Evaluation of Aeolus L2B wind product with wind profiling radar measurements and numerical weather prediction model equivalents over Australia

Zuo, Haichen; Hasager, Charlotte Bay; Karagali, Ioanna; Stoffelen, Ad; Marseille, Gert‐Jan; Kloe, Jos de

doi:10.5194/amt-2022-63

Cited by 4 publications

(7 citation statements)

References 19 publications

(23 reference statements)

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“…The corresponding random errors were 6.7 and 6.4 m s −1 (Rayleigh-clear) as well as 5.1 and 4.8 m s −1 (Mie-cloudy). The successful implementation of error corrections in the Aeolus L2B processor was also demonstrated by Guo et al (2021) and Zuo et al (2022), who used RWP measurements over China from April to July 2020 and over Australia from October 2020 until March 2021, respectively, to reveal a smaller mean systematic error of −0.6 m s −1 (Rayleigh-clear) and −0.3 m s −1 (Mie-cloudy) or 0.7 m s −1 for both Rayleigh-clear and Mie-cloudy winds. Besides that, Wu et al (2022) used ground-based DWL measurements in the timeframe of January-December 2020 and determined systematic errors of −1.2 m s −1 (Rayleigh-clear) and −0.3 m s −1 (Mie-cloudy) and random errors of 5.8 m s −1 (Rayleigh-clear) and 2.6 m s −1 (Mie-cloudy), respectively.…”

Section: Introductionmentioning

confidence: 82%

“…For the use of Aeolus observations in NWP models, a detailed characterization of the data quality as well as the minimization of systematic errors are crucial. Thus, several scientific and technical studies have been performed and published in the meantime, addressing the performance of ALADIN (Atmospheric LAser Doppler INstrument) on board Aeolus and the quality of the wind data products (e.g., Bedka et al, 2021;Martin et al, 2021;Baars et al, 2020;Guo et al, 2021;Zuo et al, 2022;Wu et al, 2022;Chou et al, 2022;Belova et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

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Validation of the Aeolus L2B wind product with airborne wind lidar measurements in the polar North Atlantic region and in the tropics

et al. 2022

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Abstract. During the first 3 years of the European Space Agency's Aeolus mission, the German Aerospace Center (Deutsches Zentrum für Luft- und Raumfahrt, DLR) performed four airborne campaigns deploying two different Doppler wind lidars (DWL) on board the DLR Falcon aircraft, aiming to validate the quality of the recent Aeolus Level 2B (L2B) wind data product (processor baseline 11 and 12). The first two campaigns, WindVal III (November–December 2018) and AVATAR-E (Aeolus Validation Through Airborne Lidars in Europe, May and June 2019), were conducted in Europe and provided first insights into the data quality at the beginning of the mission phase. The two later campaigns, AVATAR-I (Aeolus Validation Through Airborne Lidars in Iceland) and AVATAR-T (Aeolus Validation Through Airborne Lidars in the Tropics), were performed in regions of particular interest for the Aeolus validation: AVATAR-I was conducted from Keflavik, Iceland, between 9 September and 1 October 2019 to sample the high wind speeds in the vicinity of the polar jet stream; AVATAR-T was carried out from Sal, Cape Verde, between 6 and 28 September 2021 to measure winds in the Saharan dust-laden African easterly jet. Altogether, 10 Aeolus underflights were performed during AVATAR-I and 11 underflights during AVATAR-T, covering about 8000 and 11 000 km along the Aeolus measurement track, respectively. Based on these collocated measurements, statistical comparisons of Aeolus data with the reference lidar (2 µm DWL) as well as with in situ measurements by the Falcon were performed to determine the systematic and random errors of Rayleigh-clear and Mie-cloudy winds that are contained in the Aeolus L2B product. It is demonstrated that the systematic error almost fulfills the mission requirement of being below 0.7 m s−1 for both Rayleigh-clear and Mie-cloudy winds. The random error is shown to vary between 5.5 and 7.1 m s−1 for Rayleigh-clear winds and is thus larger than specified (2.5 m s−1), whereas it is close to the specifications for Mie-cloudy winds (2.7 to 2.9 m s−1). In addition, the dependency of the systematic and random errors on the actual wind speed, the geolocation, the scattering ratio, and the time difference between 2 µm DWL observation and satellite overflight is investigated and discussed. Thus, this work contributes to the characterization of the Aeolus data quality in different meteorological situations and allows one to investigate wind retrieval algorithm improvements for reprocessed Aeolus data sets.

