NumericalWeather Predictions and Re-Analysis as Input for Lidar Inversions: Assessment of the Impact on Optical Products

Detalhes bibliográficos
Autor(a) principal: Wang, Yuanzu
Data de Publicação: 2022
Outros Autores: Amodeo, Aldo, O'connor, Ewan, Baars, Holger, Bortoli, Daniele, Hu, Qiaoyun, Sun, Dongsong, D'Amico, Giuseppe
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10174/32121
https://doi.org/10.3390/rs14102342
Resumo: The atmospheric molecular number density can be obtained from atmospheric temperature and pressure profiles and is a significant input parameter for the inversion of lidar measurements. When measurements of vertical profiles of temperature and pressure are not available, atmospheric models are typically considered a valid alternative option. This paper investigates the influence of different atmospheric models (forecast and reanalysis) on the retrieval of aerosol optical properties (extinction and backscatter coefficients) by applying Raman and elastic-only methods to lidar measurements, to assess their use in lidar data processing. In general, reanalyzes are more accurate than forecasts, but, typically, they are not delivered in time for allowing near-real-time lidar data analysis. However, near-real-time observation is crucial for real-time monitoring of the environment and meteorological studies. The forecast models used in the paper are provided by the Integrated Forecasting System operated by the European Centre for Medium-RangeWeather Forecasts (IFS_ECMWF) and the Global Data Assimilation System (GDAS), whereas the reanalysis model is obtained from the fifth-generation European Centre for Medium-RangeWeather Forecasts ReAnalysis v5 (ERA5). The lidar dataset consists of measurements collected from four European Aerosol Research Lidar Network (EARLINET) stations during two intensive measurement campaigns and includes more than 200 cases at wavelengths of 355 nm, 532 nm, and 1064 nm. We present and discuss the results and influence of the forecast and reanalysis models in terms of deviations of the derived aerosol optical properties. The results show that the mean relative deviation in molecular number density is always below 3%, while larger deviations are shown in the derived aerosol optical properties, and the size of the deviation depends on the retrieval method together with the different wavelengths. In general, the aerosol extinction coefficient retrieval is more dependent on the model used than the aerosol backscatter retrievals are. The larger influence on the extinction retrieval is mainly related to the deviation in the gradient of the temperature profile provided by forecast and reanalysis models rather than the absolute deviation of the molecular number density. We found that deviations in extinction were within 5%, with a probability of 83% at 355 nm and 60% at 532 nm. Moreover, for aerosol backscatter coefficient retrievals, different models can have a larger impact when the backscatter coefficient is retrieved with the elastic method than when the backscatter coefficient is calculated using the Raman method at both 355 nm and 532 nm. In addition, the atmospheric aerosol load can also influence the deviations in the aerosol extinction and backscatter coefficients, showing a larger impact under low aerosol loading scenarios.
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spelling NumericalWeather Predictions and Re-Analysis as Input for Lidar Inversions: Assessment of the Impact on Optical ProductsACTRISEARLINETAtmospheric modelslidaraerosolThe atmospheric molecular number density can be obtained from atmospheric temperature and pressure profiles and is a significant input parameter for the inversion of lidar measurements. When measurements of vertical profiles of temperature and pressure are not available, atmospheric models are typically considered a valid alternative option. This paper investigates the influence of different atmospheric models (forecast and reanalysis) on the retrieval of aerosol optical properties (extinction and backscatter coefficients) by applying Raman and elastic-only methods to lidar measurements, to assess their use in lidar data processing. In general, reanalyzes are more accurate than forecasts, but, typically, they are not delivered in time for allowing near-real-time lidar data analysis. However, near-real-time observation is crucial for real-time monitoring of