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T. Vlemmix
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2 records found
1
Master thesis
(2019)
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Niek Bossers, Tim Vlemmix, Pieternel Felicitas Levelt, j. Pepijn Veefkind, Sandra Verhagen
The Sentinel-5 Precursor satellite has a payload of the TROPOspheric Monitoring Instrument, TROPOMI.The satellite was launched in 2017 by ESA with the intended goal of measuring trace gases in the atmosphere.One of the products of TROPOMI is the Tropospheric NO2 column. This product is based on thespectral measurements to obtain the column abundance of NO2 in the troposphere. This product alsorelies on a-priori data and one of these a-priori datasets is the albedo dataset.The currently used dataset has a resolution of 0.5°x 0.5°, which corresponds to approximately 55 kmx 34 km at mid-latitudes. The TROPOMI pixel size is significantly smaller, 3.5 km x 7 km. Due to this large difference in resolution the discussion arises if this used dataset is sufficient for accurate results. This researchmakes a comparison between the current a-priori dataset and possible replacements.
This paper makes this comparison by calculating Air Mass Factors (AMFs) using the OMI LERalbedo climatology as a reference and the two alternative high resolution surface reflectance datasets,Sentinel-2 and Landsat-8. These surface reflectance datasets were regridded and averaged on the corresponding TROPOMI grid. The focus area of this paper is the Greater Rotterdam region in the Nether-lands.Before these AMF calculations were done, a comparison between Sentinel-2 and Landsat-8 surfacereflectance datasets is made. This is done both on their own high resolution and regridded onto theTROPOMI grid. Above water surfaces and land covered by vegetation a bias of approximately 0.01 was present between the two high resolution surface reflectance datasets. These differences are relatively small. The differences calculated for the datasets regridded to the TROPOMI grid were also relatively small, with a bias of 0.01 above the water and vegetation surfaces.
Two cases were studied during this research: the 21st of April 2018 and the 6th/7th of May 2018. Theresults show that significant improvements can be made by using a higher resolution surface reflectancedataset. A median bias of -10.4% (-15.6%) was calculated for the 21st of April for Sentinel-2 (Landsat-8)compared to the AMFs based on the OMI albedo dataset. For May this was -3.9% (-9.3%). Furthermorethis study showed extreme AMF-biases of 68.0% overestimation and 39.8% underestimation by the OMIalbedo dataset compared to Sentinel-2, where the overestimation was observed over the greenhouses inthe Westland region and the underestimation in the rural region to the East of the domain in April.For May the underestimation was mostly observed to the West (North Sea), indicating that over regionswith a low surface reflectance the atmospheric correction greatly influences the AMF. The comparisonbetween Landsat-8 and OMI showed similar results in the AMF differences.These findings are supported further by a recent Sentinel-5P validation study, which comparedground based observations to the TROPOMI observations. This project found an NO2 underestimationof approximately 20% for many different stations. This research suggest that, at least partly, this difference can be explained by the coarse resolution of the a-priori albedo dataset used.
...
This paper makes this comparison by calculating Air Mass Factors (AMFs) using the OMI LERalbedo climatology as a reference and the two alternative high resolution surface reflectance datasets,Sentinel-2 and Landsat-8. These surface reflectance datasets were regridded and averaged on the corresponding TROPOMI grid. The focus area of this paper is the Greater Rotterdam region in the Nether-lands.Before these AMF calculations were done, a comparison between Sentinel-2 and Landsat-8 surfacereflectance datasets is made. This is done both on their own high resolution and regridded onto theTROPOMI grid. Above water surfaces and land covered by vegetation a bias of approximately 0.01 was present between the two high resolution surface reflectance datasets. These differences are relatively small. The differences calculated for the datasets regridded to the TROPOMI grid were also relatively small, with a bias of 0.01 above the water and vegetation surfaces.
