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Tesi amb menció de Doctorat Internacional, (English) Remote sensing of the atmosphere is widely used by the wind energy industry to both assess future wind farms deployment sites as well as to improve their operation. With the rising interest in offshore wind energy, remote sensing of the atmosphere has become essential in offshore deep-sea sites in order to reduce deployment and operation costs. Particularly, floating LiDARs have become the de-facto instrument for offshore wind resource assessment due to its flexibility and capabilities to measure the wind with equivalent accuracy as meteorological met-masts in a cost-effective manner. The main goal of this Thesis is to study and exploit the potentialities of existing atmospheric remote sensing instruments, with special emphasis on floating Doppler wind LiDARs. Towards this purpose, different signal processing and machine learning solutions are proposed and analysed. First, the correction of the effect of vertical wind on micro-rain-radar measurements is tackled by means of inverse methods. Second, the capabilities of floating LiDARs to retrieve ocean-related parameters is studied. To that end, signal processing techniques based on spectral analysis of the buoy's motion are used to characterize the ocean waves period. Third, and core part of this Thesis, the motion-induced error of floating-LiDAR measurements of the wind is studied in terms of estimation and compensation. Regarding the former, a novel analytical formulation to estimate horizontal-wind-speed bias and turbulence-intensity-increment error products is presented. The latter topic is tackled using machine learning techniques, specifically, an Unscented Kalman filter for motion compensation of floating-Doppler-wind-LiDAR measurements is presented, and its performance is analyzed under different wave and atmospheric scenarios. The presented methodologies are validated against reference fixed LiDARs and anemometers in the context of two measurement campaigns: "Pont del Petroli" and IJmuiden campaign, (Català) La indústria eòlica utilitza la teledetecció de l'atmosfera com a eina per identificar futurs emplaçaments de camps eòlics així com per optimitzar el seu funcionament. En les últimes dècades, amb l'increment de camps eòlics marins, la teledetecció ha esdevingut fonamental per reduïr el cost de les seves instal·lacions. Especialment, els LiDAR Doppler flotants han guanyat terreny en la indústria eòlica marina com a instrument estandard per a la evaluació del recurs eòlic gràcies al seu baix cost, la seva flexibilitat i la seva precisió en mesurar el vent en comparació amb altres mètodes existents. L'objectiu principal d'aquesta tesi és l'estudi i explotació de les capacitats d'instruments de teledetecció ja existents, amb especial èmfasi en el LiDAR Doppler flotant. Per a fer-ho, s'analitzaren una sèrie de mètodes de processat de senyal i "machine learning". En primer lloc s'estudia la correcció de l'efecte del vent vertical en mesures del radar de pluja "micro-rain-radar" emprant mètodes inversos. En segon lloc s'abordaren les possibles aplicacions del LiDAR flotant en la mesura de paràmetres oceanogràfics. En aquest sentit, es presenta un nou mètode basat en estimació espectral per poder estimar el període de les onades a partir de mesures de la inclinació del LiDAR flotant. En tercer lloc, la part principal de la tesi aborda l'estimació i la correcció de l'error causat per l'onatge en les mesures dels LiDAR Doppler flotants. Pel que fa a l'estimació de l'error, es desenvolupa una formulació analítica per estimar el biaix de la velocitat horitzontal del vent així com l'increment de la intensitat de turbulència. Pel que fa a la correcció de l'error, es presenta un "Unscented Kalman filter" capaç de corregir l'error en les mesures del vent causat per l'onatge en LiDARs flotants. A més a més, el seu rendiment s'estudia en diferents escenaris atmosfèrics i d'onatge d'interès. Els mètodes desenvolupats en aquesta tesi s'han validat amb mesures de LiDARs fixes i, (Español) La industria eólica utiliza la teledetección de la atmósfera como herramienta para identificar futuros emplazamientos de campos eólicos así como para optimizar su funcionamiento. En las últimas décadas, con el incremento de campos eólicos marinos, la teledetección se ha convertido en fundamental para reducir el coste de sus instalaciones. Especialmente, los LiDAR Doppler flotantes han ganado terreno en la industria eólica marina como instrumento estándar para la evaluació del recurso eólico gracias a su bajo coste, su flexibilidad y su precisión al mesurar el viento en comparación con otros métodos. El objetivo principal de esta tesis es el estudio y explotación de las capacidades de instrumentos de teledetección ya existentes, con especial énfasis en el LiDAR Doppler flotante. Para hacerlo, se analizaron una serie de métodos de procesado de señal y "machine learning". En primer lugar se estudia la corrección del efecto del viento vertical en medidas del radar de lluvia "micro-rain-radar" empleando métodos inversos. En segundo lugar se abordaran las posibles aplicaciones del LiDAR flotante en la medida de parámetros oceanográficos. En este sentido, se presenta un nuevo método basado en estimación espectral para poder estimar el periodo de oleaje a partir de medidas de la inclinación del LiDAR flotante. En tercer lugar, la parte principal de la tesis aborda la estimación y la corrección del error causado por el oleaje en las medidas de los LiDAR Doppler flotantes. En cuanto a la estimación del error, se desarrolla una formulación analítica para estimar el sesgo de la velocidad horizontal del viento así como el incremento de la intensidad de turbulencia. En cuanto a la corrección del error, se presenta un "Unscented Kalman filter" capaz de corregir el error en las medidas del viento causado por el oleaje en LiDARs flotantes. Además, su rendimiento se estudia en diferentes escenarios atmosféricos y de oleaje de interés. Los métodos desarrollados en esta tesis, Postprint (published version)

