The prospects for increasing the horizontal resolution of the Aeolus horizontal line‐of‐sight wind profiles

Šavli, Matic; Kloe, Jos de; Marseille, Gert‐Jan; Rennie, Michael; Žagar, Nedjeljka; Wedi, Nils

doi:10.1002/qj.3634

Cited by 9 publications

(6 citation statements)

References 37 publications

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“…Decreasing the horizontal averaging length of the Mie‐cloudy winds from typically 50 to 12 km on 5 March 2019 gave a factor 2–3 increase in the number of winds and only led to a small increase (∼5%) in standard deviation of O–B, which is because the cloud backscatter is sufficiently strong to not be a limiting factor in the wind errors (this agrees with pre‐launch predictions in Šavli et al . (2019)). Figure 3 shows an increase in random error occurred on 16 May 2020 associated with a processing baseline update involving the application of a new Mie calibration file to decrease systematic errors.…”

Section: Assessment Of Hlos Wind Retrieval Error Statisticsmentioning

confidence: 99%

The impact of Aeolus wind retrievals on ECMWF global weather forecasts

Rennie

Isaksen

Weiler

et al. 2021

Quart J Royal Meteoro Soc

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107

132

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Aeolus is the world's first spaceborne Doppler Wind Lidar, providing profiles of horizontal line-of-sight (HLOS) wind retrievals. Numerical weather prediction (NWP) impact and error statistics of Aeolus Level-2B (L2B) wind statistics have been assessed using the European Centre for Medium-range Weather Forecasts (ECMWF) global data assimilation system. Random and systematic error estimates were derived from observation minus background departure statistics. The HLOS wind random error standard deviation is estimated to be in the range 4.0-7.0 m⋅s −1 for the Rayleigh-clear and 2.8-3.6 m⋅s −1 for the Mie-cloudy, depending on atmospheric signal levels which in turn depend on instrument performance, atmospheric backscatter properties and the processing algorithms.Complex systematic HLOS wind error variations on time-scales less than one orbit were identified, most strongly affecting the Rayleigh-clear winds. NWP departures and instrument housekeeping data confirmed that it is caused by temperature gradients across the primary mirror. A successful bias correction scheme was implemented in the operational processing chain in April 2020.In Observing System Experiments (OSEs), Aeolus provides statistically significant improvement in short-range forecasts as verified by observations sensitive to temperature, wind and humidity. Longer forecast range verification shows positive impact that is strongest at the day two to three forecast range: ∼2% improvement in root-mean-square error for vector wind and temperature in the tropical upper troposphere and lower stratosphere, and polar troposphere.Positive impact up to 9 days is found in the tropical lower stratosphere. Both Rayleigh-clear and Mie-cloudy winds provide positive impact, but the Rayleigh accounts for most tropical impact. The Forecast Sensitivity Observation Impact (FSOI) metric is available since 9 January 2020, when Aeolus was operationally assimilated, which confirms Aeolus is a useful contribution to the global observing system, with the Rayleigh-clear and Mie-cloudy winds providing similar overall short-range impact in 2020.

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Section: Assessment Of Hlos Wind Retrieval Error Statisticsmentioning

confidence: 99%

The impact of Aeolus wind retrievals on ECMWF global weather forecasts

Rennie

Isaksen

Weiler

et al. 2021

Quart J Royal Meteoro Soc

Self Cite

107

132

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“…The resulting HLOS wind is therefore a horizontal average over 86.4 km. For the Mie channel, the horizontal integration length of the wind measurements was decreased to approximately 10 km after 5 March 2019, taking benefit of the higher signal to noise ratio of cloud returns (Matic et al, 2019). In addition to the HLOS https://doi.org/10.5194/amt-2020-404 Preprint.…”

Section: Aeolus L2b Wind Productmentioning

confidence: 99%

Validation of Aeolus winds using radiosonde observations and NWP model equivalents

