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Structural plasticity of the brain describes the creation of new and the deletion of old synapses over time. Rinke et al. (JPDC 2018) introduced a scalable algorithm that simulates structural plasticity for up to one billion neurons on current hardware using a variant of the Barnes–Hut algorithm. They demonstrate good scalability and prove a runtime complexity of O (n log 2 ⁡ n). In this comment paper, we show that with careful consideration of the algorithm and a rigorous proof, the theoretical runtime can even be classified as O (n log ⁡ n). • Improved serial runtime bound for the adapted Barnes–Hut algorithm to Θ(n*log(n)). • Improved the parallel runtime bound to Θ(n/p*log(n)+p). • Mathematical justification that the given runtime bound is sharp. [ABSTRACT FROM AUTHOR]

The mixing layer height (MLH) is a measure for the vertical turbulent exchange within the boundary layer, which is one of the controlling factors for the dilution of pollutants emitted near the ground. Based on continuous MLH measurements with a Vaisala CL51 ceilometer and measurements from an air quality network, the relationship between MLH and near surface pollutant concentrations have been investigated. In this context the uncertainty of the MLH retrievals and the representativeness of ground-based in-situ measurements are crucial. We have investigated this topic by using data from the BAERLIN2014 campaign in Berlin, Germany, conducted during June and August 2014. To derive the MLH three versions of the proprietary software BL-VIEW and a novel approach COBOLT were compared. It was found that the overall agreement is reasonable if mean diurnal cycles are considered. The main advantage of COBOLT is the continuous detection of the MLH with a temporal resolution of 10 minutes and a lower number of cases when the residual layer is misinterpreted as mixing layer. We have calculated correlations between MLH as derived from the different retrievals and concentrations of pollutants (PM10, O3 and NOx) for different locations in the metropolitan area of Berlin. It was found that the correlations with PM10 are quite different for different sites without showing a clear pattern, whereas the correlation with NOx seems to depend on the vicinity of emission sources in main roads. In case of ozone as a secondary pollutant a clear correlation was found. We conclude that the effects of the heterogeneity of the emission sources, chemical processing and mixing during transport exceed the differences due to different MLH retrievals. Moreover, it seems to be unrealistic to find correlations between MLH and near surface pollutant concentrations representative for a city like Berlin, in particular when traffic emissions are dominant. Nevertheless it is worthwhile to use advanced MLH retrievals for ceilometer data, e.g. for the validation of chemical transport models. [ABSTRACT FROM AUTHOR]

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 µ m 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 (2.7to2.9 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 µ m 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 0.7m 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. [ABSTRACT FROM AUTHOR]

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 of the Rayleighclear 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. 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. The spatial and temporal variability of the Rayleigh and Mie EE values, together with the inconsistency in the QC approaches, hampers the comparability of the results across different validation studies. 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 assimilation of Aeolus winds and against wind data measured with the 2-μm heterodyne-detection Doppler wind lidar (DWL) onboard 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 gross errors 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. The same is true for the Rayleigh-clear winds, although the errors are more homogeneously distributed. After utilization of the described QC approach, the systematic errors of 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: 5.3 m∙s-1, Mie vs. DWL: 4.1 m∙s-1; Rayleigh vs. model: 7.8 m∙s-1; Rayleigh vs. DWL: 8.2 m∙s-1) are elaborated in the text. [ABSTRACT FROM AUTHOR]

