provide an initial guess if several minimum variance regions are detected. Two study cases using ceilometer data are shown. Comparison with temperature-based estimations from a collocated microwave radiometer have been used for validation. The method can be useful for estimating the stable boundary layer height in sites with a ceilometer but without any available temperature profiler.

A solution based on a Kalman filter to trace the evolution of the atmospheric boundary layer (ABL) sensed by a ground-based elastic-backscatter tropospheric lidar is presented. An erf-like profile is used to model the mixing-layer top and the entrainment-zone thickness. The extended Kalman filter (EKF) enables to retrieve and track the ABL parameters based on simplified statistics of the ABL dynamics and of the observation noise present in the lidar signal. This adaptive feature permits to analyze atmospheric scenes with low signal-to-noise ratios (SNRs) without the need to resort to long-time averages or range-smoothing techniques, as well as to pave the way for future automated detection solutions. First, EKF results based on oversimplified synthetic and experimental lidar profiles are presented and compared with classic ABL estimation quantifiers for a case study with different SNR scenarios.