Retrieving accurate turbulence intensity from motion-corrupted floating lidar measurements is one of the main challenges of the offshore wind energy industry today. Toward overcoming this challenge, this work describes a method to retrieve the motion-corrected turbulence intensity from floating lidar measurements. The method relies on an atmospheric turbulence model combined with lidar-based flow characteristics estimates. The method simulates floating-lidar and anemometer-like measurements via synthetically-generated wind fields in turbulence boxes. We highlight the importance of accurate modeling of turbulence-box parameters to virtually eliminate motion-induced error. The proposed method was successfully tested over a 3-month campaign at IJmuiden by comparing floating-lidar turbulence measurements against those from measurements from a reference meteorological mast.

This work focuses on the feasibility of radial spatial averaging to improve satellite Synthetic Aperture Radar (SAR) ocean Horizontal Wind Speed (HWS) retrievals even in neutral atmosphere situations. The reference for comparison are in-situ HWS measurements from the FINO-1 offshore metmast. The data used from the Sentinel-1 SAR are the Level 2 Ocean (OCN) products for wind, wave, and current applications, retrieving the information from the Ocean Wind Field (OWI) component through Geophysical Model Function (GMF) CMOD-IFR2. The GMF allows for estimation of the equivalent wind speed at 10 meters and, therefore, stable and convective regimes can bias the estimations. The best agreement between SAR and FINO-1 was found -as expected- for neutral atmospheres, which yielded a determination coefficient of R2= 0.90 and a root mean squared error (RMSE) of 1.2 m/s. In non-neutral scenarios, statistical indicators showed that fine-tuned spatial averaging is also able to improve SAR HWS retrievals and yielded comparable statistical indicators (R2= 0.87, RMSE = 1.1 m/s) for the HWS estimates.

This work presents a novel method to statistically compensate for the apparent motion-induced turbulence in floating Doppler-Wind LiDAR measurements. The method relies on the analytical model [1], recently published by the authors, applied to the ZephIRTM300 continuous-wave LiDAR. Performance of the method is studied over two floating LiDAR measurement sites/configurations, a mild near-offshore Mediterranean site and an open-sea site (IJmuiden, North Sea), and compared to fixed LiDAR measurements as a reference. The method outperforms other motion compensation approaches based on turbulence increment estimation with less computational effort.

This work analyzes the influence of open-sea wave motion on the performance of the floating Doppler wind lidar motion-compensation method [1], recently published by the authors. The method is applied to a floating ZephIR 300 LiDAR and compared to a fixed LiDAR as a reference. The motion-correction results are studied as a function of the wave-motion intensity by clustering the IJmuiden experimental campaign into three categories: low, medium, and high motion.

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.

We present preliminary research on a method to estimate Vertical Air Motion (VAM) at a particular height by comparing the measured rain-rate (RR) by a vertically-pointing S-band Frequency-Modulated Continuous-Wave (FMCW) radar with that of a ground-based disdrometer. The method is based on a constrained parametric solver, assuming high correlation between 5-min averaged rain rates measured by the radar and disdrometer. The method is tested over disdrometer and radar observations during the Verification of the ORigins Tornado EXperiment in South East US (VORTEX-SE) project. Finally, the results are partially validated by means of fitting a gamma distribution to the VAM-corrected DSD profiles and studying its parameters.

This work studies the influence of the number of lidar measurement heights on the performance of the floating Doppler wind lidar motion-correction algorithm, recently published by the authors. The work is in the context of offshore wind energy and continuous-wave focusable ZephirTM 300 lidar. A downsampling technique applied over the lidar-measured wind speed time-series is used to simulate different height-sounding configurations. The operation of the filter under one, three, and five measurement heights of the lidar is studied by using data from El Pont del Petroli measurement campaign. The filter is proved to remove apparent turbulence addition in all three cases, showing a deterioration of statistical indicators as the number of sounding heights increase.

