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)<sup>2</sup> Observational Prototype Experiment (HOPE) campaign at Jülich, 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)<sup>2</sup> Observational Prototype Experiment (HOPE) campaign at Jülich, Germany is used to test the proposed method.