This work presents a parametric-solver algorithm for estimating atmospheric stability and friction velocity from floating Doppler wind lidar (FDWL) observations close to the mast of IJmuiden in the North Sea. The focus of the study was two-fold: (i) to examine the sensitivity of the computational algorithm to the retrieved variables and derived stability classes (the latter through confusion-matrix theory), and (ii) to present data screening procedures for FDWLs and fixed reference instrumentation. The performance of the stability estimation algorithm was assessed with reference to wind speed and temperature observations from the mast. A fixed-to-mast Doppler wind lidar (DWL) was also available, which provides a reference for wind-speed observations free from sea-motion perturbations. When comparing FDWL- and mast-derived mean wind speeds, the obtained determination coefficient was as high as that of the fixed-to-mast DWL against the mast (${\rho }^2=0.996$) with a root mean square error (RMSE) of 0.25 m/s. From the 82-day measurement campaign at IJmuiden (10,833 10 min records), the parametric algorithm showed that the atmosphere was neutral (31% of the cases), stable (28%), or near-neutral stable (19%) during most of the campaign. These figures satisfactorily agree with values estimated from the mast measurements ($31\%$, $27\%$, and $19\%$, respectively).

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.

This work presents an analytical formulation to assess the six-degrees-of-freedom-motion-induced error in floating Doppler wind LiDARs (FDWLs). The error products derive from the horizontal wind speed bias and apparent turbulence intensity. Departing from a geometrical formulation of the FDWL attitude and of the LiDAR retrieval algorithm, the contributions of the rotational and translational motion to the FDWL-measured total error are computed. Central to this process is the interpretation of the velocity–azimuth display retrieval algorithm in terms of a first-order Fourier series. The obtained 6 DoF formulation is validated numerically by means of a floating LiDAR motion simulator and experimentally in nearshore and open-sea scenarios in the framework of the Pont del Petroli and IJmuiden campaigns, respectively. Both measurement campaigns involved a fixed and a floating $ZephI{R}^{\mathrm{TM}}$ 300 LiDAR. The proposed formulation proved capable of estimating the motion-induced FDWL horizontal wind speed bias and returned similar percentiles when comparing the FDWL with the fixed LiDAR. The estimations of the turbulence intensity increment statistically matched the FDWL measurements under all motional and wind scenarios when clustering the data as a function of the buoy’s mean tilt amplitude, mean translational-velocity amplitude, and mean horizontal wind speed.

The Northeast region of Brazil (NEB) has a high rate of deaths from lightning strikes (18% of the country’s total). The region has states, such as Piauí, with high mortality rates (1.8 deaths per million), much higher than the national rate (0.8) and the NEB rate (0.5). In this sense, the present work analyzes the microphysical characteristics of clouds with and without the occurrence of total lightning. For this purpose, data from the Lightning Imaging Sensor (LIS), TRMM Microwave Imager (TMI) and Precipitation Radar (PR), aboard the Tropical Rainfall Measuring Mission (TRMM) satellite from 1998 to 2013 were used. The TRMM data were analyzed to establish a relationship between the occurrence of lightning and the clouds’ microphysical characteristics, comparing them as a function of lightning occurrence classes, spatial location and atmospheric profiles. A higher lightning occurrence is associated with higher values of ice water path (>38.9 kg m⁻²), rain water path (>2 kg m⁻²), convective precipitation (>5 mm h⁻¹) and surface precipitation (>7 mm h⁻¹), in addition to slightly higher freezing level height values. Reflectivity observations (>36 dBZ) demonstrated typical convective profile curves, with higher values associated with classes with higher lightning densities (class with more than 6.8 flash km⁻² year⁻¹).

We revisit two recent methodologies based on Monin–Obukhov Similarity Theory (MOST), the 2D method and Hybrid-Wind (HW), which are aimed at estimation of the Obukhov length, friction velocity and kinematic heat flux within the surface layer. Both methods use wind-speed profile measurements only and their comparative performance requires assessment. Synthetic and observational data are used for their quantitative assessment. We also present a procedure to generate synthetic noise-corrupted wind profiles based on estimation of the probability density functions for MOST-related variables (e.g., friction velocity) and the statistics of the noise-corrupting perturbational amplitude found during an 82-day IJmuiden observational campaign. In the observational part of the study, 2D and HW parameter retrievals from floating Doppler wind lidar measurements are compared against those from a reference mast. Overall, the 2D algorithm outperformed the HW in the estimation of all the three parameters above. For instance, when assessing the friction-velocity retrieval performance with reference to sonic anemometers, determination coefficients of ${\rho }_{2D}^2=0.77$ and ${\rho }_{HW}^2=0.33$ were found under unstable atmospheric stability conditions, and ${\rho }_{2D}^2=0.81$ and ${\rho }_{HW}^2=0.07$ under stable conditions, which suggests the 2D algorithm as a prominent method for estimating the above-mentioned surface-layer parameters.

This study describes a method to estimate the nocturnal stable boundary layer height (SBLH) by means of lidar observations. The method permits two approaches which yield independent retrievals through either spatial or temporal variance vertical profiles of the attenuated backscatter. Then, the minimum variance region (MVR) on this profile is identified. Eventually, when multiple MVRs are detected, a temperature-based SBLH estimation derived from radiosonde, launched within the searching time, is used to disambiguate the initial guess. In order to test the method, two study cases employing lidar-ceilometer (Jenoptik CHM 15k Nimbus) measurements are investigated. Temperature-based estimates from a collocated microwave radiometer permitted validation, using either temporal or spatial backscatter variances. The dataset was collected during the HD(CP)2 Observational Prototype Experiment (HOPE) [1].   [1] U. Saeed, F. Rocadenbosch, and S. Crewell, “Adaptive Estimation of the Stable Boundary Layer Height Using Combined Lidar and Microwave Radiometer Observations,” IEEE Trans. Geosci. Remote Sens., 54(12), 6895–6906 (2016), DOI: 10.1109/TGRS.2016.2586298.[2] U. Löhnert, J. H. Schween, C. Acquistapace, K. Ebell, M. Maahn, M. Barrera-Verdejo, A. Hirsikko, B. Bohn, A. Knaps, E. O’Connor, C. Simmer, A. Wahner, and S. Crewell, “JOYCE: Jülich Observatory for Cloud Evolution,” Bulletin of the American Meteorological Society, 96(7), 1157-1174 (2015). DOI: 10.1175/BAMS-D-14-00105.1