Search

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.

The European Space Agency (ESA)’s Earth Explorer Aeolus was launched in August 2018 carrying the world’s first spaceborne wind lidar, the Atmospheric Laser Doppler Instrument (ALADIN). ALADIN uses a high spectral resolution Doppler wind lidar operating at 355nm to determine profiles of line-of-sight wind components in near-real-time (NRT). ALADIN samples the atmosphere from 30km altitude down to the Earth’s surface or to the level where the lidar signal is attenuated by optically thick clouds.The global wind profiles provided by ALADIN help to improve weather forecasting and the understanding of atmospheric dynamics as they fill observational gaps in vertically resolved wind profiles mainly in the tropics, southern hemisphere, and over the northern hemisphere oceans. Since 2020, multiple national and international weather centres (e.g. ECMWF, DWD, Météo France, MetOffice) assimilate Aeolus observations in their operational forecasting. Additionally, the scientific exploitation of the Aeolus dataset has started.A main prerequisite for beneficial impact and scientific exploitation is data of sufficient quality. Such high data quality has been achieved through close collaboration of all involved parties within the Aeolus Data Innovation and Science Cluster (DISC), which was established after launch to study and improve the data quality of Aeolus products. The tasks of the Aeolus DISC include the instrument and platform monitoring, calibration, characterization, retrieval algorithm refinement, processor evolution, quality monitoring, product validation, and impact assessment for NWP.The achievements of the Aeolus DISC for the NRT data quality and the one currently available reprocessed dataset will be presented. The data quality of the Aeolus wind measurements will be described and an outlook on planned improvements of the dataset and processors will be provided.

International audience; ESA’s Aeolus satellite observations are expected to have the biggest impact for the improvement of numerical weather prediction in the Tropics. An especially important case relating to the evolution, dynamics, and predictability of tropical weather systems is the outflow of Saharan dust, its interaction with cloud microphysics and impact on the development of tropical storms over the Atlantic Ocean. The Atlantic Ocean off the coast of West Africa and the eastern Caribbean uniquely allows the study of the Saharan Aerosol layer, African Easterly Waves and Jet, Tropical Easterly Jet, as well as the deep convection in the Intertropical Convergence Zone and their relation to the formation of convective systems, and the long-range transport of dust and its impact on air quality. The Joint Aeolus Tropical Atlantic Campaign (JATAC) deployed on Cabo Verde and the US Virgin Islands is addressing the validation and preparation of the ESA missions Aeolus, EarthCARE and WIVERN, as well as supporting the related science objectives raised above. The JATAC campaign started in July 2021 with the deployment of ground-based instruments at the Ocean Science Center Mindelo (OSCM, Cabo Verde), including the EVE lidar, the PollyXT lidar, a W-band Doppler cloud radar and a sunphotometer. By mid-August, the CPEX-AW campaign started their operations from the US Virgin Islands with NASA’s DC-8 flying laboratory in the Western Tropical Atlantic and Caribbean with the Doppler Aerosol Wind Lidar (DAWN), Airborne Precipitation and Cloud Radar (APR-3), the Water Vapor DIAL and HSRL (HALO), a microwave sounder (HAMSR) and dropsondes. In September, a European aircraft fleet was deployed to Sal (Cabo Verde) with the DLR Falcon-20 carrying the Aeolus Airborne Demonstrator (A2D) and the 2-µm Doppler wind lidar, and the Safire Falcon-20 carrying the high-spectral-resolution Doppler lidar (LNG), the RASTA Doppler cloud radar, in-situ cloud and aerosol instruments among others. The Aerovizija Advantic WT-10 light aircraft with filter-photometers and nephelometers for in-situ aerosol characterisation was operating in close coordination with the ground-based observations from Mindelo. More than 35 flights of the four aircraft were performed. 17 Aeolus orbits were underflown, four of which completed by simultaneous observations of three aircraft, with a perfect collocation of Aeolus and the ground-based observation for two cases. Several flights by the NASA DC-8 and the Safire Falcon-20 have been dedicated to cloud microphysics and dust events. The EVE lidar has been operating on a regular basis, while the PollyXT and several other ground-based instruments were continuously operating during the campaign period. For further characterisation of the atmosphere, radiosondes were launched up to twice daily from Sal airport. Additionally, there were radiosonde launches from western Puerto Rico and northern St Croix, US Virgin Islands. The JATAC was supported by dedicated numerical weather and dust simulations supporting the forecasting efforts needed for successful planning of the flights and addressing open science questions. While the airborne activities were completed end September, the ground-based observations are continuing into 2022. The paper will present an overview and initial results of JATAC. In memory of our colleague and friend Gail.

