AS1.28

The latest results on the assessment of the impact of Aeolus Level-2B horizontal line-of-sight wind retrievals in global Numerical Weather Prediction at ECMWF will be presented.  Aeolus has been operationally assimilated at ECMWF since 9 January 2020.
Random and systematic error estimates were derived from observation minus background departure statistics.  The HLOS wind random error standard deviation is estimated to vary over the range 4.0-7.0 m/s for the Rayleigh-clear and 2.8-3.6 m/s for the Mie-cloudy; depending on atmospheric signal levels which in turn depends on instrument performance, atmospheric backscatter properties and the processing algorithms.
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 2-3 day 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 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.  If the OSEs are ready in time, we will present the impact of the first reprocessed Aeolus data for the July-December 2019 period.

How to cite: Rennie, M. P. and Isaksen, L.: An Update on the Impact of Aeolus Doppler Wind Lidar Observations for Use in Numerical Weather Prediction at ECMWF, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1254, https://doi.org/10.5194/egusphere-egu21-1254, 2021.

The impact of using wind speed data from the Aeolus satellite in a limited area Numerical Weather Prediction (NWP) system is being investigated using the limited area NWP model Harmonie-Arome over the Nordic region. We assimilate the Horizontal Line of Sight (HLOS) winds observed by Aeolus using a 3D-Var data assimilation for two different periods, one in Sept-Oct 2018 when the satellite was recently launched, and a later period in Apr-May 2020 to investigate the updated data processing of the HLOS winds. We find that the quality of the Aeolus observations have degraded between the first and second experiment period over our domain. However observations from Aeolus, in particular the Mie winds, have a clear impact on the analysis of the NWP model for both periods whereas the forecast impact is neutral when compared against radiosondes. Results from evaluation of observation minus background and observation minus analysis departures based on  Desroziers diagnostics show that the observation error should be increased for Aeolus data in our experiments, but the impact of doing so is small. We also see that there is potential improvement in using 4D-Var data assimilation with the Aeolus data. 

How to cite: Hagelin, S., Azad, R., Lindskog, M., Schyberg, H., and Körnich, H.: Evaluating the use of Aeolus satellite observations in a regional NWP model over the Nordic countries, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2275, https://doi.org/10.5194/egusphere-egu21-2275, 2021.

The horizontal line of sight (HLOS) wind data from Aeolus Doppler Wind Lidar (DWL) is available from the European Space Agency (ESA) Earth Online Portal. The data quality after the mirror bias correction was investigated using data from July to September 2020. According to the first guess departure (observation minus background) statistics in Japan Meteorological Agency’s (JMA’s) global data assimilation (DA) system, the biases were very small for both  Rayleigh and Mie HLOS wind data after quality controlled. Significant positive impacts of Aeolus HLOS wind data assimilation in the global DA system on the analysis accuracy and forecasting scores were found in experiments with Rayleigh wind data under clear-sky condition and Mie wind data under cloudy condition. Improvement of tropical cyclone track forecasting was also found for the typhoons in the Northwest Pacific Ocean and for the hurricanes in the Atlantic Ocean. The details of results of data assessment and assimilation experiments will be shown in the presentation.

How to cite: Okabe, I. and Okamoto, K.: Assessment of Aeolus DWL data and impact of assimilation in the JMA’s global data assimilation system, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6797, https://doi.org/10.5194/egusphere-egu21-6797, 2021.

Recent efforts have focused on evaluation of the reprocessed Aeolus Level 2B (L2B) wind data with ESA M1 bias correction and its impact on NOAA global forecast. Aeolus wind quality especially the remaining biases vs NOAA global model background is examined. As a result, a revised bias correction taking account of noises in both Aeolus and GFS winds is implemented in the NOAA global data assimilation system to improve Aeolus wind assimilation.  In this study we will present impact from Aeolus wind on NOAA global forecast, focusing on synoptic and mesoscale scale events, e.g., tropical cyclones track and intensity in Eastern Pacific, and heavy rainfalls over the Western Coast of US.

How to cite: Liu, H., Garrett, K., Ide, K., Hoffman, R., and Luekens, K.: Impact Assessment of Aeolus Winds on NOAA Global Forecast, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6851, https://doi.org/10.5194/egusphere-egu21-6851, 2021.

