The impact of aerosol vertical distribution on aerosol optical depth retrieval using CALIPSO and MODIS data: Case study over dust and smoke regions
Global quantitative aerosol information has been derived from MODerate Resolution Imaging SpectroRadiometer (MODIS) observations for decades since early 2000 and widely used for air quality and climate change research. However, the operational MODIS Aerosol Optical Depth (AOD) products Collection 6...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Wu, Yerong [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2017 |
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| Rechteinformationen: |
Nutzungsrecht: © 2017. American Geophysical Union. All Rights Reserved. |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Journal of geophysical research / D - Washington, DC : Union, 1984, 122(2017), 16, Seite 8801-8815 |
|---|---|
| Übergeordnetes Werk: |
volume:122 ; year:2017 ; number:16 ; pages:8801-8815 |
| Links: |
|---|
| DOI / URN: |
10.1002/2016JD026355 |
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| Katalog-ID: |
OLC1997848090 |
|---|
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| 245 | 1 | 4 | |a The impact of aerosol vertical distribution on aerosol optical depth retrieval using CALIPSO and MODIS data: Case study over dust and smoke regions |
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| 520 | |a Global quantitative aerosol information has been derived from MODerate Resolution Imaging SpectroRadiometer (MODIS) observations for decades since early 2000 and widely used for air quality and climate change research. However, the operational MODIS Aerosol Optical Depth (AOD) products Collection 6 (C6) can still be biased, because of uncertainty in assumed aerosol optical properties and aerosol vertical distribution. This study investigates the impact of aerosol vertical distribution on the AOD retrieval. We developed a new algorithm by considering dynamic vertical profiles, which is an adaptation of MODIS C6 Dark Target (C6_DT) algorithm over land. The new algorithm makes use of the aerosol vertical profile extracted from Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) measurements to generate an accurate top of the atmosphere (TOA) reflectance for the AOD retrieval, where the profile is assumed to be a single layer and represented as a Gaussian function with the mean height as single variable. To test the impact, a comparison was made between MODIS DT and Aerosol Robotic Network (AERONET) AOD, over dust and smoke regions. The results show that the aerosol vertical distribution has a strong impact on the AOD retrieval. The assumed aerosol layers close to the ground can negatively bias the retrievals in C6_DT. Regarding the evaluated smoke and dust layers, the new algorithm can improve the retrieval by reducing the negative biases by 3–5%. The MODIS Collection 6 algorithm (C6) AOD retrieval is sensitive to aerosol layer height The layer height inferred from CALIPSO data is ingested into C6 to yield a better retrieval Elevated smoke or dust layers can negatively bias the retrievals by 3–5% in C6 | ||
| 540 | |a Nutzungsrecht: © 2017. American Geophysical Union. All Rights Reserved. | ||
| 650 | 4 | |a retrieval | |
| 650 | 4 | |a MODIS data | |
| 650 | 4 | |a CALIPSO data | |
| 650 | 4 | |a simulation | |
| 650 | 4 | |a aerosol optical depth | |
| 650 | 4 | |a aerosol vertical distribution | |
| 650 | 4 | |a Reflectance | |
| 650 | 4 | |a Case depth | |
| 650 | 4 | |a Quality | |
| 650 | 4 | |a Optical properties | |
| 650 | 4 | |a Climate change | |
| 650 | 4 | |a Dust clouds | |
| 650 | 4 | |a Climatic changes | |
| 650 | 4 | |a Algorithms | |
| 650 | 4 | |a Lidar | |
| 650 | 4 | |a Air quality | |
| 650 | 4 | |a Dust | |
| 650 | 4 | |a Optical analysis | |
| 650 | 4 | |a Yield | |
| 650 | 4 | |a Dark adaptation | |
| 650 | 4 | |a Case studies | |
| 650 | 4 | |a Airborne particulates | |
| 650 | 4 | |a Aerosol vertical distribution | |
| 650 | 4 | |a Distribution | |
| 650 | 4 | |a Elevated | |
| 650 | 4 | |a Vertical distribution | |
| 650 | 4 | |a Collection | |
| 650 | 4 | |a Atmospheric particulates | |
| 650 | 4 | |a Aerosols | |
| 650 | 4 | |a Adaptations | |
| 650 | 4 | |a Mathematical models | |
| 650 | 4 | |a Climate | |
| 650 | 4 | |a Height | |
| 650 | 4 | |a Regions | |
| 650 | 4 | |a Studies | |
| 650 | 4 | |a Meteorological satellites | |
| 650 | 4 | |a Satellites | |
| 650 | 4 | |a Imaging techniques | |