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 study presents a wind retrieval simulator of a floating Doppler Wind Lidar (DWL) with 6 Degrees of Freedom (DoF) motion. The simulator considers a continuous-wave conical-scanning floating DWL which retrieves the wind vector from 50 radial velocities per scan. The 6 DoF motion framework as well as the most important buoy motion equations are summarized. Rotational and translational motion effects over HWS measurements are studied parametrically.

Relative humidity (RH) profiling using Raman lidars requires simultaneous range-resolved temperature and pressure data that are not always available. We propose and assess a method based on the use of a locally retrieved atmospheric model to estimate the temperature and pressure profiles. This model relies on the data from daily radiosonde launches at Barcelona during a five-year-long period (2015–2019). We have computed the range-resolved error of the model compared with the radiosonde “true” temperature profiles. Then, we have calculated the induced uncertainty in the recalculated RH profiles, finding that the standard deviation at 5 km isin situradiosonde humidity measurements, finding, in this case, bigger differences due to additional sources of error. Uncertainty analysis shows that the temperature model is the most significant error source below 4–5 km. For higher altitudes, the noise of the Raman signals may become the main contribution. We show that the resulting uncertainty (commonly <15% below 5 km) is compatible with the statistical analysis of the model and comparable with the ones obtained using other instruments for temperature profiling. We show that this method permits nocturnal lidar-based RH profiling with uncertainty estimation without the need for measured atmospheric profiles.

The standard deviation of the Horizontal Wind Speed as a proxy of wind turbulence is used to compare the apparent wind turbulence measured by an off-shore floating Doppler lidar to the one measured by a fixed lidar on a metmast. We use statistical analysis based on clustering the horizontal wind speed measured by the floating lidar as well as buoy angular amplitude and period under the approximation of harmonic motion. Three scenarios with different wave and wind conditions are discussed from the IJmuiden's test campaign (North Sea.).

The following topics are dealt with: remote sensing; geophysical image processing; synthetic aperture radar; radar imaging; remote sensing by radar; image classification; learning (artificial intelligence); feature extraction; neural nets; vegetation.

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.

Attenuated backscatter measurements from a Vaisala CL31 ceilometer and a modified form of the well-known slope method are used to derive the ceilometer extinction profiles during rain events, restricted to rainfall rates (RRs) below approximately 10 mm/h. RR estimates from collocated S-band radar and portable disdrometer are used to derive the RR-to-extinction correlation models for the ceilometer-radar and ceilometer-disdrometer combinations. Data were collected during an intensive observation period of the Verification of the Origins of Rotation in Tornadoes Experiment Southeast (VORTEX-SE) conducted in northern Alabama. These models are used to estimate the RR from the ceilometer observations in similar situations that do not have collocated radar or the disdrometer. Such correlation models are, however, limited by the different temporal and spatial resolutions of the measured variables, measurement capabilities of the instruments, and the inherent assumption of a homogeneous atmosphere. An empirical method based on extinction and RR uncertainty scoring and covariance fitting are proposed to solve, in part, these limitations.

Among the different techniques available for measuring the atmospheric water vapor content, Raman lidars stand out as accurate instruments providing detailed profiles with high temporal and altitude resolution. Their principle is based on obtaining the range-resolved ratio of the lidar signals corresponding to Raman returns from water vapor and nitrogen molecules, which is proportional to the water vapor mixing ratio. To do this, it is necessary to determine a calibration factor, specific of each lidar instrument. A method for obtaining this parameter, based on zenith measurements of diffuse sunlight, on Raman scattering models and on simulations, using a radiative transfer model, to estimate sky radiances at the wavelengths of interest, has been applied to the lidar system of Universitat Politècnica de Catalunya (UPC; Technical University of Catalonia, Barcelona, Spain). A set of calibrations, performed between 2016 and 2017, has permitted assessing the calibration procedure and analyzing the stability of the calibration factor in the UPC instrument. Results show that although the calibration factor can remain stable for long periods of time, it can suffer sudden variations that make indispensable to implement a convenient and reliable procedure to perform regular calibrations. We show that the method, which can be applied to any lidar with water vapor and nitrogen Raman channels, can completely dispense with radiosonde data. The calibration method is validated by comparison with simultaneous radiosonde water vapor measurements. Limitations of radiosondes for validating-and eventually calibrating-water vapor Raman lidars have been revealed.