Martin¹,

Weißmann

Reitebuch

et al. 2020

Preprint

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Abstract. In August 2018, the first Doppler Wind Lidar, developed by the European Space Agency (ESA), was launched on board the Aeolus satellite into space. Providing atmospheric wind profiles on a global basis, the Earth Explorer mission is expected to demonstrate improvements in the quality of numerical weather prediction (NWP). For the use of Aeolus observations in NWP data assimilation, a detailed characterization of the quality and the minimization of systematic errors is crucial. This study performs a statistical validation of Aeolus observations, using collocated radiosonde measurements and NWP forecast equivalents from two different global models, the ICOsahedral Nonhydrostatic model (ICON) of Deutscher Wetterdienst (DWD) and the European Centre for Medium-Range Weather Forecast (ECMWF) Integrated Forecast System (IFS) model, as reference data. For the time period from the satellite's launch to the end of December 2019, comparisons for the northern hemisphere (23.5–65° N) show strong variations of the Aeolus winds bias and differences between the ascending and descending orbit phase. The mean absolute bias for the selected validation area is found to be in the range of 1.8–2.3 m s−1 (Rayleigh) and 1.3–1.9 m s−1 (Mie), showing good agreement between the independent reference data sets. Due to lower representativeness, the random differences are larger for the validation using radiosonde observations compared to the model equivalent statistics. To achieve an estimate for the Aeolus instrumental error, the representativeness errors for the comparisons are determined, besides the estimation of the model and radiosonde observational error. The resulting Aeolus errors estimates are in the range of 4.1–4.4 m s−1 (Rayleigh) and 1.9–3.0 m s−1 (Mie). Investigations of the Rayleigh wind bias on a global scale show that in addition to the satellite flight direction and seasonal differences, the systematic differences depend on latitude. A latitude based bias correction approach is able to reduce the bias, but a residual bias of 0.4–0.6 m s−1 with a temporal trend remains. Taking additional longitudinal differences into account, the bias can be reduced further by almost 50 %. Longitudinal variations are suggested to be linked to land-sea distribution and tropical convection that influences the thermal emission of the earth. Since 20 April 2020 a bias correction scheme has been applied operationally in the L2B processor, developed by the Aeolus Data Innovation and Science Cluster (DISC).

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“…Rayleigh‐clear and Mie‐cloudy winds are currently the only two types of Aeolus winds that are assimilated into the ECMWF model for operational weather forecasts (Rennie et al ., 2021). Furthermore, at the accumulation length of Aeolus (∼90 km), the less‐accurate Rayleigh‐clear channel provides up to four times more observations than the Mie‐cloudy channel (Savli et al ., 2019). Based on these considerations, only Rayleigh‐clear and Mie‐cloudy winds were extracted to evaluate and apply VarQC in our study.…”

Section: Introductionmentioning

confidence: 99%

Assessing the benefit of variational quality control for assimilating Aeolus Mie and Rayleigh wind profiles in NOAA's global forecast system during tropical cyclones

Apodaca

Cucurull

Genkova

et al. 2023

Quart J Royal Meteoro Soc

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In this article, we show a “proof‐of‐concept” study to assess the utility of a variational quality control algorithm in increasing the number of assimilated Aeolus Mie‐cloudy and Rayleigh‐clear winds in National Oceanic and Atmospheric Administration (NOAA)'s global data assimilation and forecast system. The National Centers for Environmental Prediction (NCEP) Variational Quality Control (NCEP‐VQC) algorithm was tuned and applied during the minimization process. This type of quality control uses optimal control theory principles to treat outliers in the probability density function (PDF) of observational departure statistics, assuming that the observation errors follow a family of logistic distributions. In the case of Aeolus Mie‐cloudy and Rayleigh‐clear winds, the NCEP‐VQC algorithm permitted the relaxation of the gross error and one of the recommended ESA quality controls (reject Rayleigh‐clear observations below 850 hPa), assigned adaptive observation weights ranging from 0 to 1, and led to an increase in the number of retained Aeolus observations for the calculation of global analyses, which in turn improved the verification statistics on analyzed tropical storms This article discusses the advantage of implementing the NCEP‐VQC algorithm in the Aeolus data assimilation, the benefits of retaining more wind profiles that contribute to the analysis calculation, and shows improvements in the initialization and short‐term forecasts on several tropical cyclone cases.

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The prospects for increasing the horizontal resolution of the Aeolus horizontal line‐of‐sight wind profiles

Cited by 9 publications

References 37 publications

The impact of Aeolus wind retrievals on ECMWF global weather forecasts

The impact of Aeolus wind retrievals on ECMWF global weather forecasts

Validation of Aeolus winds using radiosonde observations and NWP model equivalents

Assessing the benefit of variational quality control for assimilating Aeolus Mie and Rayleigh wind profiles in NOAA's global forecast system during tropical cyclones

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