| 650 | 4 | |a Vertical profiles | |
| 650 | 4 | |a Smoke | |
| 650 | 4 | |a Aerosol layers | |
| 650 | 4 | |a Optical properties of aerosols | |
| 650 | 4 | |a Profiles | |
| 650 | 4 | |a Aerosol Robotic Network | |
| 650 | 4 | |a Layers | |
| 650 | 4 | |a Climate change research | |
| 650 | 4 | |a Retrieval | |
| 650 | 4 | |a Optical depth of aerosols | |
| 650 | 4 | |a Satellite observation | |
| 650 | 4 | |a Optical radar | |
| 650 | 4 | |a Bias | |
| 650 | 4 | |a MODIS | |
| 700 | 1 | |a Graaf, Martin |4 oth | |
| 700 | 1 | |a Menenti, Massimo |4 oth | |
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10.1002/2016JD026355 doi PQ20171125 (DE-627)OLC1997848090 (DE-599)GBVOLC1997848090 (PRQ)p1012-4c21b02215cc91eb054deaba2a753fe1c09d9230c547ade5c12d22a42dbaf6740 (KEY)0137985220170000122001608801impactofaerosolverticaldistributiononaerosoloptica DE-627 ger DE-627 rakwb eng 550 DNB Wu, Yerong verfasserin aut The impact of aerosol vertical distribution on aerosol optical depth retrieval using CALIPSO and MODIS data: Case study over dust and smoke regions 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Global quantitative aerosol information has been derived from MODerate Resolution Imaging SpectroRadiometer (MODIS) observations for decades since early 2000 and widely used for air quality and climate change research. However, the operational MODIS Aerosol Optical Depth (AOD) products Collection 6 (C6) can still be biased, because of uncertainty in assumed aerosol optical properties and aerosol vertical distribution. This study investigates the impact of aerosol vertical distribution on the AOD retrieval. We developed a new algorithm by considering dynamic vertical profiles, which is an adaptation of MODIS C6 Dark Target (C6_DT) algorithm over land. The new algorithm makes use of the aerosol vertical profile extracted from Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) measurements to generate an accurate top of the atmosphere (TOA) reflectance for the AOD retrieval, where the profile is assumed to be a single layer and represented as a Gaussian function with the mean height as single variable. To test the impact, a comparison was made between MODIS DT and Aerosol Robotic Network (AERONET) AOD, over dust and smoke regions. The results show that the aerosol vertical distribution has a strong impact on the AOD retrieval. The assumed aerosol layers close to the ground can negatively bias the retrievals in C6_DT. Regarding the evaluated smoke and dust layers, the new algorithm can improve the retrieval by reducing the negative biases by 3–5%. The MODIS Collection 6 algorithm (C6) AOD retrieval is sensitive to aerosol layer height The layer height inferred from CALIPSO data is ingested into C6 to yield a better retrieval Elevated smoke or dust layers can negatively bias the retrievals by 3–5% in C6 Nutzungsrecht: © 2017. American Geophysical Union. All Rights Reserved. retrieval MODIS data CALIPSO data simulation aerosol optical depth aerosol vertical distribution Reflectance Case depth Quality Optical properties Climate change Dust clouds Climatic changes Algorithms Lidar Air quality Dust Optical analysis Yield Dark adaptation Case studies Airborne particulates Aerosol vertical distribution Distribution Elevated Vertical distribution Collection Atmospheric particulates Aerosols Adaptations Mathematical models Climate Height Regions Studies Meteorological satellites Satellites Imaging techniques Vertical profiles Smoke Aerosol layers Optical properties of aerosols Profiles Aerosol Robotic Network Layers Climate change research Retrieval Optical depth of aerosols Satellite observation Optical radar Bias MODIS Graaf, Martin oth Menenti, Massimo oth Enthalten in Journal of geophysical research / D Washington, DC : Union, 1984 122(2017), 16, Seite 8801-8815 (DE-627)130444391 (DE-600)710256-2 (DE-576)015978818 2169-897X nnns volume:122 year:2017 number:16 pages:8801-8815 http://dx.doi.org/10.1002/2016JD026355 Volltext http://onlinelibrary.wiley.com/doi/10.1002/2016JD026355/abstract https://search.proquest.com/docview/1937821548 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PHY SSG-OLC-GEO SSG-OPC-GGO SSG-OPC-GEO GBV_ILN_62 GBV_ILN_154 AR 122 2017 16 8801-8815 |
