The contribution of Saharan dust to the ice-nucleating particle concentrations at the High Altitude Station Jungfraujoch (3580 m a.s.l.), Switzerland

The ice phase in mixed-phase clouds has a pivotal role in global precipitation formation as well as for Earth's radiative budget. Above 235 K, sparse particles with the special ability to initiate ice formation, ice-nucleating particles (INPs), are responsible for primary ice formation within these clouds. Mineral dust has been found to be one of the most abundant INPs in the atmosphere at temperatures colder than 258 K. However, the extent of the abundance and distribution of INPs remains largely unknown. To better constrain and quantify the impact of mineral dust on ice nucleation, we investigate the frequency of Saharan dust events (SDEs) and their contribution to the INP number concentration at 243 K and at a saturation ratio with respect to liquid water (Sw) of 1.04 at the High Altitude Research Station Jungfraujoch (JFJ; 3580 m a.s.l.) from February to December 2020. Using the single-scattering albedo Ångström exponent retrieved from a nephelometer and an Aethalometer, satellite-retrieved dust mass concentrations, simulated tropospheric residence times, and the attenuated backscatter signal from a ceilometer as proxies, we detected 26 SDEs, which in total contributed to 17 % of the time span analyzed. We found every SDE to show an increase in median INP concentrations compared to those of all non-SDE periods; however, they were not always statistically significant. Median INP concentrations of individual SDEs spread between 1.7 and 161 INP std L−1 and thus 2 orders of magnitude. In the entire period analyzed, 74.7 ± 0.2 % of all INPs were measured during SDEs. Based on satellite-retrieved dust mass concentrations, we argue that mineral dust is also present at JFJ outside of SDEs but at much lower concentrations, thus still contributing to the INP population. We estimate that 97 % of all INPs active in the immersion mode at 243 K and Sw=1.04 at JFJ are dust particles. Overall, we found INP number concentrations to follow a leptokurtic lognormal frequency distribution. We found the INP number concentrations during SDEs to correlate with the ceilometer backscatter signals from a ceilometer located 4.5 km north of JFJ and 1510 m lower in altitude, thus scanning the air masses at the same altitude as JFJ. Using the European ceilometer network allows us to study the atmospheric pathway of mineral dust plumes over a large domain, which we demonstrate in two case studies. These studies showed that mineral dust plumes form ice crystals at cirrus altitudes, which then sediment to lower altitudes. Upon sublimation in dryer air layers, the residual particles are left potentially pre-activated. Future improvements to the sampling lines of INP counters are required to study whether these particles are indeed pre-activated, leading to larger INP number concentrations than reported here.

Arctic Tundra Land Cover Classification on the Beaufort Coast Using the Kennaugh Element Framework on Dual-Polarimetric TerraSAR-X Imagery

Arctic tundra landscapes are highly complex and are rapidly changing due to the warming climate. Datasets that document the spatial and temporal variability of the landscape are needed to monitor the rapid changes. Synthetic Aperture Radar (SAR) imagery is specifically suitable for monitoring the Arctic, as SAR, unlike optical remote sensing, can provide time series regardless of weather and illumination conditions. This study examines the potential of seasonal backscatter mechanisms in Arctic tundra environments for improving land cover classification purposes by using a time series of HH/HV TerraSAR-X (TSX) imagery. A Random Forest (RF) classification was applied on multi-temporal Sigma Nought intensity and multi-temporal Kennaugh matrix element data. The backscatter analysis revealed clear differences in the polarimetric response of water, soil, and vegetation, while backscatter signal variations within different vegetation classes were more nuanced. The RF models showed that land cover classes could be distinguished with 92.4% accuracy for the Kennaugh element data, compared to 57.7% accuracy for the Sigma Nought intensity data. Texture predictors, while improving the classification accuracy on the one hand, degraded the spatial resolution of the land cover product. The Kennaugh elements derived from TSX winter acquisitions were most important for the RF model, followed by the Kennaugh elements derived from summer and autumn acquisitions. The results of this study demonstrate that multi-temporal Kennaugh elements derived from dual-polarized X-band imagery are a powerful tool for Arctic tundra land cover mapping.

Maximum covariance analysis of the sea surface backscatter signal models

In this work we study the applicability of the maximum covariance analysis (MCA) for the analysis of matrices characterizing the spatiotemporal models of sea surface backscatter signals for different types of sea waves. The method is based on the singular value decomposition of the covariance matrix describing the relationship between two spatiotemporal matrices. The dependence of the obtained correlation coefficients on the degree of sea roughness, as well as on the ratio of the heights of wind waves and rogue waves are determined. The statistical characteristics of the obtained correlation coefficients of the sea surface backscatter signals are analysed. Our results indicate that the MCA method, at least from the modelling perspective, could be applicable to the classification of the sea surface from its backscatter signal characteristics, including an early detection and analysis of the rogue waves onset and development.

