Aerosol, surface and cloud retrieval using passive remote sensing over the Arctic
The lack knowledge of aerosol optical properties is one of the sources of uncertainty in assessment and projections of the evolution of climate change and the phenomenon of Arctic Amplification. The spatial and temporal change of microphysical, chemical and optical properties of aerosols in the Arct...
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| Other Authors: | , |
| Format: | Doctoral or Postdoctoral Thesis |
| Language: | English |
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Universität Bremen
2021
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| Online Access: | https://media.suub.uni-bremen.de/handle/elib/5431 https://doi.org/10.26092/elib/1170 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib54315 |
| Summary: | The lack knowledge of aerosol optical properties is one of the sources of uncertainty in assessment and projections of the evolution of climate change and the phenomenon of Arctic Amplification. The spatial and temporal change of microphysical, chemical and optical properties of aerosols in the Arctic and the induced effects through direct and indirect radiative forcing of aerosols remain an open question. The cause of this gap in our understanding and therefore in the global aerosol optical thickness (AOT) maps is associated with the difficulty of retrieving aerosol properties over bright surfaces covered with snow and ice. Decoupling a strong surface signal from that of aerosol in the measured top-of-atmosphere reflectance is challenging and still hampered due to remaining unresolved issues in state-of-the-art algorithms. Despite the promising performance of previously-developed methods and ongoing research, there is no published long-term AOT product over polar regions (over land and ocean) to be used for climate studies. In this work, to extend our knowledge about the open issues and improve the existing algorithms, first we focus on the two major obstacles in the retrieval of AOT over snow/ice surfaces: i) cloud identification, and ii) surface properties; Second, we apply the outcome of studying the two mentioned prerequisites to improve the previously-developed aerosol retrieval algorithm called AEROSNOW and create a long-term data record for aerosol optical thickness over the Arctic circle. In the framework of this work, a new cloud identification algorithm called the AATSR/SLSTR Cloud Identification Algorithm (ASCIA) has been developed to screen cloudy scenes in observations of Advanced Along-Track Scanning Radiometer (AATSR) on-board ENVISAT as well as its successor Sea and Land Surface Temperature Radiometer (SLSTR) on-board Sentinel-3. The cloud detection results are verified by comparing them with available cloud products over the Arctic. Furthermore, the cloud product from ASCIA is validated using ... |
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