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10.1002/2016JD026355 doi PQ20171125 (DE-627)OLC1997848090 (DE-599)GBVOLC1997848090 (PRQ)p1012-4c21b02215cc91eb054deaba2a753fe1c09d9230c547ade5c12d22a42dbaf6740 (KEY)0137985220170000122001608801impactofaerosolverticaldistributiononaerosoloptica DE-627 ger DE-627 rakwb eng 550 DNB Wu, Yerong verfasserin aut The impact of aerosol vertical distribution on aerosol optical depth retrieval using CALIPSO and MODIS data: Case study over dust and smoke regions 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Global quantitative aerosol information has been derived from MODerate Resolution Imaging SpectroRadiometer (MODIS) observations for decades since early 2000 and widely used for air quality and climate change research. However, the operational MODIS Aerosol Optical Depth (AOD) products Collection 6 (C6) can still be biased, because of uncertainty in assumed aerosol optical properties and aerosol vertical distribution. This study investigates the impact of aerosol vertical distribution on the AOD retrieval. We developed a new algorithm by considering dynamic vertical profiles, which is an adaptation of MODIS C6 Dark Target (C6_DT) algorithm over land. The new algorithm makes use of the aerosol vertical profile extracted from Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) measurements to generate an accurate top of the atmosphere (TOA) reflectance for the AOD retrieval, where the profile is assumed to be a single layer and represented as a Gaussian function with the mean height as single variable. To test the impact, a comparison was made between MODIS DT and Aerosol Robotic Network (AERONET) AOD, over dust and smoke regions. The results show that the aerosol vertical distribution has a strong impact on the AOD retrieval. The assumed aerosol layers close to the ground can negatively bias the retrievals in C6_DT. Regarding the evaluated smoke and dust layers, the new algorithm can improve the retrieval by reducing the negative biases by 3–5%. The MODIS Collection 6 algorithm (C6) AOD retrieval is sensitive to aerosol layer height The layer height inferred from CALIPSO data is ingested into C6 to yield a better retrieval Elevated smoke or dust layers can negatively bias the retrievals by 3–5% in C6 Nutzungsrecht: © 2017. American Geophysical Union. All Rights Reserved. retrieval MODIS data CALIPSO data simulation aerosol optical depth aerosol vertical distribution Reflectance Case depth Quality Optical properties Climate change Dust clouds Climatic changes Algorithms Lidar Air quality Dust Optical analysis Yield Dark adaptation Case studies Airborne particulates Aerosol vertical distribution Distribution Elevated Vertical distribution Collection Atmospheric particulates Aerosols Adaptations Mathematical models Climate Height Regions Studies Meteorological satellites Satellites Imaging techniques Vertical profiles Smoke Aerosol layers Optical properties of aerosols Profiles Aerosol Robotic Network Layers Climate change research Retrieval Optical depth of aerosols Satellite observation Optical radar Bias MODIS Graaf, Martin oth Menenti, Massimo oth Enthalten in Journal of geophysical research / D Washington, DC : Union, 1984 122(2017), 16, Seite 8801-8815 (DE-627)130444391 (DE-600)710256-2 (DE-576)015978818 2169-897X nnns volume:122 year:2017 number:16 pages:8801-8815 http://dx.doi.org/10.1002/2016JD026355 Volltext http://onlinelibrary.wiley.com/doi/10.1002/2016JD026355/abstract https://search.proquest.com/docview/1937821548 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PHY SSG-OLC-GEO SSG-OPC-GGO SSG-OPC-GEO GBV_ILN_62 GBV_ILN_154 AR 122 2017 16 8801-8815 |
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10.1002/2016JD026355 doi PQ20171125 (DE-627)OLC1997848090 (DE-599)GBVOLC1997848090 (PRQ)p1012-4c21b02215cc91eb054deaba2a753fe1c09d9230c547ade5c12d22a42dbaf6740 (KEY)0137985220170000122001608801impactofaerosolverticaldistributiononaerosoloptica DE-627 ger DE-627 rakwb eng 550 DNB Wu, Yerong verfasserin aut The impact of aerosol vertical distribution on aerosol optical depth retrieval using CALIPSO and MODIS data: Case study over dust and smoke regions 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Global quantitative aerosol information has been derived from MODerate Resolution Imaging SpectroRadiometer (MODIS) observations for decades since early 2000 and widely used for air quality and climate change research. However, the operational MODIS Aerosol Optical Depth (AOD) products Collection 6 (C6) can still be biased, because of uncertainty in assumed aerosol optical properties and aerosol vertical distribution. This