On the retrieval of surface-layer parameters from lidar wind-profile measurements

This article belongs to the Section Atmospheric Remote Sensing.

Bibliographic Details
Authors: Araújo da Silva, Marcos P., Salcedo-Bosch, Andreu, Rocadenbosch, Francesc, Peña, Alfredo
Format: article
Status:Published version
Publication Date:2023
Country:España
Institution:Consejo Superior de Investigaciones Científicas (CSIC)
Repository:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/348387
Online Access:http://hdl.handle.net/10261/348387
Access Level:Open access
Keyword:Obukhov length
Friction velocity
Heat flux
Wind energy
Floating lidar
Doppler wind lidar
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spelling On the retrieval of surface-layer parameters from lidar wind-profile measurementsAraújo da Silva, Marcos P.Salcedo-Bosch, AndreuRocadenbosch, FrancescPeña, AlfredoObukhov lengthFriction velocityHeat fluxWind energyFloating lidarDoppler wind lidarThis article belongs to the Section Atmospheric Remote Sensing.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 22=0.77 and 2=0.33 were found under unstable atmospheric stability conditions, and 22=0.81 and 2=0.07 under stable conditions, which suggests the 2D algorithm as a prominent method for estimating the above-mentioned surface-layer parameters.This research is part of the project PID2021-126436OB-C21 funded by Ministerio de Ciencia e Investigación (MCIN)/Agencia Estatal de Investigación (AEI)/10.13039/501100011033 y FEDER “Una manera de hacer Europa”. 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 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 (GA-101008004) and H2020 ACTRIS-IMP (GA-871115).Peer reviewedMultidisciplinary Digital Publishing InstituteMinisterio de Ciencia, Innovación y Universidades (España)Agencia Estatal de Investigación (España)European CommissionGeneralitat de CatalunyaMinisterio de Ciencia e Innovación (España)202420242023info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/10261/348387reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2021-126436OB-C21info:eu-repo/grantAgreement/AEI//PRE2018-086054info:eu-repo/grantAgreement/EC/H2020/101008004info:eu-repo/grantAgreement/EC/H2020/871115The underlying dataset has been published as supplementary material of the article in the publisher platform at DOI 10.3390/rs15102660https://doi.org/10.3390/rs15102660Noinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/3483872026-05-22T06:33:51Z
dc.title.none.fl_str_mv On the retrieval of surface-layer parameters from lidar wind-profile measurements
title On the retrieval of surface-layer parameters from lidar wind-profile measurements
spellingShingle On the retrieval of surface-layer parameters from lidar wind-profile measurements
Araújo da Silva, Marcos P.
Obukhov length
Friction velocity
Heat flux
Wind energy
Floating lidar
Doppler wind lidar
title_short 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_sort On the retrieval of surface-layer parameters from lidar wind-profile measurements
dc.creator.none.fl_str_mv Araújo da Silva, Marcos P.
Salcedo-Bosch, Andreu
Rocadenbosch, Francesc
Peña, Alfredo
author Araújo da Silva, Marcos P.
author_facet Araújo da Silva, Marcos P.
Salcedo-Bosch, Andreu
Rocadenbosch, Francesc
Peña, Alfredo
author_role author
author2 Salcedo-Bosch, Andreu
Rocadenbosch, Francesc
Peña, Alfredo
author2_role author
author
author
dc.contributor.none.fl_str_mv Ministerio de Ciencia, Innovación y Universidades (España)
Agencia Estatal de Investigación (España)
European Commission
Generalitat de Catalunya
Ministerio de Ciencia e Innovación (España)
dc.subject.none.fl_str_mv Obukhov length
Friction velocity
Heat flux
Wind energy
Floating lidar
Doppler wind lidar
topic Obukhov length
Friction velocity
Heat flux
Wind energy
Floating lidar
Doppler wind lidar
description This article belongs to the Section Atmospheric Remote Sensing.
publishDate 2023
dc.date.none.fl_str_mv 2023
2024
2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/348387
url http://hdl.handle.net/10261/348387
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv #PLACEHOLDER_PARENT_METADATA_VALUE#
#PLACEHOLDER_PARENT_METADATA_VALUE#
#PLACEHOLDER_PARENT_METADATA_VALUE#
#PLACEHOLDER_PARENT_METADATA_VALUE#
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2021-126436OB-C21
info:eu-repo/grantAgreement/AEI//PRE2018-086054
info:eu-repo/grantAgreement/EC/H2020/101008004
info:eu-repo/grantAgreement/EC/H2020/871115
The underlying dataset has been published as supplementary material of the article in the publisher platform at DOI 10.3390/rs15102660
https://doi.org/10.3390/rs15102660
No
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute
publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.812429

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