Assimilation of Aeolus Rayleigh‐Clear Winds Using a Footprint Operator in AROME‐Arctic Mesoscale Model. Issue 10 (24th May 2022)

• Record Type: Journal Article
• Authors: Mile, Máté; Azad, Roohollah; Marseille, Gert‐Jan
• Is Part Of: Geophysical research letters. Volume 49:Issue 10(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-05-24
• Subjects: footprint operator -- data assimilation -- high‐resolution; Geophysics -- Periodicals; Planets -- Periodicals; Lunar geology -- Periodicals; 550
• DOI: 10.1029/2021GL097615
• Languages: English
• ISSNs: 0094-8276

Abstract

Abstract: The wind profiles measured by the Aeolus satellite mission are unique and valuable wind products for numerical weather prediction. However, just like any other remote sensing measurements, it has a footprint extending over a number of model grid cells, especially in high‐resolution mesoscale models. The spatial representation of Aeolus Rayleigh‐clear observations has been assessed in a mesoscale limited‐area model called AROME‐Arctic by using a footprint observation operator. The footprint representation provides small, but consistent reduction in the standard deviation of background departures and an observing system experiment comparing the default and the footprint operator based on Aeolus data assimilation showed a small positive impact on AROME‐Arctic forecasts. Plain Language Summary: The Aeolus satellite mission provides unique and pioneering wind measurements in a global distribution that helps to unveil the wind structures of sparsely observed regions like the Arctic. These observations are also very useful for improving weather prediction, however, the use of Aeolus data is usually conservative and imperfect. In this study, the added value and advanced use of Aeolus wind observations are presented and discussed in an Arctic weather prediction model. Key Points: The footprint of Aeolus wind profiles may extend over many grid points of a high‐resolution numerical weather prediction model The footprint can be represented through the observation operator in data assimilation to improve the use of Aeolus data in the analysis The applied footprint operator helps to extend the observation impact in forecast lead time and to improve slightly the forecast quality