Enhanced dual filter for floating wind lidar motion correction: The impact of wind and initial scan phase models

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 ReviewedPostprint (published version

On adaptive unscented Kalman filtering for floating Doppler wind-lidar motion correction: Effect of the number of lidar measurement heights

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 ReviewedPostprint (author's final draft

Forward method for vertical air motion estimation from frequency modulated continuous wave radar rain measurements

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 ReviewedPostprint (published version

Numerical solver for vertical air motion estimation

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 ReviewedPostprint (author's final draft

Estimation of the motion-induced horizontal-wind-speed standard deviation in an offshore doppler lidar

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 ReviewedPostprint (published version

A unified formulation for the computation of the six-degrees-of-freedom-motion-induced errors in floating Doppler wind LiDARs

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 ReviewedPostprint (published version

Assessing Obukhov length and friction velocity from floating lidar observations: A data screening and sensitivity computation approach

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 (ρ2=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).This research was part of the projects PGC2018-094132-B-I00 and MDM-2016-0600 (Comm- SensLab Excellence Unit) funded by the Ministerio de Ciencia e Investigación (MCIN)/Agencia Estatal de Investigación (AEI)/10.13039/501100011033/ FEDER. The work of M.P.A.S was supported under grant PRE2018-086054 funded by MCIN/AEU/10.13039/501100011033 and FSE “El FSE in- vierte en tu futuro”. The work of A.S-B 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 ReviewedPostprint (published version

Floating lidar assessment of atmospheric stability in the North Sea

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 ReviewedPostprint (author's final draft

Artificial intelligence, LiDAR and co-operative remote sensing for atmospheric observation and off-shore wind energy

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 campaigns. Data clustering techniques are used to assess the quality of the retrieved data products under different scenarios of interest for the industry. The results attained by the novel solutions presented here further demonstrate the capabilities of floating Doppler wind LiDARs for off-shore wind monitoring, which strengthens their position as key remote-sensing instruments for the off-shore wind energy industry.(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 anemòmetres com a referència. Les dades experimentals provenen principalment de dues campanyes de mesures: la campanya de Pont del Petroli i la campanya d'"IJmuiden". S'han utilitzat eines de clusterització de dades per tal d'estudiar la qualitat dels productes de dades obtinguts en escenaris d'interès per a la indústria eòlica. Els resultats assolits pels mètodes presentats en aquesta tesi mostren les capacitats i possibles aplicacions futures dels LiDARs flotants i reforcen la seva posició com a instrument clau dins la indústria eòlica marina.(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 se han validado con medidas de LiDARs fijos y anemómetros como referencia. Los datos experimentales provienen principalmente de dos campañas de medidas: la campaña de "Pont del Petroli" y la campaña de "IJmuiden". Se han utilizado herramientas de clusterización de datos para estudiar la calidad de los productos de datos obtenidos en escenarios de interés para la industria eólica. Los resultados logrados por los métodos presentados en esta tesis muestran las capacidades y posibles aplicaciones futuras de los LiDARs flotantes y refuerzan su posición como instrumento clave dentro de la industria eólica marina.DOCTORAT EN TEORIA DEL SENYAL I COMUNICACIONS (Pla 2013