Assessing Aerosol Retrieval Performance for Data Assimilation using MODIS and VIIRS Aerosol Products
Abstract
The launch of VIIRS on S-NPP and NOAA 20 combined with the continued advancement in aerosol algorithms provides new opportunities for the aerosol data assimilation (DA) community. However, with the increase in observational capability comes new challenges for assimilation with each retrieval and platform having different error characteristics and detection sensitivity. To investigate the performance of these new observations for DA, a Level 3 (L3) product has been developed, using a consistent aggregation methodology, for three years of observations (2016-2018). This new dataset includes, aerosol products from the Moderate Resolutions Imaging Spectroradiometer (MODIS), Visible Infrared Imaging Radiometer Suite (VIIRS), and AErosol RObotic NETwork (AERONET). For MODIS, the L3 product includes the Deep Blue, Dark Target, and Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithms. For VIIRS, the recently released NASA Deep Blue algorithm is used. Using this new dataset, the relative performance of the algorithms for both land and ocean are investigated with a focus on severe event detection and evaluation with AERONET. Regional performance differences between platforms and algorithms will be investigated including pollution events in India and Southeast Asia and severe dust events in the desert regions in Africa and Northern Asia.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2019
- Bibcode:
- 2019AGUFM.A51F..04G
- Keywords:
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- 0305 Aerosols and particles;
- ATMOSPHERIC COMPOSITION AND STRUCTURE;
- 3360 Remote sensing;
- ATMOSPHERIC PROCESSES;
- 4301 Atmospheric;
- NATURAL HAZARDS;
- 4337 Remote sensing and disasters;
- NATURAL HAZARDS