Soon after the launch of Aeolus on 22 August 2018, the first ever wind lidar in space developed by the European Space Agency (ESA) has been providing profiles of the component of the wind vector along the instrument's line of sight (LOS) on a global scale. In order to validate the quality of Aeolus wind observations, the German Aerospace Center (Deutsches Zentrum für Luft- und Raumfahrt e.V., DLR) recently performed two airborne campaigns over central Europe deploying two different Doppler wind lidars (DWLs) on board the DLR Falcon aircraft. The first campaign – WindVal III – was conducted from 5 November 2018 until 5 December 2018 and thus still within the commissioning phase of the Aeolus mission. The second campaign – AVATARE (Aeolus Validation Through Airborne Lidars in Europe) – was performed from 6 May 2019 until 6 June 2019. Both campaigns were flown out of the DLR site in Oberpfaffenhofen, Germany, during the evening hours for probing the ascending orbits. All together, 10 satellite underflights with 19 flight legs covering more than 7500 km of Aeolus swaths were performed and used to validate the early-stage wind data product of Aeolus by means of collocated airborne wind lidar observations for the first time. For both campaign data sets, the statistical comparison of Aeolus horizontal line-of-sight (HLOS) observations and the corresponding wind observations of the reference lidar (2 µm DWL) on board the Falcon aircraft shows enhanced systematic and random errors compared with the bias and precision requirements defined for Aeolus. In particular, the systematic errors are determined to be 2.1 m s -1 (Rayleigh) and 2.3 m s -1 (Mie) for WindVal III and -4.6 m s -1 (Rayleigh) and -0.2 m s -1 (Mie) for AVATARE. The corresponding random errors are determined to be 3.9 m s -1 (Rayleigh) and 2.0 m s -1 (Mie) for WindVal III and 4.3 m s -1 (Rayleigh) and 2.0 m s -1 (Mie) for AVATARE. The Aeolus observations used here were acquired in an altitude range up to 10 km and have mainly a vertical resolution of 1 km (Rayleigh) and 0.5 to 1.0 km (Mie) and a horizontal resolution of 90 km (Rayleigh) and down to 10 km (Mie). Potential reasons for those errors are analyzed and discussed. [ABSTRACT FROM AUTHOR]

Soon after the launch of Aeolus on 22 August 2018, the first ever wind lidar in space developed by the European Space Agency (ESA) has been providing profiles of the component of the wind vector along the instrument's line-of-sight (LOS) on a global scale. In order to validate the quality of Aeolus wind observations, the German Aerospace Center (Deutsches Zentrum für Luft- und Raumfahrt e.V., DLR) recently performed two airborne campaigns over Central Europe deploying two different Doppler wind lidars (DWL) on-board the DLR Falcon aircraft. The first campaign - WindVal III - was conducted from 5 November 2018 until 5 December 2018 and thus, still within the commissioning phase of the Aeolus mission. The second campaign - AVATARE (Aeolus Validation Through Airborne Lidars in Europe) - was performed from 6 May 2019 until 6 June 2019. Both campaigns were flown out of the DLR site in Oberpfaffenhofen, Germany. All together, 10 satellite underflights with 19 flight legs covering more than 7500 km of Aeolus swaths were performed and used to validate the early stage wind data product of Aeolus by means of collocated airborne wind lidar observations for the first time. For both campaign data sets, the statistical comparison of Aeolus data and the data of the reference lidar (2-µm DWL) on-board the Falcon aircraft shows enhanced systematic and random errors compared with the bias and precision requirements defined for Aeolus. In particular, the systematic errors are determined to be 2.1 m/s (Rayleigh) and 2.3 m/s (Mie) for WindVal III and -4.6 m/s (Rayleigh) and -0.2 m/s (Mie) for AVATARE. The corresponding random errors are determined to be 4.0 m/s (Rayleigh) and 2.2 m/s (Mie) for WindVal III, and 4.4 m/s (Rayleigh) and 2.2 m/s (Mie) for AVATARE. Potential reasons for those errors are analyzed and discussed. [ABSTRACT FROM AUTHOR]