Tropical Africa is characterized by the world-wide largest degree of mesoscale convective organisation. During boreal summer, the wet phase of the West African Monsoon (WAM), the midlevel African easterly jet (AEJ) over the Sahel allows for the formation of synoptic-scale African easterly waves (AEWs) with a maximum intensity close to the West African coast. AEWs interact with convection and its mesoscale organization through modifications in humidity, temperature and vertical wind shear, and often serve as initial disturbances for tropical cyclogenesis. In addition, rainfall can be modulated by other types of tropical waves such as Kelvin or mixed Rossby gravity waves. Upper-tropospheric conditions are dominated by the Tropical Easterly Jet (TEJ), whose variability appears to be connected to convective activity. Overall, our quantitative understanding of the WAM system is still limited. The observational network over the region is sparse and rainfall forecasts with current Numerical Weather Prediction models are hardly better than climatology.

The Aeolus satellite launched in 2018 offers a great opportunity to further investigate the WAM with an unprecedented density of free-tropospheric wind data. Assimilating Aeolus wind observations in denial experiments using the current operational system of the European Centre for Medium-Range Weather Forecasts (ECMWF) shows that the main circulation features of the WAM are greatly impacted: the AEJ and the TEJ are systematically weaker and stronger respectively by~1m/s in the analysis fields including Aeolus data. As a consequence AEWs also show a weakening in the propagation amplitude. We are currently investigating the contributions of the HLOS (horizontal line-of-sight) Rayleigh and Mie wind observations to these observed differences. Mie observations (i.e., those related to backscatter from hydrometeors and aerosol particles) seems to contribute strongly to the difference in the AEJ, which lies within a convectively active region with a high aerosol load. On the other hand, the difference seen in the TEJ appears to originate mostly in the Rayleigh (i.e., clear air) observations. Surprisingly, the ascending and descending HLOS observations contribute differently to the data impact, possibly revealing a remaining bias or model problems with the diurnal cycle. Future work will include systematic comparisons between the operational systems of DWD and ECMWF to understand the influence of different data assimilation approaches as well as the impact on forecasts.

How to cite: Borne, M., Knippertz, P., Weissmann, M., Rennie, M., and Cress, A.: The Impact of Aeolus wind observations on the West African Monsoon, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9449, https://doi.org/10.5194/egusphere-egu21-9449, 2021.

The quasi-biennial oscillation (QBO) is a regular cycle of alternating winds which dominates the behaviour of the tropical stratosphere. It is extremely technically challenging to model, and for this reason wind observations are vital to understand it fully. Characterised by downward propagating easterly and westerly regimes, the QBO progressed uninterrupted for more than 60 years until a highly anomalous deviation from its normal pattern in 2016. During 2019/2020, the start of a second disruption was seen in atmospheric analyses and radiosonde observations. Here, we exploit novel data from ESA's ADM-Aeolus satellite to demonstrate its ability to measure the QBO in unprecedented detail. A special adjustment of Aeolus' onboard range bin settings was implemented to observe this new disruption as it happened, providing a unique platform for studying the evolution of the event and the broader atmospheric effects triggered by it. In this presentation, we will show results from this special mode, highlighting how it has helped study the disruption, and how Aeolus and similar satellites can deepen our understanding of the QBO more generally.

How to cite: Banyard, T., Wright, C., Hindley, N., Halloran, G., and Osprey, S.: The 2019/2020 QBO Disruption in ADM-Aeolus Wind Lidar Observations, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16107, https://doi.org/10.5194/egusphere-egu21-16107, 2021.

Aeolus is a European Space Agency (ESA) Earth Explorer mission, launched on 22 August 2018 as part of the Living Planet Programme. 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). A crucial prerequisite for the use of meteorological observations in NWP data assimilation systems is a detailed characterization of the quality to minimize systematic observation errors. As part of the German initiative EVAA (Experimental Validation and Assimilation of Aeolus Observations) validation and monitoring activities for Aeolus are performed using collocated radiosonde measurements and NWP forecast equivalents from two different global models, the ICOsahedral Nonhydrostatic model (ICON) of DeutscherWetterdienst (DWD) and the European Centre for Medium-Range Weather Forecast (ECMWF) Integrated Forecast System(IFS) model, as reference data. Systematic differences and bias dependencies are investigated and estimates for the Aeolus instrumental error are determined. Furthermore, impact experiments using the global ICON model are analyzed.    

How to cite: Martin, A., Weissmann, M., Geiß, A., Reitebuch, O., and Cress, A.: Validation of Aeolus winds using radiosonde observations and NWP model equivalents, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3178, https://doi.org/10.5194/egusphere-egu21-3178, 2021.