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 research is part of the project PID2021-126436OB-C21 funded by Ministerio de Ciencia e Investigacion (MCIN)/ Agencia Estatal de Investigacion (AEI)/ 10.13039/501100011033 y FEDER “Una manera de hacer Europa”. The work of A. Salcedo-Bosch was supported under grant 2020 FISDU 00455 funded by Generalitat de Catalunya—AGAUR. The European Commission collaborated under projects H2020 ATMO-ACCESS (GA101008004) and H2020 ACTRIS-IMP (GA-871115)., Peer Reviewed, Postprint (author's final draft)

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 ZephIRTM 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., This research project was part of the project PID2021-126436OB-C21 funded by the Ministerio de Ciencia e Investigación (MCIN)/Agencia Estatal de Investigación (AEI)/10.13039/501100011033 y FEDER “Una manera de hacer Europa”. The work of A. Salcedo-Bosch was supported by grant 2020 FISDU 00455 funded by Generalitat de Catalunya—AGAUR. The work of M.P Araújo da Silva was supported under Grant PRE2018-086054 funded by MCIN/AEI/10.13039/501100011033 and FSE “El FSE invierte en tu futuro”. The European Commission collaborated under projects H2020 ATMO-ACCESS (GA-101008004) and H2020 ACTRIS-IMP (GA-871115)., Peer Reviewed, Postprint (published version)

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., This research is part of the project PID2021-126436OB-C21 funded by Ministerio de Ciencia e Investigacion (MCIN)/ Agencia Estatal de Investigacion (AEI)/ 10.13039/501100011033/ FEDER “Una manera de hacer Europa”. The work of A. Salcedo-Bosch was supported under grant 2020 FISDU 00455 funded by Generalitat de Catalunya—AGAUR. The European Commission collaborated under projects H2020 ATMO-ACCESS (GA101008004) and H2020 ACTRIS-IMP (GA-871115)., Peer Reviewed, Postprint (author's final draft)

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 P22D = 0.77 and P2HW = 0.33 were found under unstable atmospheric stability conditions, and P22D = 0.81 and P2HW = 0.07 under stable conditions, which suggests the 2D algorithm as a prominent method for estimating the above-mentioned surface-layer parameters.

