Airborne lidar observations supporting the ADM-Aeolus mission for global wind profiling

The Atmospheric Dynamics Mission ADM-Aeolus of ESA will be the first lidar mission to sense the global wind field from space. The instrument is based on a direct-detection Doppler lidar operating at 354.9 nm with two spectrometers for aerosol/cloud and molecular backscatter. In order to assess the performance of the Doppler lidar ALADIN on ADM-Aeolus and to optimize the retrieval algorithms with atmospheric signals, an airborne prototype – the ALADIN Airborne Demonstrator A2D – was developed. The A2D was the first airborne direct-detection Doppler lidar with its maiden flight on the DLR Falcon aircraft in 2005. Three airborne campaigns with a coherent-detection 2-μm wind lidar and the direct-detection wind lidar A2D were performed for pre-launch validation of Aeolus from 2007-2009. Furthermore, a unique experiment for resolving the Rayleigh-Brillouin spectral line shape in the atmosphere was accomplished in 2009 with the A2D from a mountain observatory at an altitude of 2650 m. Results of this experiment and the latest airborne campaign in the vicinity of Greenland and Iceland will be discussed

Alpine Pumping

„Alpines Pumpen“ ist ein regionales Zirkulationsphänomen, das sich tagsüber zwischen Gebirge und Vorland bei hoher Sonneneinstrahlung und schwachen Druckgradienten ausbildet. Die Luft im Alpenraum erwärmt sich tagsüber rascher als im Vorland, es bildet sich ein Hitzetief und die bodennahe Luft wird konvektiv nach oben verfrachtet.Aus dem Alpenvorland strömt Luft in einer Einströmschicht zu den Alpen und ersetzt die dort konvektiv gehobene Luft. Luftbeimengungen in der Einströmschicht gelangen so vom Alpenvorland in die freie Troposphäre bis über die Alpengipfel. Semivolatile Luftbeimengungen rekondensieren bei den niedrigen Temperaturen in den Hochalpen und reichern sich dort an. Für dieses häufige Transportphänomens, das durch intensiveren Vertikalaustausch über dem Hochgebirge ausgelöst wird, wurde die Bezeichnung „Alpines Pumpen“ gewählt. Es wird in diesem Beitrag näher beschrieben

Towards an assessment of Aeolus' Mie radiometric performance

The intensity of the return signal acquired by Aeolus depends on numerous factors such as the output laser energy, the state of the atmosphere along the line of sight, the characteristics of the target, the optical elements of the instrument and the alignment of laser beam and telescope. Already at an early stage of the mission, it was found that a significant part of the atmospheric backscatter signal was missing on the Rayleigh channel. Whereas the properties of the transmission and reception path of the Rayleigh channel can be simulated to a reasonable extent by the Aeolus End-To-End Simulator (E2S), it is much more difficult to align the simulation to the actual characteristics of the Mie channel, in particular to the transmission function of the Fizeau spectrometer. As a first attempt to assess the radiometric performance of Aeolus Mie channel, we are trying to derive a ratio between simulated and actual Aeolus signals. Therefore, we compare useful signals obtained with the E2S against measurements made with Aeolus in aerosol-laden atmospheric scenes. In this context, the backscatter and extinction measurements of portable ground-based Raman lidar systems from PollyNET as well as temperature and pressure information from external sources represent essential inputs for the simulation. Elevated, optically thick, vertically extended and preferably homogeneous aerosol layers are considered as the most suitable target. With the Raman lidars sensing a drifting aerosol layer from a fixed location and Aeolus as a mobile instrument sampling a quasi-fixed layer, optimisation is needed concerning the match of geolocation between the ground-based and space-borne measurements. Present ratios derived from scenes over Leipzig (Germany) and Al Dhaid (United Arabic Emirates) range from 0.6 to 0.9 (less measured signal than simulated). These factors show relative uncertainties of at least ±20% with expected error contributions based on the differences between Aeolus measurements, ground based measurements and simulation, i.e. location, heterogeneity of aerosol layers, E2S input parameters, assumptions in the handling of depolarised signals, potential cloud cover at altitudes higher than the measurement range, additional noise sources, etc. Reducing the number of contributors as well as their magnitude poses the biggest challenge for a reliable assessment of the Mie radiometric performance, which might only be achievable via statistical analyses on a larger number of cases