study investigates the impact of aerosol vertical distribution on the AOD retrieval. We developed a new algorithm by considering dynamic vertical profiles, which is an adaptation of MODIS C6 Dark Target (C6_DT) algorithm over land. The new algorithm makes use of the aerosol vertical profile extracted from Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) measurements to generate an accurate top of the atmosphere (TOA) reflectance for the AOD retrieval, where the profile is assumed to be a single layer and represented as a Gaussian function with the mean height as single variable. To test the impact, a comparison was made between MODIS DT and Aerosol Robotic Network (AERONET) AOD, over dust and smoke regions. The results show that the aerosol vertical distribution has a strong impact on the AOD retrieval. The assumed aerosol layers close to the ground can negatively bias the retrievals in C6_DT. Regarding the evaluated smoke and dust layers, the new algorithm can improve the retrieval by reducing the negative biases by 3–5%. The MODIS Collection 6 algorithm (C6) AOD retrieval is sensitive to aerosol layer height The layer height inferred from CALIPSO data is ingested into C6 to yield a better retrieval Elevated smoke or dust layers can negatively bias the retrievals by 3–5% in C6 Nutzungsrecht: © 2017. American Geophysical Union. All Rights Reserved. retrieval MODIS data CALIPSO data simulation aerosol optical depth aerosol vertical distribution Reflectance Case depth Quality Optical properties Climate change Dust clouds Climatic changes Algorithms Lidar Air quality Dust Optical analysis Yield Dark adaptation Case studies Airborne particulates Aerosol vertical distribution Distribution Elevated Vertical distribution Collection Atmospheric particulates Aerosols Adaptations Mathematical models Climate Height Regions Studies Meteorological satellites Satellites Imaging techniques Vertical profiles Smoke Aerosol layers Optical properties of aerosols Profiles Aerosol Robotic Network Layers Climate change research Retrieval Optical depth of aerosols Satellite observation Optical radar Bias MODIS Graaf, Martin oth Menenti, Massimo oth Enthalten in Journal of geophysical research / D Washington, DC : Union, 1984 122(2017), 16, Seite 8801-8815 (DE-627)130444391 (DE-600)710256-2 (DE-576)015978818 2169-897X nnns volume:122 year:2017 number:16 pages:8801-8815 http://dx.doi.org/10.1002/2016JD026355 Volltext http://onlinelibrary.wiley.com/doi/10.1002/2016JD026355/abstract https://search.proquest.com/docview/1937821548 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PHY SSG-OLC-GEO SSG-OPC-GGO SSG-OPC-GEO GBV_ILN_62 GBV_ILN_154 AR 122 2017 16 8801-8815 |
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10.1002/2016JD026355 doi PQ20171125 (DE-627)OLC1997848090 (DE-599)GBVOLC1997848090 (PRQ)p1012-4c21b02215cc91eb054deaba2a753fe1c09d9230c547ade5c12d22a42dbaf6740 (KEY)0137985220170000122001608801impactofaerosolverticaldistributiononaerosoloptica DE-627 ger DE-627 rakwb eng 550 DNB Wu, Yerong verfasserin aut The impact of aerosol vertical distribution on aerosol optical depth retrieval using CALIPSO and MODIS data: Case study over dust and smoke regions 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Global quantitative aerosol information has been derived from MODerate Resolution Imaging SpectroRadiometer (MODIS) observations for decades since early 2000 and widely used for air quality and climate change research. However, the operational MODIS Aerosol Optical Depth (AOD) products Collection 6 (C6) can still be biased, because of uncertainty in assumed aerosol optical properties and aerosol vertical distribution. This study investigates the impact of aerosol vertical distribution on the AOD retrieval. We developed a new algorithm by considering dynamic vertical profiles, which is an adaptation of MODIS C6 Dark Target (C6_DT) algorithm over land. The new algorithm makes use of the aerosol vertical profile extracted from Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) measurements to generate an accurate top of the atmosphere (TOA) reflectance for the AOD retrieval, where the profile is assumed to be a single layer and represented as a Gaussian function with the mean height as single variable. To test the impact, a comparison was made between MODIS DT and Aerosol Robotic Network (AERONET) AOD, over dust and smoke regions. The results show that the aerosol vertical distribution