Evaluating the use of Aeolus satellite observations in the regional numerical weather prediction (NWP) model Harmonie–Arome

Hagelin, Susanna; Azad, Roohollah; Lindskog, Magnus; Schyberg, Harald; Körnich, Heiner

doi:10.5194/amt-14-5925-2021

Cited by 11 publications

(9 citation statements)

References 27 publications

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“…Comparing Aeolus winds against different observation datasets and the NWP short-range forecasts show that the Mie-cloudy winds are better than the Rayleigh-clear winds at different vertical levels. The biases in the Aeolus winds are nearly the same irrespective of the reference dataset selected for validation similar to those reported by Martin et al (2021), Baars et al (2020) and Hagelin et al (2021).…”

Section: Indian Summer Monsoon Circulation Featuressupporting

confidence: 79%

Assessing the quality of novel Aeolus winds for NWP applications at NCMRWF

Rani

Jangid

Kumar

et al. 2022

Quart J Royal Meteoro Soc

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This study illustrates the validation of Aeolus Horizontal Line-Of-Sight (HLOS) winds, both Rayleigh-clear and Mie-cloudy, using in situ satellite wind (Atmospheric Motion Vectors, AMVs) observations, and NWP equivalents for three months, June-August 2020, covering the Indian summer monsoon season. Estimated errors in the Mie-cloudy (Rayleigh-clear) winds are clustered around 0.5-4 m⋅s −1 (3-8 m⋅s −1 ), and the differences between Aeolus Mie-cloudy (Rayleigh-clear) and sonde winds are within ±5 m⋅s −1 (± 8 m⋅s −1 ), but the systematic error is close to zero over the Northern Hemisphere where there are more sonde reports. Validation shows the quality of Mie-cloudy winds is better than Rayleigh-clear winds. Though the comparison against the observations like sonde (radiosonde and pilot balloons together) and aircraft indicate the quality of the Aeolus winds, their sparse spatial and temporal coverage limits the validation. Validation of Aeolus winds against AMVs provides similar results but with a better and nearly complete picture of the quality and quantity, with more information over the data-sparse and remote regions. Statistical scores suggest the characteristics of the Aeolus winds at different vertical levels and geographical regions remain the same irrespective of the validation reference datasets. The Indian summer monsoon features like Low-Level Jet (LLJ) and Tropical Easterly Jet (TEJ) are well represented in the Aeolus winds. This study also investigated the impact of the Aeolus HLOS winds over the Indian region through the collocated radiosonde and ALADIN wind profile assimilation experiments. Observing System Experiments (OSEs) suggest assimilation of Aeolus winds produced marginal improvement in the simulation of north Indian Ocean cyclones.

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Section: Indian Summer Monsoon Circulation Featuressupporting

confidence: 79%

Assessing the quality of novel Aeolus winds for NWP applications at NCMRWF

Rani

Jangid

Kumar

et al. 2022

Quart J Royal Meteoro Soc

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“…Aeolus measures both Rayleigh (i.e., molecular) and Mie (e.g., clouds and aerosols) backscatter to derive Horizontal Line-of-Sight (HLOS) wind profiles throughout the troposphere and lower stratosphere (Straume et al, 2020). The Aeolus HLOS Level-2B (L2B) winds have been evaluated and assimilated in operational data assimilation systems at NWP centers worldwide and have demonstrated positive impact on global weather forecasts (Cress, 2020; but to date only neutral impacts have been reported for regional NWP (e.g., Hagelin et al, 2021). As part of the Aeolus calibration and validation effort, as well as to support planning of the NOAA next-generation satellite architecture, the value of Aeolus observations on NOAA's mission to understand and predict changes in climate and weather has been investigated.…”

mentioning

confidence: 99%

Optimization and impact assessment of Aeolus HLOS wind assimilation in NOAA's global forecast system

Garrett

Liu

Ide

et al. 2022

Quart J Royal Meteoro Soc

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The European Space Agency Aeolus mission launched the first‐of‐its‐kind space‐borne Doppler wind lidar in August 2018. The Aeolus Level‐2B (L2B) Horizontal Line‐of‐Sight (HLOS) wind observations are integrated into the NOAA Finite‐Volume Cubed‐Sphere Global Forecast System (FV3GFS). Components of the data assimilation system are optimized to increase the forecast impact from these Aeolus observations. Three observing‐system experiments (OSEs) are performed using the Aeolus L2B HLOS winds for the period of August 2–September 16, 2019: a baseline experiment assimilating all observations that are operationally assimilated in NOAA's FV3GFS but without Aeolus; an experiment adding the Aeolus L2B HLOS winds on top of the baseline configuration; and an experiment adding the Aeolus L2B HLOS winds on top of the baseline but also including a total least‐squares (TLS) regression bias correction applied to the HLOS winds. The variances of the Aeolus HLOS wind random errors (i.e., observation errors) are estimated using the Hollingsworth–Lonnberg (HL) method. Results from both OSEs demonstrate positive impact of Aeolus L2B HLOS winds on the NOAA global forecast. The largest impact is seen in the tropical upper troposphere and lower stratosphere where the Day 1–3 wind vector forecast root‐mean‐square error (RMSE) is reduced by up to 4%. Additionally, the assimilation of Aeolus impacts the steering currents ambient to tropical cyclones, resulting in a 15% reduction in track forecast error in the Eastern Pacific basin Day 2–5 forecasts, and a 5% and 20% reduction in track forecast error in the Atlantic basin at Day 2 and Day 5, respectively. In most cases, the additional TLS bias correction increases the positive impact of Aeolus data assimilation in the NOAA global numerical weather prediction (NWP) system when compared to the assimilation of Aeolus without bias correction.

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“…Two categories are considered that is, HLOS winds retrieved from molecular (Rayleigh) scattering in clear air conditions, denoted Rayleigh‐clear, and from Mie scattering in non‐clear (aerosol and/or cloud) conditions, denoted Mie‐cloudy. These two categories are in general of good quality and therefore used in global and mesoscale assimilation (Hagelin et al., 2021; Rennie et al., 2021). HLOS wind retrieval is a complex data processing chain including processing from the raw measured data to Level‐1B (Reitebuch et al., 2018) and next to Level‐2B (L2B) data (Tan et al., 2008).…”

Section: Methodsmentioning

confidence: 99%

Assimilation of Aeolus Rayleigh‐Clear Winds Using a Footprint Operator in AROME‐Arctic Mesoscale Model

Mile

Azad

Marseille

2022

Geophysical Research Letters

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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 SummaryThe 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.

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Evaluating the use of Aeolus satellite observations in the regional numerical weather prediction (NWP) model Harmonie–Arome

Cited by 11 publications

References 27 publications

Assessing the quality of novel Aeolus winds for NWP applications at NCMRWF

Assessing the quality of novel Aeolus winds for NWP applications at NCMRWF

Optimization and impact assessment of Aeolus HLOS wind assimilation in NOAA's global forecast system

Assimilation of Aeolus Rayleigh‐Clear Winds Using a Footprint Operator in AROME‐Arctic Mesoscale Model

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