This paper presents the development of a new commercial floating lidar system aimed to measure the wind resource and other environmental variables at candidate wind farm locations. This work describes this new lidar system along with the pilot validation trial (IJmuiden, North Sea) carried out. The two main algorithms developed to correct and qualify wind data to fulfill Key Performance Indicators (KPIs) of wind industry are also presented. The two data-processing algorithms are: homodyne wind-direction correction and outlier data filtering. Finally, results of this pilot validation trial are discussed, showing fulfillment of the wind industry requirements for acceptance of commercial floating lidars.

This works presents a simplified Vertical Azimuth Display (VAD) motion simulator for off-shore wind lidars. The simulator is rooted to the case of a conically-scanning lidar (e.g., the Zephyr lidar), where the wind speed vector is retrieved from the Line-of-Sight velocities over one scan period. The methodological part addresses the geometrical foundations of the simulator and how the lidar attitude is assimilated in matrix form. The discussion part considers the case of time-invariant, horizontally-homogeneous wind under two motional cases of the lidar, static and dynamic. Cases examples are parameterized by Horizontal Wind Speed, Wind Direction and tilt amplitude.

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)2Observational 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.

This paper departs from a preliminary near-shore measurement test campaign hold at El Pont del Petroli (PdP), Barcelona (Spain) where measurements from a Doppler wind-lidar buoy (the ”floating” lidar) are cross-examined against an on-shore reference lidar. From this framework the methodological analysis to intercompare two such lidars in terms of the retrieved Horizontal Wind Speed (HWS) - as key variable - is presented along with an overview of the signal-processing block diagram. Central to this work is to introduce the main error indicators (e.g., bias, RMSE, determination coefficient, absolute error and scatter plots) used to assess the performance of the floating lidar in the campaign. Finally, it is shown that even a basic smoothing procedure on the retrieved 1-s HWS time series is enough to enhance the determination coefficient against the reference lidar Turbulence Intensity (TI).

The nocturnal stable boundary-layer height (SBLH) is estimated from the backscatter lidar data by using an adaptive-filtering approach based on an Extended Kalman Filter (EKF). Height-resolved temporal variance of the lidar data is used as the observables for the processing along with the modelling of minimum-variance regions. The filter is initialized by SBLH estimate from a radio-sonde. The method is applied to the Vaisala CT25K ceilometer data collected during the HD(CP)2Observational Prototype Experiment (HOPE) campaign at Jülich, Germany. Results are compared with SBLH estimates from a collocated microwave radiometer (MWR) and a good degree of agreement is observed.

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)2Observational Prototype Experiment (HOPE) campaign at Jülich, Germany.

This paper addresses a cardanic frame as mechanical-compensation device for a Doppler-wind lidar installed on a floating sea buoy as the “moving” lidar. From the methodological point of view, the horizontal wind speed (HWS) measured by both a reference lidar (i.e., “fixed”) and a floating lidar (i.e., “moving”) with and without a cardarnic frame is cross-examined by using standard statistical indicators and a compound pendulum model. Performance results are analysed both at laboratory level using a pitch/roll motion-simulation platform and at a sea-test measurement-campaign level some 250-m offshore Barcelona coast. Finally, simulations of the proposed pendulum-based model enables to tune-in and optimize cardanic-frame design parameters.