This work presents an analytical formulation to assess the six-degrees-of-freedom motion-induced error on floating Doppler Wind lidars (FDWL). Error products are 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 rotational and translational motion contributions 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 numerically validated by means of a floating-lidar motion simulator and, experimentally, in the framework of the 25-day near off-shore measurement campaign carried out at Pont del Petroli, Barcelona, involving a fixed and a floating ZephIR TM 300 lidar. The proposed formulation proved itself able to estimate the motion-induced FDWL horizontal-wind-speed bias and returned similar percentiles to when comparing the FDWL to the fixed lidar. The estimations of the turbulence intensity increment statistically matched the FDWL measurements under all motional scenarios when clustering the data as a function of the buoy mean tilt and translational velocity., Este trabajo presenta una formulación analítica para evaluar el error inducido por el movimiento de seis grados de libertad en los lidares eólicos Doppler flotantes (FDWL). Los productos del error son el sesgo de la velocidad horizontal del viento y la intensidad de la turbulencia aparente. Partiendo de una formulación geométrica de la actitud del FDWL y del algoritmo de recuperación del lidar, se calculan las contribuciones del movimiento rotacional y traslacional al error total medido por el FDWL. Un aspecto central de este proceso es la interpretación del algoritmo de recuperación de velocidad-azimut-disparo en términos de una serie de Fourier de primer orden. La formulación 6-DoF obtenida se valida numéricamente mediante un simulador de movimiento de un lidar flotante y, experimentalmente, en el marco de la campaña de medición de 25 días cerca de la costa llevada a cabo en Pont del Petroli, Barcelona, con un lidar ZephIR TM 300 fijo y otro flotante. La formulación propuesta demostró ser capaz de estimar el sesgo de la velocidad del viento horizontal inducido por el movimiento del FDWL y devolvió percentiles similares al comparar el FDWL con el lidar fijo. Las estimaciones del incremento de la intensidad de la turbulencia coincidieron estadísticamente con las mediciones del FDWL en todos los escenarios de movimiento al agrupar los datos en función de la inclinación media de la boya y la velocidad de traslación., Aquest treball presenta una formulació analítica per avaluar l'error induït pel moviment de sis graus de llibertat als llidars eòlics Doppler flotants (FDWL). Els productes de l'error són el biaix de la velocitat horitzontal del vent i la intensitat de la turbulència aparent. Partint d'una formulació geomètrica de l'actitud del FDWL i de l'algorisme de recuperació del lidar, es calculen les contribucions del moviment rotacional i translacional a l'error total mesurat pel FDWL. Un aspecte central d'aquest procés és la interpretació de l'algorisme de recuperació de velocitat-azimut-tret en termes d'una sèrie de Fourier de primer ordre. La formulació 6-DoF obtinguda es valida numèricament mitjançant un simulador de moviment d'un lidar flotant i, experimentalment, en el marc de la campanya de mesurament de 25 dies a prop de la costa duta a terme a Pont del Petroli, Barcelona, ??amb un lidar ZephIR TM 300 fix i un altre flotant. La formulació proposada va demostrar ser capaç d'estimar el biaix de la velocitat del vent horitzontal induït pel moviment del FDWL i va tornar percentils similars en comparar el FDWL amb el lidar fix. Les estimacions de l'increment de la intensitat de la turbulència van coincidir estadísticament amb els mesuraments del FDWL a tots els escenaris de moviment en agrupar les dades en funció de la inclinació mitjana de la boia i la velocitat de translació.

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., This research was funded by the Spanish Government and EU Regional Development Funds, ARS project PGC2018-094132-B-I00, H2020 ACTRIS-IMP project GA-871115 and H2020 ATMO-ACCESS project GA101008004. The European Institute of Innovation and Technology (EIT), KIC InnoEnergy project NEPTUNE (call FP7) supported the measurement campaigns. The Generalitat de Catalunya—AGAUR funded doctoral grant 2020 FISDU 00455 by A. Salcedo-Bosch. CommSensLab-UPC is an Excellence Unit (MDM-2016-0600) funded by the Agencia Estatal de Investigacion, Spain., Peer Reviewed, Postprint (author's final draft)