So far, validation of Aeolus winds for the polar regions has been based on the ECMWF global data assimilation and forecasting system (e.g. Rennie and Isaksen 2020).  Very few conventional upper air meteorological measurements (radiosondes, aircraft in-situ sensors) are available in the polar regions so the model’s accuracy is not well known in those regions.  There is a risk that different cloud conditions, surface reflectivities and summer daylight in these regions could lead to different performance of the space-borne lidar measurements. At the same time, accurate measurements over the polar regions would be a particular asset to global weather forecasting and climate monitoring as these regions are so poorly covered by other observations. We validate Aeolus Rayleigh and Mie winds by comparison with winds measured by two atmospheric radars, ESRAD and MARA, located at Esrange (68°N 21°E) in Arctic Sweden and at the Indian Antarctic station Maitri (71°S 12°E), respectively, for the period July - December 2019 when reprocessed data for baseline 10 with the telescope mirror temperature correction were available. Data were divided into two seasons: summer with 12 -24 hours direct sunlight and winter covering the rest of the time. Aeolus - radar collocation events are defined when the distance between Aeolus measurement swath and the radar sites is less than 100 km. We computed regression, bias, and standard deviation for the Aeolus winds in comparison with the radars. For Rayleigh winds the slope of regression line is not significantly different from 1, and bias is not significantly different from 0.  Random difference (std) is 4.4 m/s – 7 m/s. For Rayleigh winds at both locations and Mie winds at Esrange we did not find any statistically significant difference between ascending/descending orbits and seasons. However, at Maitri, Antarctica a few m/s bias is found for Mie winds in summer for ascending (evening) passes.

How to cite: Belova, E., Kirkwood, S., Voelger, P., Chatterjee, S., Satheesan, K., Hagelin, S., Lindskog, M., and Körnich, H.: Validation of Aeolus Rayleigh and Mie winds using atmospheric radars in Arctic Sweden and in Antarctica, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11296, https://doi.org/10.5194/egusphere-egu21-11296, 2021.

In this study we propose and test a feature track correction (FTC) observation operator for atmospheric motion vectors (AMVs).  The FTC has four degrees of freedom corresponding to wind speed multiplicative and additive corrections (γ and δV), a vertical height assignment correction (h), and an estimate of the depth of the layer that contributes to the AMV (Δz).  Since the effect of the FTC observation operator is to add a bias correction to a weighted average of the profile of background winds an alternate formulation is in terms of a profile of weights (w_k) and δV .

The FTC observation operator is tested in the context of a collocation study between AMVs projected onto the collocated Aeolus horizontal line-of-sight (HLOS) and the Aeolus HLOS wind profiles.  This is a prototype for an implementation in a variational data assimilation system and here the Aeolus profiles act as the background in the FTC observation operator.  Results were obtained for ten days of data using modest QC.  The overall OMB or collocation difference SD for a global solution applied to the independent sample is 5.49 m/s with negligible mean.  For comparison the corresponding simple (or pure) collocation SD is 7.85 m/s, and the null solution, which only interpolates the Aeolus profile to the reported height of the AMV and removes the overall bias, has an OMB SD of 7.23 m/s. These values correspond to reductions of variance of 51.0% and 42.3%, due to the FTC observation operator in comparison to the simple collocation and null solution, respectively.

These preliminary tests demonstrate the potential for the FTC observation operator for 

• Improving AMV collocations (including triple collocation) with profile wind data.
• Characterizing AMVs. For example, summary results for the HLOS winds show that AMVs compare best with wind profiles averaged over a 4.5 km layer centered 0.5 km above the reported AMV height.
• Improving AMV observation usage within data assimilation (DA) systems. Lower estimated error and more realistic representation of AMVs with variational FTC (VarFTC) should result in greater information extracted.  The FTC observation operator accomplishes this by accounting for the effects of h and Δz. 

How to cite: Hoffman, R. N., Lukens, K., Ide, K., and Garrett, K.: A Feature Track Correction (FTC) Observation Operator applied to Aeolus-AMV Collocations, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8519, https://doi.org/10.5194/egusphere-egu21-8519, 2021.