has a strong impact on the AOD retrieval. The assumed aerosol layers close to the ground can negatively bias the retrievals in C6_DT. Regarding the evaluated smoke and dust layers, the new algorithm can improve the retrieval by reducing the negative biases by 3–5%. The MODIS Collection 6 algorithm (C6) AOD retrieval is sensitive to aerosol layer height The layer height inferred from CALIPSO data is ingested into C6 to yield a better retrieval Elevated smoke or dust layers can negatively bias the retrievals by 3–5% in C6 Nutzungsrecht: © 2017. American Geophysical Union. All Rights Reserved. retrieval MODIS data CALIPSO data simulation aerosol optical depth aerosol vertical distribution Reflectance Case depth Quality Optical properties Climate change Dust clouds Climatic changes Algorithms Lidar Air quality Dust Optical analysis Yield Dark adaptation Case studies Airborne particulates Aerosol vertical distribution Distribution Elevated Vertical distribution Collection Atmospheric particulates Aerosols Adaptations Mathematical models Climate Height Regions Studies Meteorological satellites Satellites Imaging techniques Vertical profiles Smoke Aerosol layers Optical properties of aerosols Profiles Aerosol Robotic Network Layers Climate change research Retrieval Optical depth of aerosols Satellite observation Optical radar Bias MODIS Graaf, Martin oth Menenti, Massimo oth Enthalten in Journal of geophysical research / D Washington, DC : Union, 1984 122(2017), 16, Seite 8801-8815 (DE-627)130444391 (DE-600)710256-2 (DE-576)015978818 2169-897X nnns volume:122 year:2017 number:16 pages:8801-8815 http://dx.doi.org/10.1002/2016JD026355 Volltext http://onlinelibrary.wiley.com/doi/10.1002/2016JD026355/abstract https://search.proquest.com/docview/1937821548 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PHY SSG-OLC-GEO SSG-OPC-GGO SSG-OPC-GEO GBV_ILN_62 GBV_ILN_154 AR 122 2017 16 8801-8815 |
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10.1002/2016JD026355 doi PQ20171125 (DE-627)OLC1997848090 (DE-599)GBVOLC1997848090 (PRQ)p1012-4c21b02215cc91eb054deaba2a753fe1c09d9230c547ade5c12d22a42dbaf6740 (KEY)0137985220170000122001608801impactofaerosolverticaldistributiononaerosoloptica DE-627 ger DE-627 rakwb eng 550 DNB Wu, Yerong verfasserin aut The impact of aerosol vertical distribution on aerosol optical depth retrieval using CALIPSO and MODIS data: Case study over dust and smoke regions 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Global quantitative aerosol information has been derived from MODerate Resolution Imaging SpectroRadiometer (MODIS) observations for decades since early 2000 and widely used for air quality and climate change research. However, the operational MODIS Aerosol Optical Depth (AOD) products Collection 6 (C6) can still be biased, because of uncertainty in assumed aerosol optical properties and aerosol vertical distribution. This study investigates the impact of aerosol vertical distribution on the AOD retrieval. We developed a new algorithm by considering dynamic vertical profiles, which is an adaptation of MODIS C6 Dark Target (C6_DT) algorithm over land. The new algorithm makes use of the aerosol vertical profile extracted from Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) measurements to generate an accurate top of the atmosphere (TOA) reflectance for the AOD retrieval, where the profile is assumed to be a single layer and represented as a Gaussian function with the mean height as single variable. To test the impact, a comparison was made between MODIS DT and Aerosol Robotic Network (AERONET) AOD, over dust and smoke regions. The results show that the aerosol vertical distribution has a strong impact on the AOD retrieval. The assumed aerosol layers close to the ground can negatively bias the retrievals in C6_DT. Regarding the evaluated smoke and dust layers, the new algorithm can improve the retrieval by reducing the negative biases by 3–5%. The MODIS Collection 6 algorithm (C6) AOD retrieval is sensitive to aerosol layer height The layer height inferred from CALIPSO data is ingested into C6 to yield a better retrieval Elevated smoke or dust layers can negatively bias the retrievals by 3–5% in C6 Nutzungsrecht: © 2017. American Geophysical Union. All Rights Reserved. retrieval MODIS data CALIPSO data simulation aerosol optical depth aerosol vertical distribution Reflectance Case depth Quality Optical properties Climate change Dust clouds Climatic changes