An enhanced filter for floating Doppler wind lidar motion correction is presented. The filter relies on an unscented Kalman filter prototype for floating-lidar motion correction without access to the internal line-of-sight measurements of the lidar. In the present work, we implement a new architecture based on two cooperative estimation filters and study the impact of different wind and initial scan phase models on the filter performance in the coastal environment of Barcelona. Two model combinations are considered: (i) a basic random walk model for both the wind turbulence and the initial scan phase and (ii) an auto-regressive model for wind turbulence along with a uniform circular motion model for the scan phase. The filter motion-correction performance using each of the above models was evaluated with reference to a fixed lidar in different wind and motion scenarios (low- and high-frequency turbulence cases) recorded during a 25-day campaign at “Pont del Petroli”, Barcelona, by clustered statistical analysis. The auto-regressive wind model and the uniform circular motion phase model permitted the filter to overcome divergence in all wind and motion scenarios. The statistical indicators comparing both instruments showed overall improvement. The mean deviation increased from 1.62% (without motion correction) to -0.07% (with motion correction), while the root-mean-square error decreased from 1.87% to 0.58%, and the determination coefficient (R2) improved from 0.90 to 0.96., This research project was part of projects PGC2018-094132-B-I00 and MDM-2016-0600 (“CommSensLab” Excellence Unit) funded by Ministerio de Ciencia e Investigación (MCIN)/ Agencia Estatal de Investigación (AEI)/ 10.13039/501100011033/ FEDER “Una manera de hacer Europa”. The work of A. Salcedo-Bosch was supported by grant 2020 FISDU 00455 funded by Generalitat de Catalunya—AGAUR. The European Commission collaborated under projects H2020 ACTRIS-IMP (GA-871115) and H2020 ATMO-ACCESS (GA-101008004). The European Institute of Innovation and Technology (EIT), KIC InnoEnergy project NEPTUNE (call FP7), supported the measurement campaigns., Peer Reviewed, Postprint (published version)

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 research is part of the projects PGC2018-094132-B-I00 and MDM2016-0600 (“CommSensLab” Excellence Unit) funded by Ministerio de Ciencia e Investigación (MCIN)/ Agencia Estatal de Investigación (AEI)/10.13039/501100011033/ FEDER “Una manera de hacer Europa”. The work of M.P Araujo da Silva was supported under Grant PRE2018-086054 funded by MCIN/AEI/10.13039/501100011033 and FSE “El FSE invierte en tu futuro”. The work of A. Salcedo-Bosch was supported under grant 2020 FISDU 00455 funded by Generalitat de Catalunya—AGAUR. The European Commission collaborated under projects H2020 ACTRIS-IMP (GA-871115) and H2020 ATMO-ACCESS (GA-101008004)., Peer Reviewed, Postprint (author's final draft)

An enhanced filter for floating Doppler wind lidar motion correction is presented. The filter relies on an unscented Kalman filter prototype for floating-lidar motion correction without access to the internal line-of-sight measurements of the lidar. In the present work, we implement a new architecture based on two cooperative estimation filters and study the impact of different wind and initial scan phase models on the filter performance in the coastal environment of Barcelona. Two model combinations are considered: (i) a basic random walk model for both the wind turbulence and the initial scan phase and (ii) an auto-regressive model for wind turbulence along with a uniform circular motion model for the scan phase. The filter motion-correction performance using each of the above models was evaluated with reference to a fixed lidar in different wind and motion scenarios (low- and high-frequency turbulence cases) recorded during a 25-day campaign at “Pont del Petroli”, Barcelona, by clustered statistical analysis. The auto-regressive wind model and the uniform circular motion phase model permitted the filter to overcome divergence in all wind and motion scenarios. The statistical indicators comparing both instruments showed overall improvement. The mean deviation increased from 1.62% (without motion correction) to −0.07% (with motion correction), while the root-mean-square error decreased from 1.87% to 0.58%, and the determination coefficient (R2 ) improved from 0.90 to 0.96.

Vertically-pointed Frequency-Modulated Continuous-Wave (FMCW) radar measurements of rain are greatly influenced by strong vertical winds (vertical air motion, VAM) in convective rain scenarios. Particularly, 2nd order products such as rain rate (RR) and drop size distribution (DSD) experience high estimation errors due to VAM. In this work, we consider the estimation of VAM from vertically-pointed FMCW radar measurements in order to correct VAM-corrupted rain 2nd order products. We present preliminary research on a forward method to estimate VAM velocity at a particular height from S-band FMCW radar measurements in convective rain scenarios. The method relies on the parameterization of the DSD as a gamma distribution. It estimates the VAM along with the constitutive parameters of the gamma distribution by means of a parametric solver. The methodology is tested over long-duration, high-resolution measurements by the University of Massachussetts FMCW radar and validated against a ground-based disdrometer in the context of the Verification of the Origins of Rotation in Tornadoes Experiment-Southeast (VORTEX-SE)., Peer Reviewed, Postprint (published version)