An adaptive solution based on an extended Kalman filter (EKF) is proposed to estimate the atmospheric boundarylayer height (ABLH) from frequency-modulated continuous-wave S-band weather-radar returns. The EKF estimator departs from previous works, in which the transition interface between the mixing layer (ML) and the free troposphere (FT) is modeled by means of an erf-like parametric function. In contrast to lidar remote sensing, where aerosols give strong backscatter returns over the whole ML, clear-air radar reflectivity returns (Bragg scattering from refractive turbulence) shows strongest returns from the ML-FT interface. In addition, they are corrupted by “insect” noise (impulsive noise associated with Rayleigh scattering from insects and birds), all of which requires a specific treatment of the problem and the measurement noise for the clear-air radar case. The proposed radar-ABLH estimation method uses: 1) a first preprocessing of the reflectivity returns based on median filtering and threshold-limited decision to obtain “clean” reflectivity signal; 2) a modified EKF with adaptive range intervals as time tracking estimator; and 3) ad hoc modeling of the observation noise covariance. The method has successfully been implemented in clear-air, single-layer, and convective boundary-layer conditions. ABLH estimates from the proposed radar-EKF method have been cross examined with those from a collocated lidar ceilometer yielding a correlation coefficient as high as ρ = 0.93 (mean signal-to-noise ratio, SNR = 18 (linear units), at the ABLH) and in relation to the classic THM.

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.

The need of a multi-spectral lidar has widely been experienced in last few years with a view to invert the optical and microphysical properties of aerosols and their impact on the climate change. As a part of the EARLINET-GALION objectives, a joint effort has already been made by the European Aerosol Research Lidar Network (EARLINET). The EARLINET advanced standard of 3+2-channel configuration for lidar instruments (3+2 standing for 3 elastic channels and 2 respective Raman channels) enables retrieval of aerosol microphysical properties. An overview of the new RSLAB 3+2+1 multispectral lidar system, therefore, is presented in terms of power budget estimation for all the reception channels and overall system performance, that is, signal-to-noise ratio (SNR) and maximum sounding range achieved.

A methodology aimed at validating elastic-channel optical power return and signal-to-noise ratio (SNR) levels estimated at the link-budget design stage of a tropospheric lidar against the measured ones is presented. A Rayleigh fit along with knowledge of the atmospheric aerosol transmittance and emission energy is used to theoretically estimate the net voltage responsivity. As a further refinement, when simultaneous analog and photon-counting acquisition is available, the method formulates a rough estimate of the optical losses in the receiving chain. Preliminary validation of the link-budget-assessed optical power and SNR levels for the 1064-nm channel of the Remote Sensing Lab. (RSLab) lidar is discussed.

Total backscatter-coefficient inversion error bounds for the two-component lidar inversion algorithm (so-called Fernald's or Klett–Fernald–Sasano's method) are derived in analytical form in response to the following three error sources: 1) the measurement noise; 2) the user uncertainty in the backscatter-coefficient calibration; and 3) the aerosol extinction-to-backscatter ratio. The following two different types of error bounds are presented: 1) approximate error bounds using first-order error propagation and 2) exact error bounds using a total-increment method. Both error bounds are formulated in explicit analytical form, which is of advantage for practical physical sensitivity analysis and computational implementation. A Monte Carlo approach is used to validate the error bounds at 355-, 532-, and 1064-nm wavelengths.

The elastic lidar equation contains two unknown atmospheric parameters, namely, the particulate optical extinction and backscatter coefficients, which are related through the lidar ratio (i.e., the particulate-extinction-to-backscatter ratio). So far, independent inversion of the lidar signal has been carried out by means of Raman lidars (usually limited to nighttime measurements), high-spectral-resolution lidars, or scanning elastic lidars under the assumption of a homogeneously vertically stratified atmosphere. In this paper, we present a procedure to obtain the lidar ratio at 532 nm by a combined Sun-photometer–aerosol-model inversion, where the viability of the solution is largely reinforced by assimilating categorized air-mass back-trajectory information. Thus, iterative lidar-ratio tuning to reconstruct the Sun-photometric aerosol optical depth (AOD) is additionally constrained by the air-mass back trajectories provided by the hybrid single-particle Lagrangian integrated-trajectory model. The retrieved lidar ratios are validated with inversions of lidar data based on the Klett–Fernald–Sasano algorithm and with the Aerosol Robotic Network (AERONET)-retrieved lidar ratios. The estimated lidar ratios concur with the AERONET-retrieved lidar ratios and with those of the well-known KFS inversion constrained with Sun-photometric AOD values and embedded single-scattering models. The proposed method can be applied to routinely extract climatological values of the lidar ratio using measurements of direct solar irradiance (more numerous than those of sky radiance).