Algorithms Lidar Air quality Dust Optical analysis Yield Dark adaptation Case studies Airborne particulates Aerosol vertical distribution Distribution Elevated Vertical distribution Collection Atmospheric particulates Aerosols Adaptations Mathematical models Climate Height Regions Studies Meteorological satellites Satellites Imaging techniques Vertical profiles Smoke Aerosol layers Optical properties of aerosols Profiles Aerosol Robotic Network Layers Climate change research Retrieval Optical depth of aerosols Satellite observation Optical radar Bias MODIS Graaf, Martin oth Menenti, Massimo oth Enthalten in Journal of geophysical research / D Washington, DC : Union, 1984 122(2017), 16, Seite 8801-8815 (DE-627)130444391 (DE-600)710256-2 (DE-576)015978818 2169-897X nnns volume:122 year:2017 number:16 pages:8801-8815 http://dx.doi.org/10.1002/2016JD026355 Volltext http://onlinelibrary.wiley.com/doi/10.1002/2016JD026355/abstract https://search.proquest.com/docview/1937821548 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-PHY SSG-OLC-GEO SSG-OPC-GGO SSG-OPC-GEO GBV_ILN_62 GBV_ILN_154 AR 122 2017 16 8801-8815 |
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550 DNB The impact of aerosol vertical distribution on aerosol optical depth retrieval using CALIPSO and MODIS data: Case study over dust and smoke regions retrieval MODIS data CALIPSO data simulation aerosol optical depth aerosol vertical distribution Reflectance Case depth Quality Optical properties Climate change Dust clouds Climatic changes Algorithms Lidar Air quality Dust Optical analysis Yield Dark adaptation Case studies Airborne particulates Aerosol vertical distribution Distribution Elevated Vertical distribution Collection Atmospheric particulates Aerosols Adaptations Mathematical models Climate Height Regions Studies Meteorological satellites Satellites Imaging techniques Vertical profiles Smoke Aerosol layers Optical properties of aerosols Profiles Aerosol Robotic Network Layers Climate change research Retrieval Optical depth of aerosols Satellite observation Optical radar Bias MODIS |
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impact of aerosol vertical distribution on aerosol optical depth retrieval using calipso and modis data: case study over dust and smoke regions |
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The impact of aerosol vertical distribution on aerosol optical depth retrieval using CALIPSO and MODIS data: Case study over dust and smoke regions |
| abstract |
Global quantitative aerosol information has been derived from MODerate Resolution Imaging SpectroRadiometer (MODIS) observations for decades since early 2000 and widely used for air quality and climate change research. However, the operational MODIS Aerosol Optical Depth (AOD) products Collection 6 (C6) can still be biased, because of uncertainty in assumed aerosol optical properties and aerosol vertical distribution. This study investigates the impact of aerosol vertical distribution on the AOD retrieval. We developed a new algorithm by considering dynamic vertical profiles, which is an adaptation of MODIS C6 Dark Target (C6_DT) algorithm over land. The new algorithm makes use of the aerosol vertical profile extracted from Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) measurements to generate an accurate top of the atmosphere (TOA) reflectance for the AOD retrieval, where the profile is assumed to be a single layer and represented as a Gaussian function with the mean height as single variable. To test the impact, a comparison was made between MODIS DT and Aerosol Robotic Network (AERONET) AOD, over dust and smoke regions. The results show that the aerosol vertical distribution has a strong impact on the AOD retrieval. The assumed aerosol layers close to the ground can negatively bias the retrievals in C6_DT. Regarding the evaluated smoke and dust layers, the new algorithm can improve the retrieval by reducing the negative biases by 3–5%. The MODIS Collection 6 algorithm (C6) AOD retrieval is sensitive to aerosol layer height The layer height inferred from CALIPSO data is ingested into C6 to yield a better retrieval Elevated smoke or dust layers can negatively bias the retrievals by 3–5% in C6 |