Wind information obtained from various means ​​play an important role in data assimilation of numerical weather prediction. Atmospheric Motion Vector (AMV) obtained from the geostationary satellites provide a high spatio-temporal resolution wind information over the whole glove. An accurate quality control is one of the key factor that needs for a better utilization of AMV. Here, we use Aeolus/Atmospheric Laser Doppler Instrument (ALADIN) data to analyze the error characteristics of AMV derived from a newly commissioned geostationary satellite, Geostationary Korea Multi Purpose Satellite-2A (GK2A), stationed over 128.2^o E. As majority of the GK2A AMV data are obtained over the ocean where the radiosonde data (used for the reference wind measurement for the error analysis of AMV) is sparse, the ALADIN data could play an important contribution. Data obtained from December 2019 to February 2020 (northern hemisphere winter) are collocated with time, space, and altitude criteria of ±15 min, 0.9 ^o, and 50hPa. For the quality control data, only AMV data with a Quality Index (QI) of 0.85 or higher are used. In case of the ALADIN data, quality control is performed using the observation type (clear and cloudy) and error estimation value of the ALADIN data. The total number of collocated data for the AMV (using IR channel) and Mie channel ALADIN data is 39971 which gives the root mean square difference (RMSD) of 3.88 m/s. The lower layer (lower than 700 hPa altitude) RMSD shows slightly better comparison, 3.35 m/s vs. 4.17 m/s, while the correlation coefficient is better for the upper and middle layers of 0.98 compared to the 0.94 of the lower layer. In the conference, detailed analysis of the comparison results and additional AMV data, including visible channel and water vapor channel along with the extended time period are going to be presented.

How to cite: Shin, H., Ahn, M.-H., Kim, J., Kim, J.-G., and Choi, J.-T.: Inter-comparison of wind vectors derived from geostationary satellites with the Aeolus/ALADIN, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15160, https://doi.org/10.5194/egusphere-egu21-15160, 2021.

ESA supported airborne and ground-based campaigns constitute an essential element in the development and operation of satellite missions, providing the opportunity to test novel observation technologies, acquire representative data for the development of the mission concepts, processors and use cases, as well as in their calibration and validation phases once in orbit.

For the Aeolus Doppler Wind Lidar satellite mission, ESA has implemented a campaign programme that started in 2007 and has continued beyond the launch of the mission on 22. August 2018. Building on the successful WindVal-I and –II campaigns with DLR’s A2D and 2µm Doppler Wind Lidar systems on-board the DLR Falcon aircraft, a number of validation campaigns have been successfully implemented: WindVal-III in November 2018, AVATAR-E in May 2019, and AVATAR-I in September 2019. In addition, ESA supported the CNES pre-Stratéole-2/TAPAPA campaign with eight stratospheric balloons having been launched from the Seychelles in November/December 2019 providing unique upper level wind data in the Tropics. The validation by stratospheric balloons has been extended in the frame of a collaboration with Loon LLC for a test case covering the months August and September 2019.

As the largest impact of the Aeolus observations is expected in the Tropics, and in particular over the Tropical oceans, ESA, in close collaboration with NASA and European partners, is currently implementing a Tropical campaign in July 2021.  With its base in Cape Verde the activity comprises both airborne and ground-based activities addressing the tropical winds and aerosol validation, as well as a wide range of science objectives. The location is unique as it allows the study of the Saharan Aerosol layer, African Easterly Waves and Jets, the Tropical Easterly Jet, as well as deep convection in ITCZ and tropical cyclogenesis, with a focus on the impact of Saharan dust on micro-physics in tropical cloud systems. The campaign builds on remote and in-situ observations from aircraft (DLR Falcon-20, the Safire Falcon-20, the NASA DC-8 and an Aerovizija/UNG light aircraft) and drone systems, as well as an extensive aerosol and cloud measurement programme with a range of lidar, radar and radiometer systems coordinated by NOA.

This paper will provide a summary of the Aeolus campaign activities, focussing on the completed and planned post launch campaigns.

How to cite: Fehr, T., Amiridis, V., von Bismarck, J., Bley, S., Flamant, C., Hertzog, A., Lemmerz, C., Močnik, G., Parrinello, T., Skofronick-Jackson, G., and Straume, A. G.: Aeolus Calibration, Validation and Science Post-Launch Campaigns, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12562, https://doi.org/10.5194/egusphere-egu21-12562, 2021.

