This work studies the capabilities of the k-nearest-neighbours (KNN) method to estimate the atmospheric stability from floating Doppler wind lidar measurements of the vertical wind profile. A procedure to generate synthetic wind profiles to be used as training data for the KNN is presented. The method is validated with experimental data gathered during IJmuiden campaign by a floating Doppler wind lidar against a metmast as a reference. A global hit rate of 70.2% is obtained when comparing the predicted stability classes to the reference ones.

Floating-lidar-derived wind profiles are used to retrieve the vertical wind speed gradient, dimensionless wind shear, and dimensionless stability using Monin-Obukhov similarity theory (MOST) in the context of IJmuiden campaign. Reference retrievals are obtained by applying the same methodology to metmast-derived wind profiles. Wind gradient estimates from floating lidar measurements are successfully compared to those from the reference metmast at three different heights, yielding coefficients of determination higher than ρ2= 0.83. MOST flux relationships are also re-encountered when representing the dimensionless wind shear estimates as a function of dimensionless stability.

Wind profiles measured at the research platform FINO1 are used to retrieve surface boundary-layer parameters, namely, the friction velocity, heat flux, and Obukhov length. The latter is used to assess atmospheric stability. Two different retrieval algorithms, the 2D parametric solver [1] and the Hybrid-Wind method [2], are compared with reference to sonic-anemometers retrievals. Regarding atmospheric stability classification, the 2D algorithm provided better performance than the Hybrid-Wind method. When comparing the 2D-estimated (HW-estimated) friction velocity to the anemometers’ reference, a determination coefficient of ρ2= 0.70 (ρ2= 0.00) were obtained.

In this work, the 2D parametric-solver algorithm [1] used to assess atmospheric stability from floating Doppler wind lidar (FDWL) measurements is revisited. The algorithm performance is studied using data from IJmuiden campaign. Mast-measured temperature and wind-speed provided the reference parameters used to evaluate the performance of the stability estimation algorithm. From 5,922 10-min samples available, the algorithm classified the atmosphere as stable (52% of the cases), neutral (31%) and unstable (17%), which successfully agreed with the mast-derived reference classification (53%, 30% and 17%, respectively).

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.

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.

This paper tackles atmospheric stability typing using a Zephyr™ 300 offshore Doppler Wind Lidar in the context of its progressive acceptance in the offshore wind-energy industry. The lidar-retrieved wind-shear exponent, which is used as a proxy atmospheric stability, is compared against the wind-shear exponent and the potential temperature gradient both retrieved from reference metmast. A total sample of 4319 measurements is analysed from IJmuiden's test campaign, in the North Sea, from April 1stto 30th, 2015. Concerning stability typing, both lidar- and metmast-derived wind-shear indicators overestimated by 4% and 14%, respectively, the most frequent atmospheric stability case, which was convective (48% of the cases) according to the potential temperature reference indicator.