A group of eight Spanish lidars was formed in order to extend the European Aerosol Research Lidar Network-Advanced Sustainable Observation System (EARLINET-ASOS) project. This study presents intercomparisons at the hardware and software levels. Results of the system intercomparisons are based on range-square-corrected signals in cases where the lidars viewed the same atmospheres. Comparisons were also made for aerosol backscatter coefficients at 1064 nm (2 systems) and 532 nm (all systems), and for extinction coefficients at 532 nm (2 systems). In total, three field campaigns were carried out between 2006 and 2007. Comparisons were limited to the highest layer found before the free troposphere, i.e., either the atmospheric boundary layer or the aerosol layer just above it. Some groups did not pass the quality assurance criterion on the first attempt. Following modification and improvement to these systems, all systems met the quality criterion. The backscatter algorithm intercomparison consisted of processing lidar signal profiles simulated for two types of atmospheric conditions. Three stages with increasing knowledge of the input parameters were considered. The results showed that all algorithms work well when all inputs are known. They also showed the necessity to perform, when possible, additional measurements to attain better estimation of the lidar ratio, which is the most critical unknown in the elastic lidar inversion.

The European Aerosol Research LIdar NETwork (EARLINET) is an aerosol lidar network on a continental scale. EARLINET is now a leading network in quality-controlled quantitative aerosol profiling performing a schedule of routine measurements and presently consists of 28 stations distributed over Europe. The construction of an un-biased spatio-temporal database of vertical profiles of aerosol optical properties on a regional scale for climate and air quality research is the main objective of EARLINET and is accomplished through application of Raman lidars. One of the tasks in the EC-funded project EARLINET-ASOS is to optimize individual instruments with the aim of homogenization of aerosol data products over the network and increased data coverage by automation. This task is approached by selection of optimal solutions existing in the pool of individual stations. This is done for components, subsystems as well as for system integration. In system integration emphasis lies on automation to reduce the amount of manpower needed, to improve temporal coverage, and to make performance independent from individual operators. The procedure to perform these tasks is outlined and the set of tools enabling the assessment of performance under development is described.

The European Aerosol Research LIdar NETwork (EARLINET) is the first aerosol lidar network on a continental scale with the main goal to provide a comprehensive, quantitative, and statistically significant database for the aerosol distribution over Europe. Next, we present EARLINET along with the main network activities.

This works departs from previously published results of the authors and focus on joint estimation and time evolution of the atmospheric backscatter profile and a range-independent lidar ratio by means of 1) adaptive extended Kalman filtering (EKF) and 2) non-linear least-squares (NLSQ), under moderate-to-low signal-to-noise ratios (SNR<100 at the starting sounding range). A Rayleigh/Mie atmosphere and a calibrated lidar system are considered. Performance parameters studied are data sufficiency, tracking of the optical parameter time fluctuations, inversion errors, power estimation, and noise impact. The EKF inversion solution is, in turn, compared with Klett's method as a reference. Finally, it is shown that the EKF outweighs the NSLQ in noisy environments.

A lidar field campaign was performed between 30thJune and 4thJuly 2007 in the Teide Observatory in the Canarian island of Tenerife to characterize the atmosphere of this astrophysical observation site in terms of nocturnal boundary layer height and wind fields. The nocturnal boundary layer height was found lower than 810 m in all cases and the aerosol optical thickness lower than 0.005 and 0.03 at 1064 and 532 nm, respectively. Wind fields could hardly be retrieved because of faint signals and very weak wind velocities observed during the campaign.

The Lidar group in the Technical University of Catalonia is actively working in wind lidar techniques. An extensive review of such techniques is presented. A special emphasis is made on the direct Doppler technique and the direct motion technique with a lines-of-sight scanning pattern. Current developments are also mentioned.