the environment and meteorological studies. The forecast models used in the paper are provided by the Integrated Forecasting System operated by the European Centre for Medium-RangeWeather Forecasts (IFS_ECMWF) and the Global Data Assimilation System (GDAS), whereas the reanalysis model is obtained from the fifth-generation European Centre for Medium-RangeWeather Forecasts ReAnalysis v5 (ERA5). The lidar dataset consists of measurements collected from four European Aerosol Research Lidar Network (EARLINET) stations during two intensive measurement campaigns and includes more than 200 cases at wavelengths of 355 nm, 532 nm, and 1064 nm. We present and discuss the results and influence of the forecast and reanalysis models in terms of deviations of the derived aerosol optical properties. The results show that the mean relative deviation in molecular number density is always below 3%, while larger deviations are shown in the derived aerosol optical properties, and the size of the deviation depends on the retrieval method together with the different wavelengths. In general, the aerosol extinction coefficient retrieval is more dependent on the model used than the aerosol backscatter retrievals are. The larger influence on the extinction retrieval is mainly related to the deviation in the gradient of the temperature profile provided by forecast and reanalysis models rather than the absolute deviation of the molecular number density. We found that deviations in extinction were within 5%, with a probability of 83% at 355 nm and 60% at 532 nm. Moreover, for aerosol backscatter coefficient retrievals, different models can have a larger impact when the backscatter coefficient is retrieved with the elastic method than when the backscatter coefficient is calculated using the Raman method at both 355 nm and 532 nm. In addition, the atmospheric aerosol load can also influence the deviations in the aerosol extinction and backscatter coefficients, showing a larger impact under low aerosol loading scenarios.This work was supported by ACTRIS-PPP (preparatory phase) project funded from European Union’s Horizon 2020 Coordination and Support Action (grant agreement no. 739530), ACTRISIMP (implementation) project, funded in the frame of the H2020 program (grant agreement no. 871115), CAMS21b project, funded within the Framework Agreement ECMWF/COPERNICUS/2019/CAMS21b/CNR. D.B. is co-funded by national Portuguese funds through FCT—Fundação para a Ciência e Tecnologia, I.P., in the framework of the ICT project with the references UIDB/04683/2020 and UIDP/04683/2020, as well as through TOMAQAPA (PTDC/CTAMET/29678/2017) and CILIFO (0753_CILIFO_5_E) projectsMDPI2022-05-30T11:38:23Z2022-05-302022-05-12T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10174/32121http://hdl.handle.net/10174/32121https://doi.org/10.3390/rs14102342eng1. Wang, Y.; Amodeo, A.; O’Connor, E.J.; Baars, H.; Bortoli, D.; Hu, Q.; Sun, D.; D’Amico, G., Numerical Weather Predictions and Re-Analysis as Input for Lidar Inversions: Assessment of the Impact on Optical Products. Remote Sens. 2022, 14, 2342.https://www.mdpi.com/2072-4292/14/10/2342/pdfFISndndndnddb@uevora.ptndndnd390Wang, YuanzuAmodeo, AldoO'connor, EwanBaars, HolgerBortoli, DanieleHu, QiaoyunSun, DongsongD'Amico, Giuseppeinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-01-03T19:32:21Zoai:dspace.uevora.pt:10174/32121Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T01:21:08.317362Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv NumericalWeather Predictions and Re-Analysis as Input for Lidar Inversions: Assessment of the Impact on Optical Products
title NumericalWeather Predictions and Re-Analysis as Input for Lidar Inversions: Assessment of the Impact on Optical Products
spellingShingle NumericalWeather Predictions and Re-Analysis as Input for Lidar Inversions: Assessment of the Impact on Optical Products
Wang, Yuanzu
ACTRIS
EARLINET
Atmospheric models
lidar
aerosol
title_short NumericalWeather Predictions and Re-Analysis as Input for Lidar Inversions: Assessment of the Impact on Optical Products
title_full NumericalWeather Predictions and Re-Analysis as Input for Lidar Inversions: Assessment of the Impact on Optical Products
title_fullStr NumericalWeather Predictions and Re-Analysis as Input for Lidar Inversions: Assessment of the Impact on Optical Products