In 2020, a joint NASA-ESA campaign focusing on the tropics was planned to take place in Cabo Verde. This campaign, now delayed to 2021, was designed to engage the broader scientific atmospheric dynamics community and to assist in calibrating and validating the recently launched ESA Aeolus wind lidar satellite system. This campaign is an opportunity to join the U.S. and European airborne wind lidar system teams addressing the Aeolus calibration and validation. Nominally, the NASA contribution is a follow-on to the Convective Processes Experiment (CPEX) field campaign which took place in 2017 (https://cpex.jpl.nasa.gov/). The 2021 field campaign will add an aerosol (A) and winds (W) component—CPEX-AW—and will provide opportunities to study the dynamics and microphysics related to the Saharan air layer, African easterly waves and jets, the marine atmospheric boundary layer, and convection that not only advance our understanding of tropical dynamics but also improve weather forecasts. The NASA component of the field campaign plans to begin intensive operations in early July 2021 and will continue until mid-August. Approximately 150 flight hours are planned on NASA’s DC-8 aircraft. Planned instruments are the Doppler Aerosol WiNd Lidar (DAWN), the High Altitude Lidar Observatory (HALO), the APR-3 radar (Ku, Ka, and W bands), the High Altitude Monolithic Microwave integrated Circuit (MMIC) Sounding Radiometer (HAMSR), and dropsondes. During Summer 2020, NASA’s team hosted a dry run of a simulated field campaign which included virtual flights. In this talk, we will discuss the plans for NASAs contributions to the 2021 Aeolus Field Campaign and present preliminary findings from using Aeolus data and CPEX-AW dry-run virtual flights.

How to cite: Skofronick-Jackson, G., Piña, A., and Chen, S.: NASA’s Contributions to the 2021 Aeolus Field Campaign: CPEX-AW, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8975, https://doi.org/10.5194/egusphere-egu21-8975, 2021.

The VirES for Aeolus service (https://aeolus.services) has been successfully running by EOX since August 2018. The service provides easy access and analysis functions for the entire data archive of ESA's Aeolus Earth Explorer mission through a web browser.

This free and open service is being extended with a Virtual Research Environment (VRE). The VRE builds on the available data access capabilities of the service and provides a data access Application Programming Interface (API) as part of a developing environment in the cloud using JupyterHub and JupyterLab for processing and exploitation of the Aeolus data. In collaboration with Aeolus DISC user requirements are being collected, implemented and validated.

Jupyter Notebook templates, an extensive set of tutorials, and documentation are being made available to enable a quick start on how to use VRE in projects. The VRE is intended to support and simplify the work of (citizen-) scientists interested in Aeolus data by being able to quickly develop processes or algorithms that can be shared or used to create visualizations for publications. Having a unified constant platform could potentially also be very helpful for calibration and validation activities by allowing easier result comparisons.

How to cite: Santillan Pedrosa, D., Geiss, A., Krisch, I., Weiler, F., Fischer, P., and Troina, G.: VirES for Aeolus - Virtual Research Environment (VRE), EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8347, https://doi.org/10.5194/egusphere-egu21-8347, 2021.

The European Satellite has the first space-borne high-spectral resolution UV lidar onboard called ALADIN. Two detection channels, a broadband (Rayleigh channel) and a narrowband (Mie channel), are implemented. Carefully calibrated, this combination offers the possibility to derive independent estimates of the backscatter and extinction coefficients of clouds andaerosols, leading to a direct estimation of the lidar ratio, useful for aerosol classification.

The presentation will show how the official processor of the mission works for the retrieval of optical properties of cloud and aerosol particles, with a focus on the currently available products (called L2A). The potential of the L2A processor will be illustrated by results obtained on data acquired since Aeolus launch and by comparisons to ground based lidars in the frame of Cal/Val activities.

The L2A product will become publicly available during Spring 2021. Thus, this is also an opportunity to introduce a few practical aspects about its usage.

How to cite: Flament, T., Dabas, A., Trapon, D., Lacour, A., Ehlers, F., Baars, H., and Huber, D.: Aeolus aerosol and cloud product, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14390, https://doi.org/10.5194/egusphere-egu21-14390, 2021.

The Aladin instrument on-board the ESA Earth Explorer satellite Aeolus is a UV high spectral resolution Doppler Wind Lidar. The main mission product is profiles of horizontally projected line-of-sight winds, and the instrument design is therefore optimized to measure Doppler shifts of the atmospheric backscatter signals compared to the UV light emitted at ~355 nm (ESA, 2008; Stoffelen, 2005). Since the lidar backscatter contains information on the location of optically thin aerosol and cloud layers and cloud tops, spin-off products have been developed to retrieve aerosol and cloud backscatter and extinction coefficient and lidar ratio profile products (ESA, 2008; Flamant, 2008; Flamant, 2017). The advantage of a high spectral resolution lidar is that it measures molecular and particle backscatter separately in two dedicated channels. Still, some contributions from molecular backscatter exists in the measurements from the Fizeau channel and vice versa. This channel cross-talk requires correction during the product retrieval.