Two cases were studied during this research: the 21st of April 2018 and the 6th/7th of May 2018. Theresults show that significant improvements can be made by using a higher resolution surface reflectancedataset. A median bias of -10.4% (-15.6%) was calculated for the 21st of April for Sentinel-2 (Landsat-8)compared to the AMFs based on the OMI albedo dataset. For May this was -3.9% (-9.3%). Furthermorethis study showed extreme AMF-biases of 68.0% overestimation and 39.8% underestimation by the OMIalbedo dataset compared to Sentinel-2, where the overestimation was observed over the greenhouses inthe Westland region and the underestimation in the rural region to the East of the domain in April.For May the underestimation was mostly observed to the West (North Sea), indicating that over regionswith a low surface reflectance the atmospheric correction greatly influences the AMF. The comparisonbetween Landsat-8 and OMI showed similar results in the AMF differences.These findings are supported further by a recent Sentinel-5P validation study, which comparedground based observations to the TROPOMI observations. This project found an NO2 underestimationof approximately 20% for many different stations. This research suggest that, at least partly, this difference can be explained by the coarse resolution of the a-priori albedo dataset used.
...
The Sentinel-5 Precursor satellite has a payload of the TROPOspheric Monitoring Instrument, TROPOMI.The satellite was launched in 2017 by ESA with the intended goal of measuring trace gases in the atmosphere.One of the products of TROPOMI is the Tropospheric NO2 column. This product is based on thespectral measurements to obtain the column abundance of NO2 in the troposphere. This product alsorelies on a-priori data and one of these a-priori datasets is the albedo dataset.The currently used dataset has a resolution of 0.5°x 0.5°, which corresponds to approximately 55 kmx 34 km at mid-latitudes. The TROPOMI pixel size is significantly smaller, 3.5 km x 7 km. Due to this large difference in resolution the discussion arises if this used dataset is sufficient for accurate results. This researchmakes a comparison between the current a-priori dataset and possible replacements.
This paper makes this comparison by calculating Air Mass Factors (AMFs) using the OMI LERalbedo climatology as a reference and the two alternative high resolution surface reflectance datasets,Sentinel-2 and Landsat-8. These surface reflectance datasets were regridded and averaged on the corresponding TROPOMI grid. The focus area of this paper is the Greater Rotterdam region in the Nether-lands.Before these AMF calculations were done, a comparison between Sentinel-2 and Landsat-8 surfacereflectance datasets is made. This is done both on their own high resolution and regridded onto theTROPOMI grid. Above water surfaces and land covered by vegetation a bias of approximately 0.01 was present between the two high resolution surface reflectance datasets. These differences are relatively small. The differences calculated for the datasets regridded to the TROPOMI grid were also relatively small, with a bias of 0.01 above the water and vegetation surfaces.
Two cases were studied during this research: the 21st of April 2018 and the 6th/7th of May 2018. Theresults show that significant improvements can be made by using a higher resolution surface reflectancedataset. A median bias of -10.4% (-15.6%) was calculated for the 21st of April for Sentinel-2 (Landsat-8)compared to the AMFs based on the OMI albedo dataset. For May this was -3.9% (-9.3%). Furthermorethis study showed extreme AMF-biases of 68.0% overestimation and 39.8% underestimation by the OMIalbedo dataset compared to Sentinel-2, where the overestimation was observed over the greenhouses inthe Westland region and the underestimation in the rural region to the East of the domain in April.For May the underestimation was mostly observed to the West (North Sea), indicating that over regionswith a low surface reflectance the atmospheric correction greatly influences the AMF. The comparisonbetween Landsat-8 and OMI showed similar results in the AMF differences.These findings are supported further by a recent Sentinel-5P validation study, which comparedground based observations to the TROPOMI observations. This project found an NO2 underestimationof approximately 20% for many different stations. This research suggest that, at least partly, this difference can be explained by the coarse resolution of the a-priori albedo dataset used.
This paper makes this comparison by calculating Air Mass Factors (AMFs) using the OMI LERalbedo climatology as a reference and the two alternative high resolution surface reflectance datasets,Sentinel-2 and Landsat-8. These surface reflectance datasets were regridded and averaged on the corresponding TROPOMI grid. The focus area of this paper is the Greater Rotterdam region in the Nether-lands.Before these AMF calculations were done, a comparison between Sentinel-2 and Landsat-8 surfacereflectance datasets is made. This is done both on their own high resolution and regridded onto theTROPOMI grid. Above water surfaces and land covered by vegetation a bias of approximately 0.01 was present between the two high resolution surface reflectance datasets. These differences are relatively small. The differences calculated for the datasets regridded to the TROPOMI grid were also relatively small, with a bias of 0.01 above the water and vegetation surfaces.
