First triple-wavelength lidar observations of depolarization and extinction-to-backscatter ratios of Saharan dust

Two layers of Saharan dust observed over Leipzig, Germany, in February and March 2021 were used to provide the first-ever lidar measurements of the dust lidar ratio (extinction-to-backscatter ratio) and linear depolarization ratio at all three classical lidar wavelengths (355, 532 and 1064 nm). The pure-dust conditions during the first event exhibit lidar ratios of 47 ± 8, 50 ± 5 and 69 ± 14 sr and particle linear depolarization ratios of 0.242 ± 0.024, 0.299 ± 0.018 and 0.206 ± 0.010 at wavelengths of 355, 532 and 1064 nm, respectively. The second, slightly polluted-dust case shows a similar spectral behavior of the lidar and depolarization ratio with values of the lidar ratio of 49 ± 4, 46 ± 5 and 57 ± 9 sr and the depolarization ratio of 0.174 ± 0.041, 0.298 ± 0.016 and 0.242 ± 0.007 at 355, 532 and 1064 nm, respectively. The results were compared with Aerosol Robotic Network (AERONET) version 3 (v3) inversion solutions and the Generalized Retrieval of Aerosol and Surface Properties (GRASP) at six and seven wavelengths. Both retrieval schemes make use of a spheroid shape model for mineral dust. The spectral slope of the lidar ratio from 532 to 1064 nm could be well reproduced by the AERONET and GRASP retrieval schemes. Higher lidar ratios in the UV were retrieved by AERONET and GRASP. The enhancement was probably caused by the influence of fine-mode pollution particles in the boundary layer which are included in the columnar photometer measurements. Significant differences between the measured and retrieved wavelength dependence of the particle linear depolarization ratio were found. The potential sources for these uncertainties are discussed.

The Surface Longwave Cloud Radiative Effect derived from Space Lidar Observations

Clouds warm the surface in the longwave (LW) and this warming effect can be quantified through the surface LW cloud radiative effect (CRE). The global surface LW CRE is estimated using long-term observations from space-based radiometers (2000–2021) but has some bias over continents and icy surfaces. It is also estimated globally using the combination of radar, lidar and space-based radiometer over the 5–year period ending in 2011. To develop a more reliable long time series of surface LW CRE over continental and icy surfaces, we propose new estimates of the global surface LW CRE from space-based lidar observations. We show from 1D atmospheric column radiative transfer calculations, that surface LW CRE linearly decreases with increasing cloud altitude. These computations allow us to establish simple relationships between surface LW CRE, and five cloud properties that are well observed by the CALIPSO space-based lidar: opaque cloud cover and altitude, and thin cloud cover, altitude, and emissivity. We use these relationships to retrieve the surface LW CRE at global scale over the 2008–2020 time period (27 Wm−2). We evaluate this new surface LW CRE product by comparing it to existing satellite-derived products globally on instantaneous collocated data at footprint scale and on global averages, as well as to ground-based observations at specific locations. Our estimate appears to be an improvement over others as it appropriately capture the surface LW CRE annual variability over bright polar surfaces and it provides a dataset of more than 13 years long.

Fluorescence lidar observations of wildfire smoke inside cirrus: A contribution to smoke-cirrus – interaction research

A remote sensing method, based on fluorescence lidar measurements, that allows to detect and to quantify the smoke content in upper troposphere and lower stratosphere (UTLS) is presented. The unique point of this approach is that, smoke and cirrus properties are observed in the same air volume simultaneously. In the article, we provide results of fluorescence and multiwavelength Mie-Raman lidar measurements performed at ATOLL observatory from Laboratoire d’Optique Atmosphérique, University of Lille, during strong smoke episodes in the summer and autumn seasons of 2020. The aerosol fluorescence was induced by 355 nm laser radiation and the fluorescence backscattering was measured in a single spectral channel, centered at 466 nm of 44 nm width. To estimate smoke properties, such as number, surface area and volume concentration, the conversion factors, which link the fluorescence backscattering and the smoke microphysical properties, are derived from the synergy of multiwavelength Mie-Raman and fluorescence lidar observations. Based on two case studies, we demonstrate that the fluorescence lidar technique provides possibility to estimate the smoke surface area concentration within freshly formed cirrus layers. This value was used in smoke INP parameterization scheme to predict ice crystal number concentrations in cirrus generation cells.

