On the Retrieval of Surface-Layer Parameters from Lidar Wind-Profile Measurements
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...
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MDPI AG
2023-05-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/10/2660 |
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| author | Marcos Paulo Araújo da Silva Andreu Salcedo-Bosch Francesc Rocadenbosch Alfredo Peña |
| author_facet | Marcos Paulo Araújo da Silva Andreu Salcedo-Bosch Francesc Rocadenbosch Alfredo Peña |
| author_sort | Marcos Paulo Araújo da Silva |
| collection | DOAJ |
| description | 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msubsup><mi>ρ</mi><mrow><mn>2</mn><mi>D</mi></mrow><mn>2</mn></msubsup><mo>=</mo><mn>0.77</mn></mrow></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msubsup><mi>ρ</mi><mrow><mi>H</mi><mi>W</mi></mrow><mn>2</mn></msubsup><mo>=</mo><mn>0.33</mn></mrow></semantics></math></inline-formula> were found under unstable atmospheric stability conditions, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msubsup><mi>ρ</mi><mrow><mn>2</mn><mi>D</mi></mrow><mn>2</mn></msubsup><mo>=</mo><mn>0.81</mn></mrow></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msubsup><mi>ρ</mi><mrow><mi>H</mi><mi>W</mi></mrow><mn>2</mn></msubsup><mo>=</mo><mn>0.07</mn></mrow></semantics></math></inline-formula> under stable conditions, which suggests the 2D algorithm as a prominent method for estimating the above-mentioned surface-layer parameters. |
| first_indexed | 2024-03-11T03:21:11Z |
| format | Article |
| id | doaj.art-28760fc4b77c4f1b84797313be8f14f1 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-11T03:21:11Z |
| publishDate | 2023-05-01 |
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| spelling | doaj.art-28760fc4b77c4f1b84797313be8f14f12023-11-18T03:08:22ZengMDPI AGRemote Sensing2072-42922023-05-011510266010.3390/rs15102660On the Retrieval of Surface-Layer Parameters from Lidar Wind-Profile MeasurementsMarcos Paulo Araújo da Silva0Andreu Salcedo-Bosch1Francesc Rocadenbosch2Alfredo Peña3CommSensLab-UPC, Department of Signal Theory and Communications, Universitat Politècnica de Catalunya (UPC), C/ Jordi Girona, 1-3, 08034 Barcelona, SpainCommSensLab-UPC, Department of Signal Theory and Communications, Universitat Politècnica de Catalunya (UPC), C/ Jordi Girona, 1-3, 08034 Barcelona, SpainCommSensLab-UPC, Department of Signal Theory and Communications, Universitat Politècnica de Catalunya (UPC), C/ Jordi Girona, 1-3, 08034 Barcelona, SpainDTU Wind and Energy Systems, Technical University of Denmark, Frederiksborgvej 399, 4000 Roskilde, DenmarkWe 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msubsup><mi>ρ</mi><mrow><mn>2</mn><mi>D</mi></mrow><mn>2</mn></msubsup><mo>=</mo><mn>0.77</mn></mrow></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msubsup><mi>ρ</mi><mrow><mi>H</mi><mi>W</mi></mrow><mn>2</mn></msubsup><mo>=</mo><mn>0.33</mn></mrow></semantics></math></inline-formula> were found under unstable atmospheric stability conditions, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msubsup><mi>ρ</mi><mrow><mn>2</mn><mi>D</mi></mrow><mn>2</mn></msubsup><mo>=</mo><mn>0.81</mn></mrow></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msubsup><mi>ρ</mi><mrow><mi>H</mi><mi>W</mi></mrow><mn>2</mn></msubsup><mo>=</mo><mn>0.07</mn></mrow></semantics></math></inline-formula> under stable conditions, which suggests the 2D algorithm as a prominent method for estimating the above-mentioned surface-layer parameters.https://www.mdpi.com/2072-4292/15/10/2660Obukhov lengthfriction velocityheat fluxwind energyfloating lidarDoppler wind lidar |
| spellingShingle | Marcos Paulo Araújo da Silva Andreu Salcedo-Bosch Francesc Rocadenbosch Alfredo Peña On the Retrieval of Surface-Layer Parameters from Lidar Wind-Profile Measurements Remote Sensing Obukhov length friction velocity heat flux wind energy floating lidar Doppler wind lidar |
| title | On the Retrieval of Surface-Layer Parameters from Lidar Wind-Profile Measurements |
| title_full | On the Retrieval of Surface-Layer Parameters from Lidar Wind-Profile Measurements |
| title_fullStr | On the Retrieval of Surface-Layer Parameters from Lidar Wind-Profile Measurements |
| title_full_unstemmed | On the Retrieval of Surface-Layer Parameters from Lidar Wind-Profile Measurements |
| title_short | On the Retrieval of Surface-Layer Parameters from Lidar Wind-Profile Measurements |
| title_sort | on the retrieval of surface layer parameters from lidar wind profile measurements |
| topic | Obukhov length friction velocity heat flux wind energy floating lidar Doppler wind lidar |
| url | https://www.mdpi.com/2072-4292/15/10/2660 |
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