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Global quantitative aerosol information has been derived from MODerate Resolution Imaging SpectroRadiometer (MODIS) observations for decades since early 2000 and widely used for air quality and climate change research. However, the operational MODIS Aerosol Optical Depth (AOD) products Collection 6 (C6) can still be biased, because of uncertainty in assumed aerosol optical properties and aerosol vertical distribution. This study investigates the impact of aerosol vertical distribution on the AOD retrieval. We developed a new algorithm by considering dynamic vertical profiles, which is an adaptation of MODIS C6 Dark Target (C6_DT) algorithm over land. The new algorithm makes use of the aerosol vertical profile extracted from Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) measurements to generate an accurate top of the atmosphere (TOA) reflectance for the AOD retrieval, where the profile is assumed to be a single layer and represented as a Gaussian function with the mean height as single variable. To test the impact, a comparison was made between MODIS DT and Aerosol Robotic Network (AERONET) AOD, over dust and smoke regions. The results show that the aerosol vertical distribution has a strong impact on the AOD retrieval. The assumed aerosol layers close to the ground can negatively bias the retrievals in C6_DT. Regarding the evaluated smoke and dust layers, the new algorithm can improve the retrieval by reducing the negative biases by 3–5%. The MODIS Collection 6 algorithm (C6) AOD retrieval is sensitive to aerosol layer height The layer height inferred from CALIPSO data is ingested into C6 to yield a better retrieval Elevated smoke or dust layers can negatively bias the retrievals by 3–5% in C6 |
| abstract_unstemmed |
Global quantitative aerosol information has been derived from MODerate Resolution Imaging SpectroRadiometer (MODIS) observations for decades since early 2000 and widely used for air quality and climate change research. However, the operational MODIS Aerosol Optical Depth (AOD) products Collection 6 (C6) can still be biased, because of uncertainty in assumed aerosol optical properties and aerosol vertical distribution. This study investigates the impact of aerosol vertical distribution on the AOD retrieval. We developed a new algorithm by considering dynamic vertical profiles, which is an adaptation of MODIS C6 Dark Target (C6_DT) algorithm over land. The new algorithm makes use of the aerosol vertical profile extracted from Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) measurements to generate an accurate top of the atmosphere (TOA) reflectance for the AOD retrieval, where the profile is assumed to be a single layer and represented as a Gaussian function with the mean height as single variable. To test the impact, a comparison was made between MODIS DT and Aerosol Robotic Network (AERONET) AOD, over dust and smoke regions. The results show that the aerosol vertical distribution has a strong impact on the AOD retrieval. The assumed aerosol layers close to the ground can negatively bias the retrievals in C6_DT. Regarding the evaluated smoke and dust layers, the new algorithm can improve the retrieval by reducing the negative biases by 3–5%. The MODIS Collection 6 algorithm (C6) AOD retrieval is sensitive to aerosol layer height The layer height inferred from CALIPSO data is ingested into C6 to yield a better retrieval Elevated smoke or dust layers can negatively bias the retrievals by 3–5% in C6 |
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The impact of aerosol vertical distribution on aerosol optical depth retrieval using CALIPSO and MODIS data: Case study over dust and smoke regions |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a2200265 4500</leader><controlfield tag="001">OLC1997848090</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230715075602.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">171125s2017 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1002/2016JD026355</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">PQ20171125</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC1997848090</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)GBVOLC1997848090</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(PRQ)p1012-4c21b02215cc91eb054deaba2a753fe1c09d9230c547ade5c12d22a42dbaf6740</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(KEY)0137985220170000122001608801impactofaerosolverticaldistributiononaerosoloptica</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">550</subfield><subfield