Compositional Learning and Verification of Neural Network Controllers

Recent advances in deep learning have enabled data-driven controller design for autonomous systems. However, verifying safety of such controllers, which are often hard-to-analyze neural networks, remains a challenge. Inspired by compositional strategies for program verification, we propose a framework for compositional learning and verification of neural network controllers. Our approach is to decompose the task (e.g., car navigation) into a sequence of subtasks (e.g., segments of the track), each corresponding to a different mode of the system (e.g., go straight or turn). Then, we learn a separate controller for each mode, and verify correctness by proving that (i) each controller is correct within its mode, and (ii) transitions between modes are correct. This compositional strategy not only improves scalability of both learning and verification, but also enables our approach to verify correctness for arbitrary compositions of the subtasks. To handle partial observability (e.g., LiDAR), we additionally learn and verify a mode predictor that predicts which controller to use. Finally, our framework also incorporates an algorithm that, given a set of controllers, automatically synthesizes the pre- and postconditions required by our verification procedure. We validate our approach in a case study on a simulation model of the F1/10 autonomous car, a system that poses challenges for existing verification tools due to both its reliance on LiDAR observations, as well as the need to prove safety for complex track geometries. We leverage our framework to learn and verify a controller that safely completes any track consisting of an arbitrary sequence of five kinds of track segments.

Properties of coherent structures over Paris: a study based on an automated classification method for Doppler lidar observations

The studies related to the coherent structures in the atmosphere, using Doppler wind lidar observations, so far relied on the manual detection and classification of the structures in the lidar images, making this process time-consuming. We developed an automated classification based on texture analysis parameters and the quadratic discriminant analysis algorithm for the detection of medium-to-large fluctuations and coherent structures recorded by single Doppler wind lidar quasi-horizontal scans. The algorithm classified a training dataset of 150 cases into four types of patterns, namely streaks (narrow stripes), rolls (wide stripes), thermals (enclosed areas) and “others” (impossible to classify), with 91% accuracy. Subsequently, we applied the trained algorithm to a dataset of 4577 lidar scans recorded in Paris, atop a 75 m tower for a 2-month period (September-October 2014). The current study assesses the quality of the classification by examining the physical properties of the classified cases. The results show a realistic classification of the data: with rolls and thermals cases mostly classified concurrently with a well-developed atmospheric boundary layer and the streaks cases associated with nocturnal low-level jets (nllj) events. Furthermore, rolls and streaks cases were mostly observed under moderate or high wind conditions. The detailed analysis of a four-day period reveals the transition between the types. The analysis of the space spectra in the direction transverse to the mean wind, during these four days, revealed streaks spacing of 200 to 400 m, and rolls sizes, as observed in the lower level of the mixed layer, of approximately 1 km.

Combining radiative transfer calculations and a neural network for the remote sensing of volcanic ash using MSG/SEVIRI