title_full_unstemmed NumericalWeather Predictions and Re-Analysis as Input for Lidar Inversions: Assessment of the Impact on Optical Products
title_sort NumericalWeather Predictions and Re-Analysis as Input for Lidar Inversions: Assessment of the Impact on Optical Products
author Wang, Yuanzu
author_facet Wang, Yuanzu
Amodeo, Aldo
O'connor, Ewan
Baars, Holger
Bortoli, Daniele
Hu, Qiaoyun
Sun, Dongsong
D'Amico, Giuseppe
author_role author
author2 Amodeo, Aldo
O'connor, Ewan
Baars, Holger
Bortoli, Daniele
Hu, Qiaoyun
Sun, Dongsong
D'Amico, Giuseppe
author2_role author
author
author
author
author
author
author
dc.contributor.author.fl_str_mv Wang, Yuanzu
Amodeo, Aldo
O'connor, Ewan
Baars, Holger
Bortoli, Daniele
Hu, Qiaoyun
Sun, Dongsong
D'Amico, Giuseppe
dc.subject.por.fl_str_mv ACTRIS
EARLINET
Atmospheric models
lidar
aerosol
topic ACTRIS
EARLINET
Atmospheric models
lidar
aerosol
description The atmospheric molecular number density can be obtained from atmospheric temperature and pressure profiles and is a significant input parameter for the inversion of lidar measurements. When measurements of vertical profiles of temperature and pressure are not available, atmospheric models are typically considered a valid alternative option. This paper investigates the influence of different atmospheric models (forecast and reanalysis) on the retrieval of aerosol optical properties (extinction and backscatter coefficients) by applying Raman and elastic-only methods to lidar measurements, to assess their use in lidar data processing. In general, reanalyzes are more accurate than forecasts, but, typically, they are not delivered in time for allowing near-real-time lidar data analysis. However, near-real-time observation is crucial for real-time monitoring of the environment and meteorological studies. The forecast models used in the paper are provided by the Integrated Forecasting System operated by the European Centre for Medium-RangeWeather Forecasts (IFS_ECMWF) and the Global Data Assimilation System (GDAS), whereas the reanalysis model is obtained from the fifth-generation European Centre for Medium-RangeWeather Forecasts ReAnalysis v5 (ERA5). The lidar dataset consists of measurements collected from four European Aerosol Research Lidar Network (EARLINET) stations during two intensive measurement campaigns and includes more than 200 cases at wavelengths of 355 nm, 532 nm, and 1064 nm. We present and discuss the results and influence of the forecast and reanalysis models in terms of deviations of the derived aerosol optical properties. The results show that the mean relative deviation in molecular number density is always below 3%, while larger deviations are shown in the derived aerosol optical properties, and the size of the deviation depends on the retrieval method together with the different wavelengths. In general, the aerosol extinction coefficient retrieval is more dependent on the model used than the aerosol backscatter retrievals are. The larger influence on the extinction retrieval is mainly related to the deviation in the gradient of the temperature profile provided by forecast and reanalysis models rather than the absolute deviation of the molecular number density. We found that deviations in extinction were within 5%, with a probability of 83% at 355 nm and 60% at 532 nm. Moreover, for aerosol backscatter coefficient retrievals, different models can have a larger impact when the backscatter coefficient is retrieved with the elastic method than when the backscatter coefficient is calculated using the Raman method at both 355 nm and 532 nm. In addition, the atmospheric aerosol load can also influence the deviations in the aerosol extinction and backscatter coefficients, showing a larger impact under low aerosol loading scenarios.
publishDate 2022
dc.date.none.fl_str_mv 2022-05-30T11:38:23Z
2022-05-30
2022-05-12T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10174/32121
http://hdl.handle.net/10174/32121
https://doi.org/10.3390/rs14102342
url http://hdl.handle.net/10174/32121
https://doi.org/10.3390/rs14102342
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 1. Wang, Y.; Amodeo, A.; O’Connor, E.J.; Baars, H.; Bortoli, D.; Hu, Q.; Sun, D.; D’Amico, G., Numerical Weather Predictions and Re-Analysis as Input for Lidar Inversions: Assessment of the Impact on Optical Products. Remote Sens. 2022, 14, 2342.
https://www.mdpi.com/2072-4292/14/10/2342/pdf
FIS
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