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 research is part of the projects PGC2018-094132-B-I00 and MDM2016-0600 (“CommSensLab” Excellence Unit) funded by Ministerio de Ciencia e Investigación (MCIN)/ Agencia Estatal de Investigación (AEI)/10.13039/501100011033/ FEDER “Una manera de hacer Europa”. The work of A. Salcedo-Bosch was supported under grant 2020 FISDU 00455 funded by Generalitat de Catalunya—AGAUR. The European Commission collaborated under projects H2020 ACTRIS-IMP (GA-871115) and H2020 ATMOACCESS (GA-101008004)., Peer Reviewed, Postprint (author's final draft)

We present research on two methods to estimate the Vertical Air Motion (VAM) velocity by comparing rainfall observations from two different sensing instruments: an S-band radar and a ground-based disdrometer during the Verification of the Origins of Rotation in Tornadoes Experiment-Southeast (VORTEX-SE)., Presentamos una investigación sobre dos métodos para estimar la velocidad del movimiento vertical del aire (VAM) mediante la comparación de las observaciones de lluvia de dos instrumentos de detección diferentes: un radar de banda S y un disdrómetro terrestre durante la Verificación de los Orígenes de la Rotación en el Experimento de tornados-Sureste. (VORTEX-SE)., Presentem investigacions sobre dos mètodes per estimar la velocitat del moviment vertical de l'aire (VAM) comparant les observacions de pluja de dos instruments de detecció diferents: un radar de banda S i un disdròmetre terrestre durant la Verificació dels Orígens de la Rotació en l'Rxperiment de Tornados-Sud-Est. (VORTEX-SE).

This work proposes a new wave-period estimation (L-dB) method based on the power-spectral-density (PSD) estimation of pitch and roll motional time series of a Doppler wind lidar buoy under the assumption of small angles (±22 deg) and slow yaw drifts (1 min), and the neglection of translational motion. We revisit the buoy’s simplified two-degrees-of-freedom (2-DoF) motional model and formulate the PSD associated with the eigenaxis tilt of the lidar buoy, which was modelled as a complex-number random process. From this, we present the L-dB method, which estimates the wave period as the average wavelength associated to the cutoff frequency span at which the spectral components drop off L decibels from the peak level. In the framework of the IJmuiden campaign (North Sea, 29 March–17 June 2015), the L-dB method is compared in reference to most common oceanographic wave-period estimation methods by using a TriaxysTM buoy. Parametric analysis showed good agreement (correlation coefficient, ρ = 0.86, root-mean-square error (RMSE) = 0.46 s, and mean difference, MD = 0.02 s) between the proposed L-dB method and the oceanographic zero-crossing method when the threshold L was set at 8 dB., This research was funded by the Spanish Government and EU Regional Development Funds, ARS project PGC2018-094132-B-I00 and ACTRIS-IMP project GA-871115. The European Institute of Innovation and Technology (EIT), KIC InnoEnergy project NEPTUNE (call FP7) supported the measurement campaigns. The Generalitat de Catalunya—AGAUR funded doctoral grant 2020 FISDU 00455 by A. Salcedo-Bosch. CommSensLab-UPC is an Excellence Unit (MDM-2016-0600) funded by the Agencia Estatal de Investigación, Spain., Peer Reviewed, Postprint (published version)