The realization of the European Space Agency's Aeolus mission was supported by the long-standing development and field deployment of the Atmospheric LAser Doppler INstrument (ALADIN) Airborne Demonstrator (A2D) which, since the launch of the Aeolus satellite in 2018, has been serving as a key instrument for the validation of ALADIN, the first-ever Doppler wind lidar (DWL) in space. However, the validation capabilities of the A2D are compromised by deficiencies of the dual-channel receiver which, like its spaceborne counterpart, consists of a Rayleigh and a complementary Mie spectrometer for sensing the wind speed from both molecular and particulate backscatter signals, respectively. Whereas the accuracy and precision of the Rayleigh channel is limited by the spectrometer's high alignment sensitivity, especially in the near field of the instrument, large systematic Mie wind errors are caused by aberrations of the interferometer in combination with the temporal overlap of adjacent range gates during signal readout. The two error sources are mitigated by modifications of the A2D wind retrieval algorithm. A novel quality control scheme was implemented, which ensures that only backscatter return signals within a small angular range are further processed. Moreover, Mie wind results with large bias of opposing sign in adjacent range bins are vertically averaged. The resulting improvement of the A2D performance was evaluated in the context of two Aeolus airborne validation campaigns that were conducted between May and September 2019. Comparison of the A2D wind data against a high-accuracy, coherent DWL that was deployed in parallel on board the same aircraft shows that the retrieval refinements considerably decrease the random errors of the A2D line-of-sight (LOS) Rayleigh and Mie winds from about 2.0 to about 1.5 m s -1 , demonstrating the capability of such a direct detection DWL. Furthermore, the measurement range of the Rayleigh channel could be largely extended by up to 2 km in the instrument's near field close to the aircraft. The Rayleigh and Mie systematic errors are below 0.5 m s -1 (LOS), hence allowing for an accurate assessment of the Aeolus wind errors during the September campaign. The latter revealed different biases of the Level 2B (L2B) Rayleigh-clear and Mie-cloudy horizontal LOS (HLOS) winds for ascending and descending orbits, as well as random errors of about 3 m s -1 (HLOS) for the Mie and close to 6 m s -1 (HLOS) for the Rayleigh winds, respectively. In addition to the Aeolus error evaluation, the present study discusses the applicability of the developed A2D algorithm modifications to the Aeolus processor, thereby offering prospects for improving the Aeolus wind data quality. [ABSTRACT FROM AUTHOR]

The realization of the European Space Agency's Aeolus mission was supported by the long-standing development and field deployment of the ALADIN Airborne Demonstrator (A2D) which, since the launch of the Aeolus satellite in 2018, has been serving as a key instrument for the validation of the Atmospheric LAser Doppler INstrument (ALADIN), the first-ever Doppler wind lidar (DWL) in space. However, the validation capabilities of the A2D are compromised by deficiencies of the dual-channel receiver which, like its spaceborne counterpart, consists of a Rayleigh and a complementary Mie spectrometer for sensing the wind speed from both molecular and particulate backscatter signals, respectively. Whereas the accuracy and precision of the Rayleigh channel is limited by the spectrometer's high alignment sensitivity, especially in the near field of the instrument, large systematic Mie wind errors are caused by aberrations of the interferometer in combination with the temporal overlap of adjacent range gates during signal readout. The two error sources are mitigated by modifications of the A2D wind retrieval algorithm. A novel quality control scheme was implemented which ensures that only backscatter return signals within a small angular range are further processed. Moreover, Mie wind results with large bias of opposing sign in adjacent range bins are vertically averaged. The resulting improvement of the A2D performance was evaluated in the context of two Aeolus airborne validation campaigns that were conducted between May and September 2019. Comparison of the A2D wind data against a high-accuracy, coherent Doppler wind lidar that was deployed in parallel on-board the same aircraft shows that the retrieval refinements considerably decrease the random errors of the A2D line-of-sight (LOS) Rayleigh and Mie winds from about 2.0 m∙s-1 to about 1.5 m∙s-1, demonstrating the capability of such a direct detection DWL. Moreover, the measurement range of the Rayleigh channel could be largely extended by up to 2 km in the instrument's near field close to the aircraft. The Rayleigh and Mie systematic errors are below 0.5 m∙s-1 (LOS), hence allowing for an accurate assessment of the Aeolus wind errors during the September campaign. The latter revealed different biases of the L2B Rayleigh-clear and Mie-cloudy horizontal LOS (HLOS) for ascending and descending orbits as well as random errors of about 3 m∙s-1 (HLOS) for the Mie and close to 6 m∙s-1 (HLOS) for the Rayleigh winds, respectively. In addition to the Aeolus error evaluation, the present study discusses the applicability of the developed A2D algorithm modifications to the Aeolus processor, thereby offering prospects for improving the Aeolus wind data quality. [ABSTRACT FROM AUTHOR]