Two cases were studied during this research: the 21st of April 2018 and the 6th/7th of May 2018. Theresults show that significant improvements can be made by using a higher resolution surface reflectancedataset. A median bias of -10.4% (-15.6%) was calculated for the 21st of April for Sentinel-2 (Landsat-8)compared to the AMFs based on the OMI albedo dataset. For May this was -3.9% (-9.3%). Furthermorethis study showed extreme AMF-biases of 68.0% overestimation and 39.8% underestimation by the OMIalbedo dataset compared to Sentinel-2, where the overestimation was observed over the greenhouses inthe Westland region and the underestimation in the rural region to the East of the domain in April.For May the underestimation was mostly observed to the West (North Sea), indicating that over regionswith a low surface reflectance the atmospheric correction greatly influences the AMF. The comparisonbetween Landsat-8 and OMI showed similar results in the AMF differences.These findings are supported further by a recent Sentinel-5P validation study, which comparedground based observations to the TROPOMI observations. This project found an NO2 underestimationof approximately 20% for many different stations. This research suggest that, at least partly, this difference can be explained by the coarse resolution of the a-priori albedo dataset used.
Anthropogenic nitrogen dioxide (NO₂) in the troposphere is mainly produced by combustion engines in traffic, industry and energy production, and continues to affect air quality, human health and environment. Daily global measurements of tropospheric NO₂ columns are obtained by satellites with increasing spatial resolution and signal-to-noise levels, to improve monitoring of emission sources and air quality forecasting. The recently launched TROPOMI instrument on-board ESA’s Sentinel-5 Precursor satellite measures tropospheric NO₂ with a spatial resolution of 7.1 km by 3.6 km. During its commissioning phase, the instrument was temporarily operated in ’zoom-mode’ to measure at a resolution of 2.4 km by 1.8 km. This research presents the processed results from this unique data-set, which allows mapping NO₂ pollution sources from space with unprecedented detail. Comparison to measurements at operational resolution shows the improvement in spatial information content, at the cost of increased noise and uncertainty. The benefits and possibilities of measuring tropospheric NO₂ at high resolution are explored with several case studies. Comparisons with chemical transfer model forecasts show the improved ability of these measurements to capture local NO₂ enhancements and possibly improve emission inventories. The found correlations with co-located space-borne CO₂ column observations and the performance of a plume detection algorithm applied to the data-set provide additional support for simultaneous high resolution measurements of co-emitted CO₂ and NO₂, planned for future satellites to improve CO₂ emission monitoring. Finally, test retrievals with the zoom-mode data, using experimental high resolution surface reflectivity and NO₂ profile shape input, demonstrate the potential impact of high resolution a priori databases on the retrieval performance.
...
Anthropogenic nitrogen dioxide (NO₂) in the troposphere is mainly produced by combustion engines in traffic, industry and energy production, and continues to affect air quality, human health and environment. Daily global measurements of tropospheric NO₂ columns are obtained by satellites with increasing spatial resolution and signal-to-noise levels, to improve monitoring of emission sources and air quality forecasting. The recently launched TROPOMI instrument on-board ESA’s Sentinel-5 Precursor satellite measures tropospheric NO₂ with a spatial resolution of 7.1 km by 3.6 km. During its commissioning phase, the instrument was temporarily operated in ’zoom-mode’ to measure at a resolution of 2.4 km by 1.8 km. This research presents the processed results from this unique data-set, which allows mapping NO₂ pollution sources from space with unprecedented detail. Comparison to measurements at operational resolution shows the improvement in spatial information content, at the cost of increased noise and uncertainty. The benefits and possibilities of measuring tropospheric NO₂ at high resolution are explored with several case studies. Comparisons with chemical transfer model forecasts show the improved ability of these measurements to capture local NO₂ enhancements and possibly improve emission inventories. The found correlations with co-located space-borne CO₂ column observations and the performance of a plume detection algorithm applied to the data-set provide additional support for simultaneous high resolution measurements of co-emitted CO₂ and NO₂, planned for future satellites to improve CO₂ emission monitoring. Finally, test retrievals with the zoom-mode data, using experimental high resolution surface reflectivity and NO₂ profile shape input, demonstrate the potential impact of high resolution a priori databases on the retrieval performance.