In the context of offshore wind energy, LIDAR technology is emerging with the use of floating Doppler Wind Lidars. These offshore devices have arisen as an alternative to traditional meteorological masts in the wind resource assessment phase of offshore wind farm deployments. When placed atop buoys, DWLs can assess the wind resource in a cost-effective way compared to expensive meteorological masts. Measuring wind conditions offshore can provide information that can considerably reduce the costs of offshore wind farms. Moreover, they can be easily moved to new locations to cover large areas, offering high versatility. Nowadays floating DWLs are being commercially accepted in the wind energy industry. However, floating DWLs suffer from translational and rotational motion (6 Degrees of Freedom, DoF) due to the waves movement, which induce errors in the wind vector retrieval. Multiple studies address the floating DWL motion compensation in the state of the art. On the other hand, the study of the 6 DoF motion effect over wind vector retrieval error still needs a thorough study. Floating DWLs measure multiple lines of sights following a conical scanning pattern. These lines of sights are wind vector projections over the LIDAR pointing direction. Then, the wind vector is retrieved from the measured LoSs by means of the vertical azimuth display algorithm. The complexity of vector transformations and the high non-linearity of VAD algorithm compel the development of a floating DWL simulator in order to analyse the effect of 6 DoF motion over the wind vector retrieval. On the other hand, one of the applications of these instruments, the floating DWL, is related with atmosphere stability. With their measurements you can determine how the wind shear behaves in terms of stability and comparing these measurements with the meteorological masts ones, various conclusions can be obtained explaining that in future terms, floating Doppler Wind Lidar will be able to replace these meteo, En el contexto de la energía eólica marina, la tecnología LIDAR está emergiendo con el uso de Lidars eólicos Doppler flotantes. Estos dispositivos marinos han surgido como una alternativa a los mástiles meteorológicos tradicionales en la fase de evaluación de recursos eólicos de los despliegues de parques eólicos marinos. Cuando se colocan sobre boyas, los DWL pueden evaluar el recurso eólico de una manera rentable en comparación con los costosos mástiles meteorológicos. La medición de las condiciones del viento en alta mar puede proporcionar información que puede reducir considerablemente los costes de los parques eólicos marinos. Además, se pueden mover fácilmente a nuevas ubicaciones para cubrir grandes áreas, ofreciendo una gran versatilidad. Hoy en día, los DWL flotantes están siendo aceptados comercialmente en la industria de la energía eólica. Sin embargo, los DWL flotantes sufren un movimiento de traslación y rotación (6 grados de libertad, DoF) debido al movimiento de las olas, que inducen errores en la recuperación del vector del viento. Múltiples estudios abordan la compensación de movimiento DWL flotante en el estado de la técnica. Por otro lado, el estudio del efecto de movimiento de 6 DoF sobre el error de recuperación del vector de viento aún necesita un estudio exhaustivo. Los DWL flotantes miden múltiples líneas de visión siguiendo un patrón de escaneo cónico. Estas líneas de visión son proyecciones de vectores de viento sobre la dirección de apuntado del LIDAR. Luego, el vector de viento se recupera de los LoS medidos por medio del algoritmo de visualización de azimut vertical. La complejidad de las transformaciones vectoriales y la alta no linealidad del algoritmo VAD obligan al desarrollo de un simulador DWL flotante para analizar el efecto del movimiento de 6 DoF sobre la recuperación del vector del viento. Por otro lado, una de las aplicaciones de estos instrumentos, el DWL flotante, está relacionada con la estabilidad atmosférica. Con sus medi, En el context de l'energia eòlica marina, la tecnologia LIDAR està sorgint amb l'ús de lidars eòlics flotants Doppler. Aquests dispositius marins han sorgit com a alternativa als pals meteorològics tradicionals en la fase d'avaluació dels recursos eòlics dels desplegaments de parcs eòlics marins. Quan es col·loquen a sobre de boies, els DWL poden avaluar el recurs eòlic d'una manera rendible en comparació amb els costosos pals meteorològics. Mesurar les condicions eòliques en alta mar pot proporcionar informació que pot reduir considerablement els costos dels parcs eòlics marins. A més, es poden traslladar fàcilment a noves ubicacions per cobrir grans àrees, oferint una gran versatilitat. Avui dia els DWL flotants s'accepten comercialment a la indústria de l'energia eòlica. No obstant això, els DWL flotants pateixen un moviment de translació i rotació (6 graus de llibertat, DoF) a causa del moviment de les ones, que indueixen errors en la recuperació del vector del vent. Diversos estudis tracten la compensació del moviment DWL flotant en l'estat de la tècnica. D'altra banda, l'estudi de l'efecte de moviment de 6 DoF sobre l'error de recuperació de vectors del vent encara necessita un estudi exhaustiu. Els DWL flotants mesuren diverses línies de vistes seguint un patró d'escaneig cònic. Aquestes línies de vistes són projeccions vectorials del vent sobre la direcció apuntant LIDAR. Aleshores, el vector del vent es recupera dels LoS mesurats mitjançant l'algorisme de visualització de l'azimut vertical. La complexitat de les transformacions vectorials i l'alta no linealitat de l'algoritme VAD obliguen al desenvolupament d'un simulador DWL flotant per tal d'analitzar l'efecte del moviment de 6 DoF sobre la recuperació del vector del vent. D'altra banda, una de les aplicacions d'aquests instruments, el DWL flotant, està relacionada amb l'estabilitat de l'atmosfera. Amb les seves mesures es pot determinar com es comporta la cisalla del vent en termes d'estabilitat i compar

