The Contribution of Aeolus Wind Observations to Global Sea Surface Wind Forecast
In August 2018, the European Space Agency (ESA) launched the Aeolus satellite equipped with a Direct Detection Doppler Wind Lidar to characterise global wind profiles and improve numerical weather prediction (NWP).
Following the successful launch and operation, to evaluate the value of Aeolus observations to NWP, the observing system experiments (OSEs) with and without Aeolus data assimilation were performed by many meteorological institutes, such as the European Centre for Medium-Range Weather Forecasts (ECMWF), Météo-France, National Oceanic and Atmospheric Administration, Environment and Climate Change Canada, etc. The existing studies show that the tropical and polar upper troposphere and lower stratosphere are the regions where Aeolus has the most favourable effects on wind predictions. However, much of the research done so far has not treated sea surface wind forecasts in much detail. Given this, the main purpose of this study is to assess the effects of Aeolus wind assimilation on sea surface wind forecast geographically by comparing with Advanced Scatterometer (ASCAT) winds.
The assessment is based on the short-term forecasts from ECMWF OSEs, which include a control experiment (no Aeolus) and an experiment with the assimilation of the 2nd reprocessed Aeolus L2B Rayleigh-clear and Mie-cloudy winds. The study period is from July 2019 to June 2020. To achieve the goal, we mainly quantified the normalised changes in root mean square difference of vector winds and the differences in biases of wind speed geographically. By segregating different climate regions on a global map, we may infer in which cases Aeolus brings improvement and in which cases deteriorates the forecast. Additionally, we intend to investigate the relationship between model errors and error reductions after Aeolus wind assimilation.
The results of this study not only display which ocean regions benefit more from Aeolus wind observations but also have the potential to provide valuable information for understanding and then avoiding the detrimental impact cases.
Acknowledgement: This study is a part of PhD project Aeolus satellite lidar for wind mapping, which is a sub-project of the LIdar Knowledge Europe Innovative Training Network Marie Skłodowska-Curie Actions funded by European Union Horizon 2020 (Grant number: 858358). Our appreciation goes to ECMWF for providing the OSE data for analysis.