The Aeolus L2A operational aerosol and cloud retrieval algorithm is applying the so-called high spectral resolution retrieval method for the calculation of the particle and extinction backscatter coefficient products. The algorithm, developed at IPSL and Météo-France, is called the Standard Correct Algorithm (SCA) (Flamant, 2008; Flamant, 2017). High signal noise is obtained due to ever-decreasing laser energies and instrument receive path transmission. As a result, the Aeolus SCA optical properties retrieval is hampered. Particularly the ill-posed particle extinction coefficient retrieval is severely affected. In the past, attempts were made to mitigate nonphysical optical properties by measures like zero-flooring or signal accumulation in even coarser range gates (Flamant, 2017). Their success was limited.

An alternative noise suppression approach by Maximum Likelihood Estimation has therefore been prototyped that permits the retrieval of extinction coefficients and lidar ratios solely within pre-defined physical bounds. The optical properties are fitted to the 24 Aeolus atmospheric range gates within single atmospheric columns, minimizing the corresponding distance to the observed L1B useful signals measured by both spectrometers. This up to 48-dimensional non-linear regression problem is solved by means of the L-BFGS-B algorithm (Zhu, 1997). The method has proven its usefulness in noise suppression with astonishing efficiency. Particularly, the retrieved extinction coefficient profiles are less noisy, clearly revealing atmospheric layers also visible in the L1B useful signal profiles. The method is validated on end-to-end simulations and in-orbit observations.

References

ESA, ADM-Aeolus Science Report. ESA SP-1311, ESA Communication Production Office, 121 pp., 2008, available on http://www.esa.int/aeolus.

Flamant, P. H., Cuesta, J., Denneulin, M.-L., Dabas, A., Huber, D. ADM-Aeolus retrieval algorithms for aerosol and cloud products, Tellus, 60A, 273-286, 2008, https://doi.org/10.1111/j.1600-0870.2007.00287.x.

Flamant, P. et al. ADM-Aeolus L2A Algorithm Theoretical Baseline Document, 2017, available on https://earth.esa.int/aos/AeolusCalVal.

Stoffelen, A. et al. The atmospheric dynamics mission for global wind field measurement, Bulletin of the American Meteorological Society, 86, 73-88, 2005, https://doi.org/10.1175/BAMS-86-1-73.

Zhu, C., Byrd R. H. and Nocedal, J. L-BFGS-B: Algorithm 778: L-BFGS-B, FORTRAN routines for large scale bound constrained optimization, ACM Transactions on Mathematical Software, 23 (4), 550-560, 1997, https://doi.org/10.1145/279232.279236.

How to cite: Ehlers, F., Dabas, A., Flament, T., Trapon, D., Lacour, A., and Straume-Lindner, A. G.: Noise Suppression in AEOLUS Optical Properties Retrieval by Maximum Likelihood Estimation, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9768, https://doi.org/10.5194/egusphere-egu21-9768, 2021.

ALADIN (Atmospheric Laser Doppler Instrument) is the world’s first space-based Doppler wind lidar. It is a direct detection system operating at 355 nm. ALADIN’s primary products are atmospheric line-of-sight winds. Wind-profiles are derived from the Doppler shift of the backscattered signals. Using a variation of the High Spectral Resolution Lidar technique (HSRL), two detection channels are used, a `Mie ‘-channel and a `Rayleigh’-channel. Cloud/aerosol information is also present in the signals, however, ALADIN’s design is optimized for wind observations.

ATLID (Atmospheric Lidar) is the lidar to be embarked on the Earth Clouds and Radiation Explorer (EarthCARE) mission. EarthCARE is a joint ESA-JAXA mission and will embark a cloud/aerosol lidar (ATLID), a cloud-profiling Radar (CPR) a multispectral cloud/aerosol imager (MSI) and a three—view broad-band radiometer (BBR). Both ALADIN and ATLID are HSRL systems, however, ATLID does not measure winds and is optimized exclusively for cloud and aerosol observations. In particular, compared to ALADIN, ATLID has a higher spatial resolution, measures the depolarization of the return signal and has a much cleaner Rayleigh- Mie backscatter signal separation.