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., This work was supported via Spanish Government–European Regional Development Funds project PGC2018-094132-B-I00 and H2020 ACTRIS- IMP (GA-871115). The European Institute of Innovation and Technology (EIT), KIC InnoEnergy project NEPTUNE (Offshore Metocean Data Measuring Equipment and Wind, Wave and Current Analysis and Forecasting Software, call FP7) supported measurements campaigns. CommSensLab is a María-de-Maeztu Unit of Excellence funded by the Agencia Estatal de Investigación (Spanish National Science Foundation). The work of Andreu Salcedo was supported by the “Agència de Gestió d’Adjuts Universitaris i de la Recerca (AGAUR)”, Generalitat de Catalunya, under Grant no. 2020 FISDU 00455., Peer Reviewed, Postprint (author's final draft)

This study presents a new method for correcting the six degrees of freedom motion-induced error in ZephIR 300 floating Doppler Wind-LiDAR-derived data, based on a Robust Adaptive Unscented Kalman Filter. The filter takes advantage of the known floating Doppler Wind-LiDAR (FDWL) dynamics, a velocity–azimuth display algorithm, and a wind model describing the LiDAR-retrieved wind vector without motion influence. The filter estimates the corrected wind vector by adapting itself to different atmospheric and motion scenarios, and by estimating the covariance matrices of related noise processes. The measured turbulence intensity by the FDWL (with and without correction) was compared against a reference fixed LiDAR over a 25-day period at “El Pont del Petroli”, Barcelona. After correction, the apparent motion-induced turbulence was greatly reduced, and the statistical indicators showed overall improvement. Thus, the Mean Difference improved from -1.70% (uncorrected) to 0.36% (corrected), the Root Mean Square Error (RMSE) improved from 2.01% to 0.86%, and coefficient of determination improved from 0.85 to 0.93., This research is part of the projects PGC2018-094132-B-I00 and MDM-2016-0600 (“Comm- SensLab” Excellence Unit) funded by Ministerio de Ciencia e Investigación (MCIN)/ Agencia Estatal de Investigación (AEI)/ 10.13039/501100011033/ FEDER “Una manera de hacer Europa”. The work of A. Salcedo-Bosch was supported under grant 2020 FISDU 00455 funded by Generalitat de Catalunya—AGAUR. The European Commission collaborated under projects H2020 ACTRIS-IMP (GA-871115) and H2020 ATMO-ACCESS (GA-101008004). The European Institute of Innovation and Technology (EIT), KIC InnoEnergy project NEPTUNE (call FP7) supported the measurement campaigns., Peer Reviewed, Postprint (published version)

This study presents a new method for correcting the six degrees of freedom motion-induced error in ZephIR 300 floating Doppler Wind-LiDAR-derived data, based on a Robust Adaptive Unscented Kalman Filter. The filter takes advantage of the known floating Doppler Wind-LiDAR (FDWL) dynamics, a velocity-azimuth display algorithm, and a wind model describing the LiDARretrieved wind vector without motion influence. The filter estimates the corrected wind vector by adapting itself to different atmospheric and motion scenarios, and by estimating the covariance matrices of related noise processes. The measured turbulence intensity by the FDWL (with and without correction) was compared against a reference fixed LiDAR over a 25-day period at “El Pont del Petroli”, Barcelona. After correction, the apparent motion-induced turbulence was greatly reduced, and the statistical indicators showed overall improvement. Thus, the Mean Difference improved from -1.70% (uncorrected) to 0.36% (corrected), the Root Mean Square Error (RMSE) improved from 2.01% to 0.86%, and coefficient of determination improved from 0.85 to 0.93.