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 wind 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 ms-1 (Rayleigh) and 1.3–1.9 ms-1 (Mie), showing good agreement between the three independent reference data sets. Due to the greater representativeness errors associated with the comparisons using radiosonde observations, the random differences are larger for the validation with radiosondes compared to the model equivalent statistics. To achieve an estimate for the Aeolus instrumental error, the representativeness errors for the comparisons are determined, as well as the estimation of the model and radiosonde observational error. The resulting Aeolus error estimates are in the range of 4.1–4.4 ms-1 (Rayleigh) and 1.9–3.0 ms-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 vary with latitude. A latitude-based bias correction approach is able to reduce the bias, but a residual bias of 0.4–0.6 ms-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 telescope temperature-based bias correction scheme has been applied operationally in the L2B processor, developed by the Aeolus Data Innovation and Science Cluster (DISC). [ABSTRACT FROM AUTHOR]

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). [ABSTRACT FROM AUTHOR]

Shortly after the successful launch of the European Space Agency's wind mission Aeolus, co-located airborne wind lidar observations were performed in central Europe; these observations employed a prototype of the satellite instrument – the ALADIN (Atmospheric LAser Doppler INstrument) Airborne Demonstrator (A2D). Like the direct-detection Doppler wind lidar on-board Aeolus, the A2D is composed of a frequency-stabilized ultra-violet (UV) laser, a Cassegrain telescope and a dual-channel receiver to measure line-of-sight (LOS) wind speeds by analysing both Mie and Rayleigh backscatter signals. In the framework of the first airborne validation campaign after the launch and still during the commissioning phase of the mission, four coordinated flights along the satellite swath were conducted in late autumn of 2018, yielding wind data in the troposphere with high coverage of the Rayleigh channel. Owing to the different measurement grids and LOS viewing directions of the satellite and the airborne instrument, intercomparison with the Aeolus wind product requires adequate averaging as well as conversion of the measured A2D LOS wind speeds to the satellite LOS (LOS*). The statistical comparison of the two instruments shows a positive bias (of 2.6 m s -1) of the Aeolus Rayleigh winds (measured along its LOS*) with respect to the A2D Rayleigh winds as well as a standard deviation of 3.6 m s -1. Considering the accuracy and precision of the A2D wind data, which were determined from comparison with a highly accurate coherent wind lidar as well as with the European Centre for Medium-Range Weather Forecasts (ECMWF) model winds, the systematic and random errors of the Aeolus LOS* Rayleigh winds are 1.7 and 2.5 m s -1 respectively. The paper also discusses the influence of different threshold parameters implemented in the comparison algorithm as well as an optimization of the A2D vertical sampling to be used in forthcoming validation campaigns. [ABSTRACT FROM AUTHOR]

Shortly after the successful launch of ESA's wind mission Aeolus, carried out by the European Space Agency, collocated airborne wind lidar observations were performed in Central Europe, employing the prototype of the satellite instrument, the ALADIN Airborne Demonstrator (A2D). Like the direct-detection Doppler wind lidar on-board Aeolus, the A2D is composed of a frequency-stabilised ultra-violet laser, a Cassegrain telescope and a dual-channel receiver to measure line-of-sight (LOS) wind speeds by analysing both Mie and Rayleigh backscatter signals. In the frame of the first airborne validation campaign after the launch still during the commissioning phase of the mission, four coordinated flights along the satellite swath were conducted in late autumn of 2018, yielding wind data in the troposphere with high coverage of the Rayleigh channel. Owing to the different measurement grids and viewing directions of the satellite and airborne instrument, intercomparison with the Aeolus wind product requires adequate averaging as well as conversion of the measured A2D LOS wind speeds to the satellite LOS. The statistical comparison of the two instruments with model wind data from the ECMWF shows biases of the A2D and Aeolus LOS wind speeds of -10.9 m s-1 and +1.6 m s-1, respectively, while the random errors are around 2.5 m s-1. The paper also discusses the influence of different threshold parameters implemented in the comparison algorithm as well as optimization of the A2D vertical sampling to be used in forthcoming validation campaigns. [ABSTRACT FROM AUTHOR]