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Section: Introductionmentioning

confidence: 82%

Section: Introductionmentioning

confidence: 99%

Validation of the Aeolus L2B wind product with airborne wind lidar measurements in the polar North Atlantic region and in the tropics

et al. 2022

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show abstract

“…The corresponding random errors were 6.7 m s −1 / 6.4 m s −1 (Rayleigh-clear) and 5.1 m s −1 /4.8 m s −1 (Mie-cloudy). The successful implementation of error corrections in the Aeolus L2B processor was also demonstrated by Guo et al (2021) and Zuo et al (2022) who used RWP measurements over China from April to July 2020 and over Australia from October 2020 until March 2021, respectively, to reveal a smaller mean systematic error of −0.6 m s −1 (Rayleigh-clear) and −0.3 m s −1 (Mie-cloudy), or 0.7 m s −1 for both Rayleigh-clear and Mie-cloudy winds. Besides that, Wu et al (2022) used ground-based heterodyne detection Doppler wind lidar measurements in the timeframe from January to December 2020 and determined systematic errors of −1.2 m s −1 (Rayleigh-clear) and −0.3 m s −1 (Mie-cloudy) and random errors of 5.8 m s −1 (Rayleigh-clear) and 2.6 m s −1 (Mie-cloudy), respectively.…”

Section: Introductionmentioning

confidence: 82%

“…For the use of Aeolus observations in NWP models, a detailed characterization of the data quality as well as the minimization of systematic errors is crucial. Thus, several scientific and technical studies have been performed and published in the meanwhile, addressing the performance of ALADIN (Atmospheric LAser Doppler INstrument) on-board Aeolus and the quality of the wind data products (e.g., Bedka et al, 2021;Martin et al, 2021;Baars et al, 2020;Guo et al, 2021;Zuo et al, 2022;Wu et al, 2022;Chou et al, 2021;Belova et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Validation of the Aeolus L2B wind product with airborne wind lidar measurements in the polar North Atlantic region and in the tropics

Witschas

Lemmerz

Geiß³

et al. 2022

Preprint

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Abstract. During the first three years of European Space Agency’s Aeolus mission, the German Aerospace Center (Deutsches Zentrum für Luft- und Raumfahrt, DLR) performed four airborne campaigns deploying two different Doppler wind lidars (DWL) on-board the DLR Falcon aircraft, aiming to validate the quality of the recent Aeolus Level 2B (L2B) wind data product (processor baseline 11 and 12). The first two campaigns, WindVal III (Nov/Dec 2018) and AVATAR-E (Aeolus Validation Through Airborne Lidars in Europe, May/Jun 2019) were conducted in Europe and provided first insights in the data quality at the beginning of the mission phase. The two later campaigns, AVATAR-I (Aeolus Validation Through Airborne Lidars in Iceland) and AVATAR-T (Aeolus Validation Through Airborne Lidars in the Tropics), were performed in regions of particular interest for the Aeolus validation: AVATAR-I was conducted from Keflavik, Iceland between 9 September and 1 October 2019 to sample the high wind speeds in the vicinity of the polar jet stream. AVATAR-T was carried out from Sal, Cape Verde between 6 September and 28 September 2021 to measure winds in the Saharan dust-laden African easterly jet. Altogether, 10 Aeolus underflights were performed during AVATAR-I and 11 underflights during AVATAR-T, covering about 8000 km and 11000 km along the Aeolus measurement track, respectively. Based on these collocated measurements, statistical comparisons of Aeolus data with the reference lidar (2-µm DWL) as well as with in-situ measurements by the Falcon were performed to determine the systematic and random error of Rayleigh-clear and Mie-cloudy winds that are contained in the Aeolus L2B product. It is demonstrated that the systematic error almost fulfills the mission requirement of being below 0.7 m s-1 for both Rayleigh-clear and Mie-cloudy winds. The random error is shown to vary between 5.5 m s-1 and 7.1 m s-1 for Rayleigh-clear winds and is thus larger than specified (2.5 m s-1), whereas it is close to the specifications for Mie-cloudy winds (2.7 to 2.9 m s-1). In addition, the dependency of the systematic and random errors on the actual wind speed, the geolocation, the scattering ratio and the time difference between 2-µm DWL observation and satellite overflight is investigated and discussed. Thus, this work contributes to the characterization of the Aeolus data quality in different meteorological situations and allows to investigate wind retrieval algorithm improvements for reprocessed Aeolus data sets.