code="q">DNB</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Wu, Yerong</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="4"><subfield code="a">The impact of aerosol vertical distribution on aerosol optical depth retrieval using CALIPSO and MODIS data: Case study over dust and smoke regions</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2017</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Global quantitative aerosol information has been derived from MODerate Resolution Imaging SpectroRadiometer (MODIS) observations for decades since early 2000 and widely used for air quality and climate change research. However, the operational MODIS Aerosol Optical Depth (AOD) products Collection 6 (C6) can still be biased, because of uncertainty in assumed aerosol optical properties and aerosol vertical distribution. This study investigates the impact of aerosol vertical distribution on the AOD retrieval. We developed a new algorithm by considering dynamic vertical profiles, which is an adaptation of MODIS C6 Dark Target (C6_DT) algorithm over land. The new algorithm makes use of the aerosol vertical profile extracted from Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) measurements to generate an accurate top of the atmosphere (TOA) reflectance for the AOD retrieval, where the profile is assumed to be a single layer and represented as a Gaussian function with the mean height as single variable. To test the impact, a comparison was made between MODIS DT and Aerosol Robotic Network (AERONET) AOD, over dust and smoke regions. The results show that the aerosol vertical distribution has a strong impact on the AOD retrieval. The assumed aerosol layers close to the ground can negatively bias the retrievals in C6_DT. Regarding the evaluated smoke and dust layers, the new algorithm can improve the retrieval by reducing the negative biases by 3–5%. The MODIS Collection 6 algorithm (C6) AOD retrieval is sensitive to aerosol layer height The layer height inferred from CALIPSO data is ingested into C6 to yield a better retrieval Elevated smoke or dust layers can negatively bias the retrievals by 3–5% in C6</subfield></datafield><datafield tag="540" ind1=" " ind2=" "><subfield code="a">Nutzungsrecht: © 2017. American Geophysical Union. All Rights Reserved.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">retrieval</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">MODIS data</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">CALIPSO data</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">simulation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">aerosol optical depth</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">aerosol vertical distribution</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Reflectance</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Case depth</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Quality</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Optical properties</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Climate change</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Dust clouds</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Climatic changes</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Algorithms</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Lidar</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Air quality</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Dust</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Optical analysis</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Yield</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Dark adaptation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Case 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models</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Climate</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Height</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Regions</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Studies</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Meteorological satellites</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Satellites</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Imaging techniques</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Vertical profiles</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Smoke</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Aerosol layers</subfield></datafield><datafield tag="650" ind1=" " 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