ADM-Aeolus pre-launch activities and recent advances in spaceborne and airborne Wind Lidar Systems

The first space-borne wind lidar mission ADM-Aeolus from ESA is currently scheduled for launch by mid-2017. For the preparation of the Aeolus validation, an airborne field experiment was performed during 3 weeks in May 2015 with the DLR Falcon and the NASA DC-8 aircraft. For the first time 4 wind lidars were deployed during an airborne campaign including two coherent and two direct-detection wind lidars at a wavelength of 2μm and 355 nm. A total of 7 coordinated flights of the Falcon and DC-8 yielded an extensive dataset. Additionally, DLR’s airborne coherent Doppler Wind Lidar was recently deployed in 3 coordinated airborne campaigns aiming to investigate the life cycle of gravity waves from ground up to the mesosphere. The horizontal and vertical wind measurements of the lidar provide valuable data for characterizing tropospheric gravity waves and background wind conditions

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

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.</p

Airborne temperature profiling in the troposphere during daytime by lidar utilizing Rayleigh–Brillouin scattering

The airborne measurement of a temperature profile from 10.5 km down towards ground (about 1.4 km above sea level) during daytime by means of a lidar utilizing Rayleigh-Brillouin (RB) scattering is demonstrated for the first time, to our knowledge. The spectra of the scattered light were measured by tuning the laser (Lambda=354.9 nm) over a 11 GHz frequency range with a step size of 250 MHz while using a Fabry Perot interferometer as a spectral filter. The measurement took 14 min and was conducted over a remote area in Iceland with the ALADIN Airborne Demonstrator on-board the DLR Falcon aircraft. The temperature profile was derived by applying an analytical RB line shape model to the backscatter spectra, which were measured at different altitudes with a vertical resolution of 630 m. A comparison with temperature profiles from radiosonde observations and model temperatures shows reasonable agreement with biases of less than +/-2K. Based on Poisson statistics, the random error of the derived temperatures is estimated to vary between 0.1 K and 0.4 K. The work provides insight into the possible realization of airborne lidar temperature profilers based on RB scattering

Verification of different Fizeau fringe analysis algorithms based on airborne wind lidar data in support of ESA's Aeolus mission

The Aeolus mission by the European Space Agency was launched in August 2018 and stopped operations in April 2023. Aeolus carried the direct-detection Atmospheric LAser Doppler INstrument (ALADIN). To support the preparation of Aeolus, the ALADIN Airborne Demonstrator (A2D) instrument was developed and applied in several field campaigns. Both ALADIN and A2D consist of so-called Rayleigh and Mie channels used to measure wind from both molecular and particulate backscatter signals. The Mie channel is based on the fringe-imaging technique, which relies on determining the spatial location of a linear interference pattern (fringe) that originated from multiple interference in a Fizeauspectrometer.The accuracy of the retrieved winds is among others depending on the analytic algorithm used for determining the fringe location on the detector. In this paper, the performance of two algorithms using Lorentzian and Voigt fit functions is investigated by applying them to A2D data that were acquired during the AVATAR-I airborne campaign. For performance validation, the data of a highly accurate heterodyne detection wind lidar (2-µm DWL) that was flown in parallel are used as a reference. In addition, a fast and non-fit-based algorithm based on a four-pixel intensity ratio approach (R4) is developed. It is revealed that the Voigt-fit-based algorithm provides 50% more data points than the Lorentzian-based algorithm while applying a quality control that yields a similar random error of about 1.5 m/s. The R4 algorithm is shown to deliver a similar accuracy as the Voigt-fit-based algorithms, with the advantage of a one to two orders of magnitude faster computation time. Principally, the R4 algorithm can be adapted to other spectroscopic applications where sub-pixel knowledge of the location of measured peak profiles is needed

Validation of the Aeolus L2B wind product: A new, very fast algorithm for the Fizeau fringe analysis based on pixel intensity ratios

