Abstract. Carrying a laser Doppler instrument, the Aeolus satellite was launched in 2018, becoming the first mission for atmospheric wind profile measurements from space. Before utilizing the Aeolus winds for different applications, evaluating their data quality is essential. With the help of ground-based wind profiling radar measurements and the European Centre for Medium-Range Weather Forecasts (ECMWF) model equivalents, this study quantifies the error characteristics of Aeolus L2B (baseline-11) near-real-time horizontal line-of-sight winds across Australia during October 2020–March 2021 by using both inter-comparison and triple collocation analysis. The results of the inter-comparison analysis indicate that both Rayleigh-clear winds and Mie-cloudy winds are in good agreement with the ground-based radar measurements with overall absolute mean biases smaller than 0.7 m s−1 and correlation coefficients larger than or equal to 0.9. Moreover, assuming the radar measurements as the reference data set, Mie-cloudy winds are shown to be more precise than Rayleigh-clear winds with an overall random error of 4.14 and 5.81 m s−1, respectively. Similar results were also found from triple collocation analysis, with error standard deviations of 5.61 and 3.50 m s−1 for Rayleigh-clear winds and Mie-cloudy winds. In addition, the Mie channel is shown to be more capable of capturing the wind in the planetary boundary layer (< 1500 m). The findings of this study demonstrate the good performance of space-borne Doppler lidar for wind profiling and provide valuable information for data assimilation in numerical weather prediction.
Abstract. Carrying a laser Doppler instrument, the Aeolus satellite was launched in 2018, becoming the first mission for atmospheric wind profile measurements from space. Before utilizing the Aeolus winds for different applications, evaluating its data quality is essential. With the help of ground-based wind profiling radar measurements and the European Centre for Medium-Range Weather Forecasts (ECMWF) model equivalents, this study quantifies the error characteristics of Aeolus L2B (baseline-11) near real time horizontal line-of-sight winds across Australia by using both inter-comparison and triple collocation analysis. The results of the inter-comparison analysis indicate that both Rayleigh-clear winds and Mie-cloudy winds are in good agreement with the ground-based radar measurements with overall absolute mean biases smaller than 0.7 m s-1 and correlation coefficients larger than 0.9. Moreover, taking radar measurements as reference data set, Mie-cloudy winds are shown to be more precise than Rayleigh-clear winds with an overall random error of 5.81 m s-1 for Rayleigh-clear winds and 4.14 m s-1 for Mie-cloudy winds. Similar results were also found from triple collocation analysis, with error standard deviations of 5.61 m s-1 and 3.50 m s-1 for Rayleigh-clear winds and Mie-cloudy winds, respectively. In addition, the Mie channel is shown to be better capable of capturing the wind in the planetary boundary layer (< 1,500 m). The findings of this study demonstrate the good performance of space-borne Doppler lidar for wind profiling and provide valuable information for data assimilation in numerical weather prediction.
Abstract. To detect global wind profiles and improve numerical weather prediction (NWP), the European Space Agency (ESA) launched the Aeolus satellite carrying a space-borne Doppler Wind Lidar in 2018. After the successful launch, the European Centre for Medium-Range Weather Forecasts (ECMWF) performed the observing system experiments (OSEs) to evaluate the contribution of Aeolus data to NWP. This study aims to assess the impact of Aeolus wind assimilation in the ECMWF model on surface wind forecast over tropical ocean regions by taking buoy measurements for reference and over high latitude regions by taking weather station data for reference for the year 2020. The assessments were conducted through inter-comparison analysis and triple collocation analysis. The results show that with Aeolus data assimilation, the tropical sea surface wind forecast could be slightly improved at some forecast time steps. The random errors of u (zonal) and v (meridional) wind components from OSEs are within 1 m s-1 with respect to the model resolution. For the high latitude regions, Aeolus can reduce the wind forecast errors in the Northern Hemisphere with forecast extending, particularly during the first half-year of 2020 and during the winter months. For the Southern Hemisphere, the positive impact is mainly found for the u component at most forecast steps during June, July and August. Moreover, compared with the tropical ocean regions and the region > 60° N, the random error of OSEs for the region > 60° S increases significantly to 3 m s-1 with forecast extending. Overall, this study demonstrates the ability of Aeolus winds to improve surface wind forecast over tropical oceans and high latitude regions, which provides valuable information for practical applications with Aeolus data in the future.
No abstract
Abstract. The growing size of wind turbines leads to extremely high tip speeds when the blades are rotating. The blades are prone to leading edge erosion when raindrops hit the blades at such high speeds and blade damage will eventually affect the power production until repair or replacement of the blade is performed. Since these actions come with a high cost, it is relevant to estimate the blade lifetime for a given wind farm site prior to wind farm construction. Modelling tools for blade lifetime prediction require input time series of rainfall intensities and wind speeds in addition to a turbine-specific tip speed curve. In this paper, we investigate the suitability of satellite-based precipitation data from the Global Precipitation Measurement (GPM) Mission in the context of blade lifetime prediction. We first evaluate satellite-based rainfall intensities from the Integrated Multi-Satellite Retrievals for GPM (IMERG) final product against in situ observations at 18 weather stations located in Germany, Denmark, and Portugal. We then use the satellite and in situ rainfall intensities as input to a model for blade lifetime prediction together with the wind speeds measured at the stations. We find that blade lifetimes estimated with rainfall intensities from satellites and in situ observations are in good agreement despite the very different nature of the observation methods and the fact that IMERG products have a 30 minute temporal resolution whereas in situ stations deliver 10 minute accumulated rainfall intensities. Our results indicate that the wind speed has a large impact on the estimated blade lifetimes. Inland stations show significantly longer blade lifetimes than coastal stations, which are more exposed to high mean wind speeds. One station located in mountainous terrain shows large differences between rainfall intensities and blade lifetimes based on satellite and in situ observations. IMERG rainfall products are known to have a limited accuracy in mountainous terrain. Our analyses also confirm that IMERG overestimates light rainfall and underestimates heavy rainfall. Given that networks of in situ stations have large gaps over the oceans, there is a potential for utilizing rainfall products from satellites to estimate and map blade lifetimes. This is useful as more wind power is installed offshore including floating installations very far from the coast.
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