After the eruption of volcanoes all over the world the monitoring of the dispersion of ash in the atmosphere is an important task for satellite remote sensing since ash represents a threat to air traffic. In this work we present a novel method that uses thermal observations of the SEVIRI imager aboard the geostationary Meteosat Second Generation satellite to detect ash clouds and determine their mass column concentration and top height during day and night. This approach requires the compilation of an extensive data set of synthetic SEVIRI observations to train an artificial neural network. This is done by means of the RTSIM tool that combines atmospheric, surface and ash properties and runs automatically a large number of radiative transfer calculations for the entire SEVIRI disk. The resulting algorithm is called VADUGS (Volcanic Ash Detection Using Geostationary Satellites) and has been evaluated against independent radiative transfer simulations. VADUGS detects ash contaminated pixels with a probability of detection of 0.84 and a false alarm rate of 0.05. Ash column concentrations are provided by VADUGS with correlations up to 0.5, a scatter up to 0.6 g m−2 for concentrations smaller than 2.0 g m−2 and small overestimations in the range 5–50 % for moderate viewing angles 35–65°, but up to 300 % for satellite viewing zenith angles close to 90° or 0°. Ash top heights are mainly underestimated, with the smallest underestimation of −9 % for viewing zenith angles between 40° and 50°. Absolute errors are smaller than 70 % and with high correlation coefficients up to 0.7 for ash clouds with high mass column concentrations. A comparison against spaceborne lidar observations by CALIPSO/CALIOPconfirms these results. VADUGS is run operationally at the German Weather Service and this application is presented as well.

Rayleigh Lidar Observations and Comparisons with TIMED/SABER of Typical Case Studies in Beijing (40.5° N, 116.2° E), China

Based on 139 nights of observational data of the Rayleigh lidar site in Beijing, China (40.5° N, 116.2° E), typical lower MIL cases and their temperature inversion evolution process were reported and compared with the SABER data from the same time. Meanwhile, the seasonal distribution of lower MIL cases over North China was also statistically analyzed. The average inversion temperature of the low MIL is 23.4 K, and the average layer thickness is 4.78 km with an average MIL bottom altitude of 68.2 km. Meanwhile, 65% of the MIL propagates vertically, most of which goes downward. These results show the temperature behavior properties of the lower MIL over North China, which may be helpful for us to further understand middle atmosphere chemical and dynamics processes.

The Influence of Tropospheric Processes on Disturbances in the D and E Ionospheric Layers

Determination of the physical mechanisms of the energy transfer of tropospheric disturbances to the ionosphere is one of the fundamental problems of atmospheric physics. This article presents the observational results of tropospheric and ionospheric disturbances during the passages of the solar terminator and solar eclipse. Lidar observations showed the occurrence of tropospheric regions with noticeably increased amplitudes of density, pressure, and temperature variations with periods corresponding to acoustic and internal gravity waves, which were generated in the troposphere during the development of these events. Simultaneous satellite measurements demonstrate the response of the ionosphere to these tropospheric disturbances. Based on the experimental data, we determine the typical periods and spatial scales of variations. It is shown that the response time of the ionosphere to tropospheric disturbances is 30–40 min.

First triple-wavelength lidar observations of depolarization and extinction-to-backscatter ratios of Saharan dust

Two Saharan dust layers observed over Leipzig in February and March 2021 were used to provide the first ever lidar measurements of the extinction coefficient at 1064 nm for desert dust. The advanced multiwavelength Raman polarization lidar was able to provide, for the first time, the lidar ratio (extinction-to-backscatter ratio) and particle linear depolarization ratio at all three classical lidar wavelengths (355, 532 and 1064 nm). The pure dust conditions during the first event exhibit lidar ratios of 47±8, 50±5 and 63±13 sr and particle linear depolarization ratios of 0.260±0.026, 0.298±0.017 and 0.214±0.025 at the wavelengths of 355, 532 and 1064 nm, respectively. The second, slightly polluted dust case shows a similar spectral behavior with values of the lidar ratio of 52±8, 47±5 and 61±10 sr and the depolarization ratio of 0.188±0.053, 0.270±0.017 and 0.242±0.007 at 355, 532 and 1064 nm, respectively. The results were compared to AERONET v3 inversions and GRASP retrievals at six and seven wavelengths, which could reproduce the spectral slope of the lidar ratio from 532 to 1064 nm. The spectral slope of the particle linear depolarization ratio could not be reproduced by the AERONET inversions, especially at 1064 nm.