This paper proposes two methods to estimate the so-called “characteristic motional period” of a floating wind-lidar buoy. These techniques aim to characterise the buoy's pitch and roll tilting as simple harmonic motions by estimating their period. The “peak method” (PM) and the “3-dB method” are introduced as two different aproaches to study the multi-modality of the wave motion. Additionally, the off-shore wind measurement campaign at Ijmuiden's is briefly introduced to contextualize where data comes from and the added value of this study., This work was supported via Spanish Government–European Regional Development Funds project PGC2018-094132-B-I00 and EU H2020 ACTRIS-2 (GA 654109). The European Institute of Innovation and Technology (EIT), KIC InnoEnergy project NEPTUNE (Offshore Metocean Data Measuring Equipment and Wind, Wave and Current Analysis and Forecasting Software, call FP7) supported measurements campaigns. CommSensLab is a María-de-Maeztu Unit of Excellence funded by the Agencia Estatal de Investigacion (Spanish National Science Foundation)., Peer Reviewed, Postprint (author's final draft)

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 1 st to 30 th, 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., This work was supported via Spanish Government–European Regional Development Funds project PGC2018-094132-B-I00 and EU H2020 ACTRIS-2 (GA 654109). The European Institute of Innovation and Technology (EIT), KIC InnoEnergy project NEPTUNE (Offshore Metocean Data Measuring Equipment and Wind, Wave and Current Analysis and Forecasting Software, call FP7) supported measurements campaigns. CommSensLab is a María-de-Maeztu Unit of Excellence funded by the Agencia Estatal de Investigacion (Spanish National Science Foundation) that also funded the project MDM-2016-0600-18-1. Spanish NSF (Ministry of Science, Innovation and Universities; Ministerio de Ciencia, Innovacion y Universidades) for doctoral grant PRE2018-086054 hold by Ph.D. student Araujo da Silva M.P. at CommSensLab-UPC., Peer Reviewed, Postprint (author's final draft)

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.)., This work was supported via Spanish Government–European Regional Development Funds project PGC2018-094132-B-I00 and EU H2020 ACTRIS-2 (GA 654109). The European Institute of Innovation and Technology (EIT), KIC InnoEnergy project NEPTUNE (Offshore Metocean Data Measuring Equipment and Wind, Wave and Current Analysis and Forecasting Software, call FP7) supported measurements campaigns. CommSensLab is a María-de-Maeztu Unit of Excellence funded by the Agencia Estatal de Investigacion (Spanish National Science Foundation)., Peer Reviewed, Postprint (author's final draft)

This work presents a new methodology to estimate the motion-induced standard deviation and related turbulence intensity on the retrieved horizontal wind speed by means of the velocity-azimuth-display algorithm applied to the conical scanning pattern of a floating Doppler lidar. The method considers a ZephIR™300 continuous-wave focusable Doppler lidar and does not require access to individual line-of-sight radial-wind information along the scanning pattern. The method combines a software-based velocity-azimuth-display and motion simulator and a statistical recursive procedure to estimate the horizontal wind speed standard deviation—as a well as the turbulence intensity—due to floating lidar buoy motion. The motion-induced error is estimated from the simulator’s side by using basic motional parameters, namely, roll/pitch angular amplitude and period of the floating lidar buoy, as well as reference wind speed and direction measurements at the study height. The impact of buoy motion on the retrieved wind speed and related standard deviation is compared against a reference sonic anemometer and a reference fixed lidar over a 60-day period at the IJmuiden test site (the Netherlands). Individual case examples and an analysis of the overall campaign are presented. After the correction, the mean deviation in the horizontal wind speed standard deviation between the reference and the floating lidar was improved by about 70%, from 0.14 m/s (uncorrected) to -0.04 m/s (corrected), which makes evident the goodness of the method. Equivalently, the error on the estimated turbulence intensity (3–20 m/s range) reduced from 38% (uncorrected) to 4% (corrected)., Peer Reviewed, Postprint (published version)