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“…The accuracy and precision of the Aeolus wind data, which are included in the Level-2B (L2B) product, has been evaluated in the context of numerous calibration and validation (Cal/Val) activities. These studies include model comparisons (Martin et al, 2021;Chen et al, 2021) and the validation of the Aeolus wind product against groundbased and airborne instruments (Zuo et al, 2022;Wu et al, 2022;Liu et al, 2022;Bedka et al, 2021;Iwai et al, 2021;Baars et al, 2020). Within this international effort, the German Aerospace Center (Deutsches Zentrum für Luftund Raumfahrt, DLR) has carried out four airborne validation campaigns after the launch in 2018, deploying the AL-ADIN Airborne Demonstrator (A2D) and the 2 µm DWL.…”

Section: Introductionmentioning

confidence: 99%

Quality control and error assessment of the Aeolus L2B wind results from the Joint Aeolus Tropical Atlantic Campaign

Lux

Witschas²,

Geiß³

et al. 2022

Atmos. Meas. Tech.

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Abstract. Since the start of the European Space Agency's Aeolus mission in 2018, various studies were dedicated to the evaluation of its wind data quality and particularly to the determination of the systematic and random errors in the Rayleigh-clear and Mie-cloudy wind results provided in the Aeolus Level-2B (L2B) product. The quality control (QC) schemes applied in the analyses mostly rely on the estimated error (EE), reported in the L2B data, using different and often subjectively chosen thresholds for rejecting data outliers, thus hampering the comparability of different validation studies. This work gives insight into the calculation of the EE for the two receiver channels and reveals its limitations as a measure of the actual wind error due to its spatial and temporal variability. It is demonstrated that a precise error assessment of the Aeolus winds necessitates a careful statistical analysis, including a rigorous screening for gross errors to be compliant with the error definitions formulated in the Aeolus mission requirements. To this end, the modified Z score and normal quantile plots are shown to be useful statistical tools for effectively eliminating gross errors and for evaluating the normality of the wind error distribution in dependence on the applied QC scheme, respectively. The influence of different QC approaches and thresholds on key statistical parameters is discussed in the context of the Joint Aeolus Tropical Atlantic Campaign (JATAC), which was conducted in Cabo Verde in September 2021. Aeolus winds are compared against model background data from the European Centre for Medium-Range Weather Forecasts (ECMWF) before the assimilation of Aeolus winds and against wind data measured with the 2 µm heterodyne detection Doppler wind lidar (DWL) aboard the Falcon aircraft. The two studies make evident that the error distribution of the Mie-cloudy winds is strongly skewed with a preponderance of positively biased wind results distorting the statistics if not filtered out properly. Effective outlier removal is accomplished by applying a two-step QC based on the EE and the modified Z score, thereby ensuring an error distribution with a high degree of normality while retaining a large portion of wind results from the original dataset. After the utilization of the described QC approach, the systematic errors in the L2B Rayleigh-clear and Mie-cloudy winds are determined to be below 0.3 m s−1 with respect to both the ECMWF model background and the 2 µm DWL. Differences in the random errors relative to the two reference datasets (Mie vs. model is 5.3 m s−1, Mie vs. DWL is 4.1 m s−1, Rayleigh vs. model is 7.8 m s−1, and Rayleigh vs. DWL is 8.2 m s−1) are elaborated in the text.

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Evaluation of Aeolus L2B wind product with wind profiling radar measurements and numerical weather prediction model equivalents over Australia

Cited by 4 publications

References 19 publications

Validation of the Aeolus L2B wind product with airborne wind lidar measurements in the polar North Atlantic region and in the tropics

Validation of the Aeolus L2B wind product with airborne wind lidar measurements in the polar North Atlantic region and in the tropics

Validation of the Aeolus L2B wind product with airborne wind lidar measurements in the polar North Atlantic region and in the tropics

Quality control and error assessment of the Aeolus L2B wind results from the Joint Aeolus Tropical Atlantic Campaign

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