This article presents an intercomparison between four different ROMTs: differential optical absorption spectroscopy (DOAS), differential absorption LIDAR (DIAL), Fourier transform infrared spectroscopy (FTIR), and tunable diode laser absorption spectroscopy (TDLAS). The main focus is on the TDLAS technique, where the main laser-diode typologies and modulation schemes, namely, wavelength modulation spectroscopy (WMS) and frequency modulation spectroscopy (FMS), are reviewed. At present, new promising modulation schemes with range resolution capability are being investigated. Among them, analog frequency modulation (FM) and digital pseudo-random schemes are discussed.

This article presents step-by-step applied methodology to design a 905-nm 5-kHz rep. rate diode-laser biaxial lidar ceilometer prototype beginning from the definition of initial specs, and related trade-offs to the first measurement tests. It is shown how key variables such as the received and background power, the range-dependent signal-to-noise ratio (SNR), the integration time, and the overlap factor have been simulated and analysed by means of parameter tuning inside acceptable state-of-the-art goal intervals. Main emission and reception subsystems as well as some auxiliary mechanical subsystems (adjustment parts and protection/subjection) are discussed. Finally, preliminary test measurements on topographic targets and storm clouds carried out with the ceilometer prototype are presented. Future improvements and trade-offs are also discussed.

The UPC lidar group is developing a Doppler wind lidar based on the "edge-technique". Such systems use the slope of a high resolution optical filter as frequency discriminator. A laboratory prototype, able to measure the speed of solid targets emitting continuous wave radiation and using a Fabry-Perot interferometer as optical filter, has been built. A tuning control has been included to compensate drifts between the emitted frequency and the filter resonance frequencies in order to assure maximum sensitive measurements. The design of the system and different results concerning the performance of the tuning control system and speed measurements will be presented.

A straightforward signal-to-noise ratio (SNR) estimator for elastic/Raman lidar channels and related noise-induced errorbars is presented. The estimator is based on piece-wise estimation of the mean signal power and noise variance component under analog detection. The piece-wise estimator results are compared with those obtained from a previously published SNR parametric estimator under high and low SNR scenarios.

At the basis of the Raman lidar extinction inversion algorithm is the derivative of the logarithm of the ratio between the atmospheric nitrogen number density and the range-corrected Raman power return. While its computation is straightforward under ideal (noiseless) conditions, this is not the case under low signal-to-noise ratios, for which the observation noise may lead the logarithm to singular values. This work presents an analytical-statistical overview of the inversion problem and related error bars, tentative noise limiting criteria, and justifies the approximations at play to estimate the inversion error by means of error-propagation techniques for high signal- to-noise ratios. Simulation examples consider a 532/607-nm elastic-Raman system.

This article presents a preliminary semi-automated range-interval segmentation toolset for the identification of: 1) the apparent range of full overlap, 2) the clear-sky level (i.e., the molecular level), and cloud layers (cloud-base, cloud-peak, and cloud-top range), and 3) apparent homogeneous extinction intervals.

To extend and reinforce the action of the EARLINETASOS project, a nucleus of Spanish advanced lidars was created. Four systems were intercompared satisfactorily in terms of backscatter coefficients at two elastic wavelengths.

Aerosol distribution in the atmosphere is used as a wind-tracer by lidars since it is drifted by the wind and respond to its changes. Two methods are used: the correlation and the Doppler method. This first one is easier and cheaper to implement than the latter. It makes it competitive for retrieving wind speed profiles or estimating wind turbulence. However, its accuracy decreases significantly as the distance from the optical radiation source increases, hence the need to characterize the method by means of wind field profile simulations and validate it by comparing the retrieval of real wind velocities with that of cooperative instruments such as radiosoundings. In this paper, the bases of a 2D lidar signal simulator are presented. The relationship between wind fields and the aerosol concentration dynamics, and the way they relate to lidar signal returns is explained. The first results of the application of the correlation method for the retrieval of wind velocities from real data at the UPC are presented and compared to radiosoundings measurements.