In this paper, we present a comparison of model simulations of aerosol profiles with measurements of the ceilometer network operated by the German Weather Service (DWD) over 1 year from September 2015 to August 2016. The aerosol forecasts are produced by the Copernicus Atmosphere Monitoring Service (CAMS) using the aerosol module developed within the Global and regional Earth-system Monitoring using Satellite and in-situ data (GEMS) and Monitoring Atmospheric Composition and Climate (MACC) projects and coupled into the European Centre for Medium-Range Weather Forecasts Integrated Forecasting System (ECMWF-IFS). As the model output provides mass mixing ratios of different types of aerosol, whereas the ceilometers do not, it is necessary to determine a common physical quantity for the comparison. We have chosen the attenuated backscatter β* for this purpose. The β* profiles are calculated from the mass mixing ratios of the model output assuming the inherent aerosol microphysical properties. Comparison of the attenuated backscatter averaged between an altitude of 0.2 km (typical overlap range of ceilometers) and 1 km in general shows similar annual average values. However, the standard deviation of the difference between model and observation is larger than the average in 8 out of 12 sites. [ABSTRACT FROM AUTHOR]

In this paper, we present a comparison of European Centre for Medium-Range Weather Forecast Integrated Forecast System (ECMWF-IFS) model simulation of aerosol backscatter profiles with measurements of the ceilometer network operated by the German weather service (DWD) over 1 year from September 2015 to August 2016. As the model output provides mass mixing ratios of different types of aerosol whereas the ceilometers don't, it is necessary to determine a common physical quantity for the comparison. We have chosen the attenuated backscatter β* for this purpose. The β*-profiles are calculated from the mass mixing ratios of the model output assuming the inherent aerosol microphysical properties. Comparison of the attenuated backscatter, averaged between an altitude from 0.2 km (typical overlap range of ceilometers) and 1 km, showed slightly larger values from the model. To investigate possible reasons for the differences, we have examined the role of the hygroscopic growth of particles and the particle shape. Our results show that using a more recent particle growth model would result in a ~ 22 % reduction of particle backscatter for sea salt aerosols, corresponding to a 10 %-reduction of the total backscatter signal on average. Accounting for non-spherical dust particles in the model would reduce attenuated backscatter of dust particles by ~ 30 %. As the concentration of dust aerosol is in general very low in Germany, a significant effect on the total backscatter signal is restricted to dust episodes. In summary, consideration of both effects tend to improve the agreement between model and observations, but without leading to a perfect consistency. In addition a case study was conducted to investigate the agreement of the spatiotemporal distribution of particles. It was found that for a dust episode in April 2016 the arrival time of the dust layer and its vertical extent very well agree between model and ceilometer measurements for several stations. However, due to the large set of parameters characterizing the aerosol distribution and the complexity of the ceilometer retrieval an automated and quantitative comparison scheme for β*-profiles is still missing. Consequently, the representativeness of the case study remains open. [ABSTRACT FROM AUTHOR]

Abstract: The ash plume of the Eyjafjallajökull eruption in April 2010 offered an exceptional opportunity to assess the potential of advanced lidar systems to characterize the volcanic aerosols. Consequently, the plume was continuously observed in the framework of EARLINET. In this paper we focus on the EARLINET-Raman-depolarization-lidar measurements at Maisach near Munich, Germany. From these data sets the lidar ratio S p and the particle linear depolarization ratio δ p at two wavelengths (355 nm and 532 nm) were retrieved. These quantities can be used to characterize volcanic aerosols and to establish criteria for the discrimination from other aerosol types. In the pure volcanic ash plume, observed until noon of 17 April, wavelength independent values of δ p as high as 0.35 < δ p < 0.38, indicating non-spherical particles, were found, and lidar ratios of 50 < S p < 60 sr at 355 nm and 45 < S p < 55 sr at 532 nm. Later, volcanic aerosols were mixed into the boundary layer. This mixture showed in general lower values of δ p as expected from the contribution of boundary layer aerosols. Especially noteworthy is the increase of δ p with wavelength, when volcanic ash was mixed with small spherical particles. [Copyright &y& Elsevier]