With regards to the retrieval of aerosol and cloud properties both lidars face similar challenges. Amongst, these is the fact that the SNR ratio of the backscatter signals is low compared to terrestrial signal, this creates esp. large difficulties when using direct standard HSRL inversion methods. Along-track averaging can increase the SNR, however, the presence of clouds and other inhomogeneities will lead to often very large biases in the retrieved extinction and backscatters if not accounted for in an appropriate manner.

Over the past several years, cloud/aerosol algorithms have been developed for ATLID that have focused on the challenge of making accurate retrievals of cloud and aerosol extinction and backscatter specifically addressing the low SNR nature of the lidar signals and the need for intelligent binning/averaging of the data. Two of these ATLID processors are A-FM (ATLID featuremask) and A-PRO (ATLID profile processor)

A-FM uses techniques inspired from the field of image processing to detect the presence of targets at high resolution while A-PRO (using A-FM as input) preforms a multi-scale optimal-estimation technique in order to retrieve both aerosol and cloud extinction and backscatter profiles.

Versions of the A-FM and A-PRO processors have been developed for Aeolus (called AEL-FM and AEL-PRO, respectively). Prototype codes exist and preliminary versions are in the process of being introduced into the L2a operational processor. In this presentation AEL-FM and AEL-PRO will be described and preliminary results presented and discussed.

How to cite: Donovan, D., van Zadelhoff, G.-J., Wang, P., and Huber, D.: ATILD cloud/aerosol algorithms applied to ALADIN, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15189, https://doi.org/10.5194/egusphere-egu21-15189, 2021.

Clouds and aerosols play an important role in the Earth’s energy budget through a complex interaction with solar, atmospheric, and terrestrial radiation, and air humidity. Optically thick clouds efficiently reflect the incoming solar radiation and, globally, clouds are responsible for about two thirds of the planetary albedo. Thin cirrus trap the outgoing longwave radiation and keep the planet warm. Aerosols scatter or absorb sunlight depending on their size and shape and interact with clouds in various ways.

Due to the importance of clouds and aerosols for the Earth’s energy budget, global satellite observations of their properties are essential for climate studies, for constraining climate models, and for evaluating cloud parameterizations. Active sounding from space by lidars and radars is advantageous since it provides the vertically resolved information. This has been proven by CALIOP lidar which has been observing the Earth’s atmosphere since 2006. Another instrument of this kind, CATS lidar on-board ISS provided measurements for over 33 months starting from the beginning of 2015. The ALADIN lidar on-board ADM/Aeolus has been measuring horizontal winds and aerosols/clouds since August 2018. More lidars are planned – in 2022, the ATLID/EarthCare lidar will be launched and other space-borne lidars are in the development phase.

In this work, we compare the scattering ratio products retrieved from ALADIN and CALIOP observations. The former is aimed at 35 deg from nadir, it measures the atmospheric backscatter at 355nm from nadir, is capable of separating the molecular and particular components (HSRL), and provides the profiles with a vertical resolution of ~1km up to 20km altitude.  The latter, operating at 532nm is aimed at 3 deg from nadir and measures the total backscatter up to 40 km. Its natural vertical resolution is higher than that of ALADIN, but the scattering ratio product used in the comparison is provided at ~0.5km vertical grid.

We have performed a search of nearly simultaneous common volume observations of atmosphere by these two instruments for the period from 28/06/2019 through 31/12/2019 and analyzed the collocated data. We present the zonal averages of scattering ratios as well as the instantaneous profile comparisons and the statistical analysis of cloud detection, cloud height agreement, and temporal evolution of these characteristics.

The preliminary conclusion, which can be drawn from this analysis, is that the general agreement of scattering ratio profiles retrieved from ALADIN and CALIOP observations is good up to 6-7 km height whereas in the higher atmospheric layers ALADIN is less sensitive to clouds than the CALIOP. This lack of sensitivity might be compensated by further averaging of the input signals and/or by an updating of the retrieval algorithms using the collocated observations dataset provided in the present work.

How to cite: Feofilov, A., Chepfer, H., Noel, V., and Chiriaco, M.: Scattering ratio profiles retrieved from ALADIN/Aeolus and CALIOP/CALIPSO lidar observations: instantaneous overlaps, statistical comparison, and sensitivity to high clouds, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4746, https://doi.org/10.5194/egusphere-egu21-4746, 2021.