The European Space Agency (ESA)’s Earth Explorer Aeolus was launched in August 2018 carrying the world’s first spaceborne wind lidar, the Atmospheric Laser Doppler Instrument (ALADIN). ALADIN uses a high spectral resolution Doppler wind lidar operating at 355nm to measure profiles of line-of-sight wind components in near-real-time (NRT). ALADIN samples the atmosphere from 30km altitude down to the Earth’s surface or to the level where the lidar signal is attenuated by optically thick clouds.The global wind profiles provided by ALADIN help to improve weather forecasting and the understanding of atmospheric dynamics as they fill observational gaps in vertically resolved wind profiles mainly in the tropics, southern hemisphere, and over the northern hemisphere oceans. In January 2020, the European Centre for Medium-Range Weather Forecasts (ECMWF) became the first numerical weather prediction (NWP) centre to assimilate Aeolus observations for operational forecasting.A main prerequisite for beneficial impact is data of sufficient quality. Such high data quality has been achieved through close collaboration of all involved parties within the Aeolus Data Innovation and Science Cluster (DISC), which was established after launch to study and improve the data quality of Aeolus products. The tasks of the Aeolus DISC include the instrument and platform monitoring, calibration, characterization, retrieval algorithm refinement, processor evolution, quality monitoring, product validation, and impact assessment for NWP.The achievements of the Aeolus DISC for the NRT data quality and the current status of Aeolus wind measurements will be described and summarized. Further, an outlook on future improvements and the availability of reprocessed datasets with enhanced data quality will be provided.

Since the successful launch of ESA's Earth Explorer mission Aeolus in August 2018, atmospheric wind profiles from the ground to the lower stratosphere are being acquired on a global scale deploying the first-ever satellite-borne wind lidar system ALADIN (Atmospheric LAser Doppler INstrument). ALADIN provides one component of the wind vector along the instrument's line-of-sight (LOS) with a vertical resolution of 0.25 km to 2 km depending on altitude, while the precision in wind speed is between 2 m/s to 4 m/s. The near-real-time wind observations contribute to improving the accuracy of numerical weather prediction and advance the understanding of tropical dynamics and processes relevant to climate variability. Already several years before the satellite launch, an airborne prototype of the Aeolus payload, the ALADIN Airborne Demonstrator (A2D), was developed at DLR (German Aerospace Center). Due to its representative design and operating principle, the A2D has since delivered valuable information on the wind measurement strategies of the satellite instrument as well as on the optimization of the wind retrieval and related quality-control algorithms. Broad vertical and horizontal coverage across the troposphere is achieved thanks to the complementary design of the A2D receiver, which, like ALADIN, comprises a Rayleigh and Mie channel for analysing both molecular and particulate backscatter signals. In addition to the A2D, DLR's research aircraft carries a well-established coherent Doppler wind lidar (2-µm DWL) which allows determining the wind vector with accuracy of better than 0.1 m/s and precision of better than 1 m/s. Hence, both instruments represent key instruments for the calibration and validation activities during the Aeolus mission.

