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Volume 14
Issue 6
10.3390/rs14061394
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Article

Assessing Obukhov Length and Friction Velocity from Floating Lidar Observations: A Data Screening and Sensitivity Computation Approach

by
Marcos Paulo Araújo da Silva
1,
Francesc Rocadenbosch
1,2,*,
Joan Farré-Guarné
1,
Andreu Salcedo-Bosch
1,
Daniel González-Marco
3,4 and
Alfredo Peña
5
1
CommSensLab-UPC, Department of Signal Theory and Communications, Universitat Politècnica de Catalunya (UPC), C/Jordi Girona, 1-3, E-08034 Barcelona, Spain
2
Institut d’Estudis Espacials de Catalunya (Institute of Space Studies of Catalonia, IEEC), E-08034 Barcelona, Spain
3
Laboratori d’Enginyeria Marítima, Universitat Politècnica de Catalunya, C/Jordi Girona, 1-3, E-08034 Barcelona, Spain
4
Centre Internacional d’Investigació dels Recursos Costaners (CIIRC), C/Jordi Girona, 1-3, E-08034 Barcelona, Spain
5
Wind Energy Department, Technical University of Denmark, Frederiksborgvej 399, 4000 Roskilde, Denmark
*
Author to whom correspondence should be addressed.
Remote Sens. 2022, 14(6), 1394; https://doi.org/10.3390/rs14061394
Submission received: 5 January 2022 / Revised: 21 February 2022 / Accepted: 7 March 2022 / Published: 14 March 2022
(This article belongs to the Topic High-Resolution Earth Observation Systems, Technologies, and Applications)
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Abstract

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).
Keywords: atmospheric stability; lidar; Monin-Obukhov Similarity Theory; offshore; wind energy atmospheric stability; lidar; Monin-Obukhov Similarity Theory; offshore; wind energy

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MDPI and ACS Style

Araújo da Silva, M.P.; Rocadenbosch, F.; Farré-Guarné, J.; Salcedo-Bosch, A.; González-Marco, D.; Peña, A. Assessing Obukhov Length and Friction Velocity from Floating Lidar Observations: A Data Screening and Sensitivity Computation Approach. Remote Sens. 2022, 14, 1394. https://doi.org/10.3390/rs14061394

AMA Style

Araújo da Silva MP, Rocadenbosch F, Farré-Guarné J, Salcedo-Bosch A, González-Marco D, Peña A. Assessing Obukhov Length and Friction Velocity from Floating Lidar Observations: A Data Screening and Sensitivity Computation Approach. Remote Sensing. 2022; 14(6):1394. https://doi.org/10.3390/rs14061394

Chicago/Turabian Style

Araújo da Silva, Marcos Paulo, Francesc Rocadenbosch, Joan Farré-Guarné, Andreu Salcedo-Bosch, Daniel González-Marco, and Alfredo Peña. 2022. "Assessing Obukhov Length and Friction Velocity from Floating Lidar Observations: A Data Screening and Sensitivity Computation Approach" Remote Sensing 14, no. 6: 1394. https://doi.org/10.3390/rs14061394

APA Style

Araújo da Silva, M. P., Rocadenbosch, F., Farré-Guarné, J., Salcedo-Bosch, A., González-Marco, D., & Peña, A. (2022). Assessing Obukhov Length and Friction Velocity from Floating Lidar Observations: A Data Screening and Sensitivity Computation Approach. Remote Sensing, 14(6), 1394. https://doi.org/10.3390/rs14061394

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