Since several years, the number of aerosol data coming from lidar has grown and improved in quality. These new datasets are providing a valuable information on the vertical distribution of aerosols which is missing in the AOD (Aerosol Optical Depth), which has been used so far in aerosols analysis. The launch of AEOLUS in 2018 has increased the interest in the assimilation of the aerosol lidar information. In parallel, the ground-based network EARLINET (European Aerosol Research LIdar NETwork) has grown to cover the Europe with good quality data. Assimilation of these data in the ECMWF/CAMS (European Centre for Medium-range Weather Forecasts / Copernicus Atmosphere Monitoring Service) system is expected to provide improvements in the aerosol analyses and forecasts.

Three preliminary studies have been done in the past four years using AEOLUS data (A3S-ESA funded) and EARLINET data (ACTRIS-2 and EUNADIC-AV, EU-funded). These studies have allowed the full development of the tangent linear and adjoint code for lidar backscatter in the ECMWF's 4D-VAR system. These developments are now in the operational model version in research mode. The first results are promising and open the path to more intake of aerosol lidar data for assimilation purposes. The future launch of EARTHCARE (Earth-Cloud Aerosol and Radiation Explorer) and later ACCP (Aerosol Cloud, Convention and Precipitation) might even upgrade the use of aerosol lidar data in COMPO-IFS (Composition-Integrated Forecast system).

The most recent results using AEOLUS data (for October 2019 and April 2020) and using EARLINET data (October 2020) will be shown in this presentation. The output will be compared to the CAMS operational aerosol forecast as well as to independent data from AERONET (AErosol Robotic NETwork).

How to cite: Letertre-Danczak, J., Benedetti, A., Vasiljevic, D., Dabas, A., Flament, T., Trapon, D., and Mona, L.: Aerosol Assimilation of lidar data from Satellite (AEOLUS) and Ground-based (EARLINET) instruments in COMPO-IFS., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4799, https://doi.org/10.5194/egusphere-egu21-4799, 2021.

In this study, we present a comparison of the AEOLUS satellite L2A product with the retrievals of the ground-based lidar systems of EARLINET (European Aerosol Research Lidar Network), part the European Research Infrastructure for the observation of Aerosol, Clouds and Trace Gases (ACTRIS). Dedicated ground‐based measurements during AEOLUS overpasses have been performed among the 29 member stations since the beginning of the mission, however, we have included only the stations that have gathered a significant number of collocations in the analysis. The satellite timeseries we deployed covers the period 2019-2020 that correspond to the best available version of the satellite processing algorithms. We harvest the collocations using the following spacio-temporal criteria. Only overpasses that fall within a radius less than 100km around the station are included. Using this criterion, the AEOLUS L2A climatology is generated per station independently of the ground-based measurements. To isolate collocated data we reject all AEOLUS data with a time interval between the overpass and the central time of the ground-based measurement that is greater than 3 hours. The ground based lidar climatology is also computed per station. AEOLUS L2A products include aerosol extinction coefficient profiles and aerosol co-polar backscatter coefficient profiles from circularly polarized light emission. While the extinction profiles are directly comparable with the ground-based lidars, this is not the case for the backscatter profiles since AEOLUS cannot measure the cross polar component of the aerosol backscatter. The co-polar backscatter is close to the total backscatter only in the absence of depolarizing scatterers such as dust, pollen, volcanic ash, and cirrus ice crystals. Ground-based measurements are divided in two categories for the evaluation depending on whether aerosol depolarization measurements have been performed. If the particle linear depolarization ratio (PLDR) is available, it can be applied to convert the lidar total backscatter to an AOLUS-like co-polar backscatter coefficient. This category is applied for the direct evaluation of the satellite product. Cases that lack PLDR information assist to quantify the uncertainties introduced by using the AEOLUS co-polar backscatter as a substitute for the total backscatter. The analysis includes both an indirect climatological comparison and a direct collocation comparison between the ground based and satellite datasets. Via the collocation comparison, random and systematic uncertainties in the satellite product are identified and quantified. A climatological comparison can show the potential of AEOLUS to capture annual cycles despite its intrinsic random errors. In the future, the analysis will be further supported with auxiliary data such as sunphotometer measurements, aerosol classification flags, modeled backward trajectories, and satellite cloud fraction data.

How to cite: Siomos, N., Gkikas, A., Baars, H., Wandinger, U., Amiridis, V., and Paschou, P. and the EARLINET consortium: Investigating the performance of AEOLUS L2A products over Europe with EARLINET ground-based lidars, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12460, https://doi.org/10.5194/egusphere-egu21-12460, 2021.