Aerosols are important constituents of the Earth’s atmosphere. Their impact on global climate, but also on air quality and hence human health is huge. A region were pollutants can disperse is the Mixing Layer (ML), the lowermost part of the Earth’s atmosphere. It’s thickness is directly influenced from the Earth’s surface. The physics of the ML is of great interest for the meteorological community as effects on the dynamics, thermodynamics and air quality of the atmosphere are crucial. Recently, networks of automated single-wavelength backscatter lidars (“ceilometers”) have been implemented, primarily by weather services. As a consequence, the potential of ceilometers to quantitatively determine the spatio-temporal distribution of atmospheric aerosols must be investigated. With regard to ceilometer networks, automatic mixing layer height retrievals for air quality studies and a fully automated calibration of ceilometers to derive aerosol optical properties is required. The absolute calibration approach, which is based on the determination of the lidar constant C_L was fully automated and is applicable in a three-step procedure to several ceilometer types. As a result, the particle backscatter coefficient β_p can be determined at virtually any weather condition during day and night, independent of the main ceilometer issue—the limited signal-to-noise ratio. Applied to 5 years of measurement of a Jenoptik CHM15kx, a lidar constant could be determined on 391 days out of 1900 available days. With knowing C_L, β_p-profiles within an accuracy of typically 17% can be derived. To allow investigations of the ML, the automatic ML-height retrieval algorithm COBOLT (Continuous Boundary Layer Tracing) was developed. In contrast to ML-cycles with large jumps or even temporal gaps, determined by already available and frequently used algorithms, COBOLT uses a time-height tracking procedure. On basis of a best-of-all-approach utilizing state-of-the-art layer detection techniques, a traceable parameter is defined and allows to detect complete diurnal ML-cycles without steps by including a multi-member approach. Validation and crosschecks with ML-heights from radiosonde data and two other ML-height retrieval algorithms demonstrated the reliability of COBOLT. A wide range of applications is possible with a calibrated ceilometer and a reliable ML-height retrieval algorithm. Following examples are shown: a β_p-profile statistic and ML-height statistic above Munich; ML-height comparisons between rural and an urban site, as well as a validation of a chemistry transport model and an investigation of ML-height influences on air quality., Aerosolpartikel sind ein wichtiger Bestandteil der Erdatmosphäre. Sie haben Einfluss auf das globale Klima, aber auch auf die Luftqualität und somit letztlich auch auf die menschliche Gesundheit. Schadstoffe breiten sich insbesondere in der Mischungsschicht (ML) aus, dem untersten Teil der Erdatmosphäre. Die ML-Höhe wird direkt von der Erdoberfläche aus beeinflusst. Die Physik der ML ist von großem Interesse für die Meteorologie, da dynamische und thermodynamische Prozesse sowie die Zusammensetzung der Atmosphäre eine große Rolle spielen. In letzter Zeit haben vor allem Wetterdienste Netzwerke mit automatisierten Rückstreulidargeräten mit einer Wellenlänge (Ceilometer) aufgebaut. Folglich muss untersucht werden, welches Potential Ceilometer bei der quantitativen Bestimmung der räumlichen sowie zeitlichen Aerosolverteilung in der Atmosphäre haben. Im Hinblick auf Ceilometernetzwerke ist die Entwicklung einer automatischen Bestimmung der ML-Höhe für Untersuchungen der Luftqualität und eine automatisierte Kalibrierung der Ceilometer nötig. Erst mit einer automatisierten Kalibrierung kann man optische Eigenschaften anhand von Ceilometernetzwerken ableiten. Es ist gelungen die hier entwickelte absolute Kalibrierung, welche auf der Bestimmung der Lidarkonstante C_L basiert, vollständig zu automatisieren. Sie kann in einem dreistufigen Verfahren auf verschiedenste Ceilometer angewendet werden. Dadurch kann man den Partikelrückstreukoeffizienten β_p unabhängig vom Signal-Rausch-Verhältnis, dem größten Problem der Ceilometer, bei fast allen Wetterbedingungen sowohl bei Tag als auch bei Nacht bestimmen. Mit der Anwendung auf eine fünfjährige Messreihe von einem Jenoptik CHM15kx lässt sich eine Lidarkonstante an 391 von 1900 verfügbaren Tagen ermitteln. Unter Nutzung von C_L können β_p-Profile mit einer Genauigkeit von 17% abgeleitet werden. Um Untersuchungen der ML vorzunehmen, wird der automatische Algorithmus COBOLT (Continuous Boundary Layer Tracing) zur Bestimmung der ML-Höhe entwickelt. Im Gegensatz zu ML-Tagesgängen von häufig benutzten Algorithmen, die große Sprünge oder gar Lücken in der von ihnen bestimmten ML-Höhe aufweisen, basiert COBOLT auf einer Zeit-Höhen-Verfolgung. Auf Grundlage eines “best-of-all”-Ansatzes und unter der Verwendung aktueller Methoden zur Schichtbestimmung wird ein Parameter definiert. Dieser Parameter erlaubt unter Verwendung eines “multi-member”-Ansatzes ein Verfolgen der ML-Höhe zur Auswertung von vollständigen ML-Tagesgängen ohne Zwischenschritte. Der Vergleich und die Validierung von ML-Höhen aus Daten von Radiosonden und zwei anderen Algorithmen zur ML-Höhenbestimmung zeigen die Zuverlässigkeit von COBOLT. Die Kalibrierung von Ceilometern und der Algorithmus zur Mischungsschichthöhenbestimmung eröffnen eine Vielzahl von Anwendungsmöglichkeiten. Zu den gezeigten Beispielen gehört eine Statistik des β_p-Profils und eine Statistik der ML-Höhen über München. Dabei werden sowohl die ML-Höhen von ländlichen Gebieten mit urbanen Zentren verglichen, als auch eine Validierung von Chemietransportmodellen und eine Untersuchung des Einflusses der ML-Höhen auf die Luftqualität vorgenommen.