The measurement of Aeolus Mie-cloudy winds is based on the fringe-imaging technique. It relies on determining the spatial location of a linear interference pattern (fringe) that is originated from multiple interference in a Fizeau spectrometer and vertically imaged onto the Mie-channel detector. The accuracy of Mie-cloudy winds thus depends on several pre- and post-detection factors. These include the optical quality of the Fizeau interferometer, its manner of illumination and any spurious background light, as well as the number of detector pixels and the analytic algorithm used for determining the fringe location on the detector. In the Aeolus Level 1 B (L1B) processor, the centroid location and the width of the Fizeau fringes are usually analyzed by the Mie-core 2 algorithm, which applies a downhill simplex fit routine of a Lorentzian peak function to the measurement data. Although this algorithm works accurately and reliably, recent investigations based on atmospheric ground return signals demonstrated, that the Mie fringe profile is better described by a Voigt profile and, thus, the application of a Voigt fit improves the frequency measurement and the accuracy of the retrieved scattering ratio. The Voigt-fit was implemented in the L1B processor in 2022 and will be tested in the future for Mie fringe centroid computation. Against this background, an alternative algorithm based on an intensity ratio of the inner 4 pixels was developed (R4) which is insensitive to uniform background illumination. Simulations also demonstrated that the R4 algorithm is rather insensitive to the spectral shape of the fringe profile and that it is potentially one to two orders of magnitude faster than the fit-based approaches. Besides simulations, the R4 algorithm was applied to data of the Aladin Airborne Demonstrator (A2D) and the results were compared to both, the Lorentzian and the Voigt fit analysis. In particular, the data set from the AVATAR-I (Aeolus VAlidation Through Airborne LidaRs in Iceland) campaign was used for this study. In this contribution, we introduce the R4 algorithm in detail and investigate differences to the existing Mie-core algorithms (Lorentzian and Voigt fits) based on A2D data acquired during the AVATAR-I campaign in Iceland in 2019

The quasi-biennial oscillation (QBO) and global-scale tropical waves in Aeolus wind observations, radiosonde data, and reanalyses

The quasi-biennial oscillation (QBO) of the stratospheric tropical winds influences the global circulation over a wide range of latitudes and altitudes. Although it has strong effects on surface weather and climate, climate models have great difficulties in simulating a realistic QBO, especially in the lower stratosphere. Therefore, global wind observations in the tropical upper troposphere and lower stratosphere (UTLS) are of particular interest for investigating the QBO and the tropical waves that contribute significantly to its driving. In our work, we focus on the years 2018–2022 and investigate the QBO and different tropical wave modes in the UTLS region using global wind observations made by the Aeolus satellite instrument and three meteorological reanalyses: the fifth generation European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA-5), the Japanese 55-year Reanalysis (JRA-55) of the Japan Meteorological Agency (JMA), and the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2). Further, we compare these data with observations of selected radiosonde stations. By comparison with Aeolus observations, we find that, on zonal average, the QBO in the lower stratosphere is well represented in all three reanalyses, with ERA-5 performing best. Averaged over the years 2018–2022, agreement between Aeolus and the reanalyses is better than 1 to 2 m s−1, with somewhat larger differences during some periods. Differently from zonal averages, radiosonde stations provide only local observations and are therefore biased by global-scale tropical waves, which limits their use as a QBO standard. While reanalyses perform well on zonal average, there can be considerable local biases between reanalyses and radiosondes. We also find that, in the tropical UTLS, zonal wind variances of stationary waves and the most prominent global-scale traveling equatorial wave modes, such as Kelvin waves, Rossby-gravity waves, and equatorial Rossby waves, are in good agreement between Aeolus and all three reanalyses (in most cases better than 20 % of the peak values in the UTLS). On zonal average, this supports the use of reanalyses as a reference for comparison with free-running climate models, while locally, certain biases exist, particularly in the QBO wind shear zones and around the 2019–2020 QBO disruption.</p

On the derivation of zonal and meridional wind components from Aeolus horizontal line-of-sight wind

Since its launch in 2018, the European Space Agency’s Earth Explorer satellite Aeolus has provided global height resolved measurements of horizontal wind in the troposphere and lower stratosphere for the first time. Novel datasets such as these provide an unprecedented opportunity for the research of atmospheric dynamics and provide new insights into the dynamics of the upper troposphere and lower stratosphere (UTLS) region. Aeolus measures the wind component along its horizontal line-of-sight, but for the analysis and interpretation of atmospheric dynamics, zonal and/or meridional wind components are most useful