ABSTRACT Measurements with two Raman-depolarization lidars of the Meteorological Institute of the Ludwig-Maximilians-Universität, München, Germany, performed during SAMUM-2, were used to characterize the planetary boundary layer (PBL) over Praia, Cape Verde. A novel approach was used to determine the volume fraction of dust υ d in the PBL. This approach primarily relies on accurate measurements of the linear depolarization ratio. Comparisons with independent in situ measurements showed the reliability of this approach. Based on our retrievals, two different phases could be distinguished within the measurement period of almost one month. The first (22-31 January 2008) was characterized by high aerosol optical depth (AOD) in the PBL and large υ d > 95%. During the second phase, the AOD in the PBL was considerably lower and υ d less than ∼40%. These findings were in very good agreement with ground based in situ measurements, when ambient volume fractions are considered that were calculated from the actual measurements of the dry volume fraction. Only in cases when dust was not the dominating aerosol component (second phase), effects due to hygroscopic growth became important. [ABSTRACT FROM AUTHOR]

In the last years a significant number of eye-safe single-wavelength backscatter lidars, oftenreferred to as ceilometers, was installed for unattended operation. Though the primaryreason for such ceilometer networks is the automation of synoptic observations, inparticular the determination of the cloud base height, the potential of quantitativeremote sensing of aerosols has been investigated for approximately 10 years. Oneof the main benefits is the very good spatial coverage that allows to track aerosollayers in real time. The optical characterization of aerosols is however limited tothe particle backscatter coefficient βp. In this context it was ignored for a longtime that most of the ceilometers work in a spectral range where water vapor isabsorbing, so that a correction is required if one aims at the quantitative retrieval ofβp. As a consequence we have investigated the relevance of water vapor absorption forceilometer signals and developed a numerical procedure to correct for it (Wiegner andGasteiger, 2015). Recently the validation of this correction scheme was attempted (Wiegneret al., 2018), based on data from the CeiLinEx2015 campaign in Lindenberg, Germany, whenseveral types of ceilometers and a reference lidar system were deployed. In this context itmust be considered that a strict validation is hardly possible as the optical properties ofparticles (backscatter and extinction) at the wavelength of the reference system (withoutwater vapor absorption, in our case at 1064 nm) and at the wavelength of the ceilometer(with absorption, around 910 nm) are different. They can only be estimated from sunphotometer data and the inversion of the reference lidar signal. Moreover, the emittedwavelength of the ceilometers is typically not exactly known due to missing technicalspecifications and possible drifts with temperature. Taking these uncertainties intoaccount it was demonstrated that the correction of CL51 (Vaisala) measurements istrustworthy. We want to emphasize that water vapor absorption does not play a significant role for theassessment of the mixing layer height (mlh) and the detection of (elevated) aerosol layers. Forthese applications the low signal-to-noise ratio and (possible) signal artefacts, but also theambiguity of most mlh-retrievals are more relevant. Nevertheless, we recommend monitoringthe emitted wavelength of the instrument to obtain new options of aerosol remotesensing. Wiegner, M. and Gasteiger, J.: Correction of water vapor absorption foraerosol remote sensing with ceilometers, Atmos. Meas. Tech., 8, 3971-3984,https://doi.org/10.5194/amt-8-3971-2015, 2015. Wiegner, M. et al.: Aerosol backscatter profiles from ceilometers: validation of watervapor correction in the framework of CeiLinEx2015, Atmos. Meas. Tech. Discuss.,https://doi.org/10.5194/amt-2018-307, in review, 2018. [ABSTRACT FROM AUTHOR]