Industry 4.0-Oriented Chipless RFID Backscatter Signal Variable Polarization Amplitude Deep Learning Coding

Due to the weak network security protection capabilities of control system network protocols under Industry 4.0, the research on industrial control network intrusion detection is still in its infancy. This article discussed and researched the intrusion prevention technology of industrial control networks based on deep learning. According to the electromagnetic scattering theory, the backscatter signal model of the chipless tag was established as a chipless tag structure. Polarized deep learning coding was used for the label; that was, deep learning coding was performed on the copolarization component and the cross-polarization component at the same time, and a 16-bit deep learning coding bit number was obtained. The wave crest deep learning coding was used for the split ellipse ring patch label, and the 6-bit deep learning coding bit number was obtained. Then, the poles of the scattered signal of the tag were extracted to identify the tag. The variable polarization effect was achieved by adopting the dipole resonant unit with the two ends bent. Aiming at the problem of low detection rate caused by the shallow selection of feature classification of intrusion prevention systems, an industrial control network intrusion prevention model based on self-deep learning encoders and extreme learning machines was proposed to extract features from industrial control network data through deep learning. For accurate classification, the theoretical judgment was also verified through simulation experiments, and it was proved that the detection rate of the model has also improved. It forms a set of industrial control network intrusion prevention system with complete functions and superior performance with data acquisition module, system log module, defense response module, central control module, etc. The matrix beam algorithm was used to extract the poles and residues for the late response, and the extracted poles and residues were used to reconstruct the signal. The reconstructed signal was compared with the scattered signal to verify the correctness of the pole extraction. Finally, the tags were processed and tested in the actual environment, and the measured results were consistent with the theoretical analysis and simulation results.

The contribution of Saharan dust to the ice nucleating particle concentrations at the High Altitude Station Jungfraujoch (3580 m a.s.l.), Switzerland

The ice phase in mixed-phase clouds has a pivotal role in global precipitation formation as well as for Earth's radiative budget. Above 235 K, sparse particles with the special ability to initiate ice formation, ice nucleating particles (INPs), are responsible for primary ice formation within these clouds. However, the abundance and distribution of INPs remain largely unknown. Mineral dust is known to be the most abundant INP in the atmosphere at temperatures colder than 258 K. To better constrain and quantify the impact of mineral dust on ice nucleation, we investigate the frequency of Saharan dust events (SDEs) and their contribution to the INP number concentration at 243 K and at a saturation ratio with respect to liquid water (Sw) of 1.04 at the High Altitude Research Station Jungfraujoch (JFJ; 3580 m a.s.l.) from February to December 2020. Using the single scattering albedo Angström exponent, satellite retrieved dust mass concentrations, simulated tropospheric residence times, and the attenuated backscatter signal from a ceilometer as proxies, we detected 26 SDEs, which in total contributed to 17 % of the time span analyzed. We found every SDE to show an increase in median INP concentrations compared to that of all non-SDE periods, however, not always statistically significant. Median INP concentrations of individual SDEs spread between 1.7 and 161 INP std L−1, thus, two orders of magnitude. In the entire period analyzed, 74.7 ± 0.2 % of all INPs were measured during SDEs. Based on satellite retrieved dust mass concentrations, we argue that mineral dust is also present at the JFJ outside of SDEs, but at much lower concentrations, thus still contributing to the INP population. We estimate 97.0 ± 0.3 % of all INPs active in the immersion mode at 243 K Sw = 1.04 at the JFJ to be mineral dust particles. Overall, we found INP number concentrations to follow a leptokurtic log-normal frequency distribution. We found the INP number concentrations during SDEs to correlate with the ceilometer backscatter signals from a ceilometer located 4.5 km north of the JFJ and 1510 m lower in altitude, thus scanning the air masses at the same altitude as the JFJ. Using the European ceilometer network allows studying the atmospheric pathway of mineral dust plumes over a large domain, which we demonstrate in two case studies. These studies showed that mineral dust plumes form ice crystals at cirrus altitudes, which then sediment to lower altitudes. Upon sublimation in dryer air layers, the residual particles are left potentially pre-activated. Future improvements to the sampling lines of INP counters are required to study if these particles are indeed pre-activated, leading to larger INP number concentrations than reported here.

INVESTIGATION OF SENTINEL-1 TIME SERIES FOR SENSITIVITY TO FERN VEGETATION IN AN EUROPEAN TEMPERATE FOREST

In this study, a dense Copernicus Sentinel-1 time series is analyzed to gain a better understanding of the influence of undergrowth vegetation, in particular of eagle fern (Pteridium aquilinum), on the C-band SAR signal in a temperate forest in the Free State of Thuringia, Germany. Even if signals from the ground below the canopy may not be expected at C-band, previous studies showed seasonal fluctuations of the backscatter for temperate forests without canopy closure, notably for evergreen coniferous stands. Many factors can be responsible for these observed fluctuations, but in this study, we analyze one possible factor: the presence of undergrowth vegetation, in particular, of fern. Especially, the Sentinel-1 backscatter signal is analyzed for different acquisition configurations regarding its temporal and its spatial stability at different growth stages. This time series study shows that a difference of backscattered signal of up to 0.7 dB exists between forest patches with a dense fern density in the understory and the ones with low undergrowth vegetation. This signal difference depends on the season and is remarkably strong comparing winter (no fern undergrowth) with summer (major fern undergrowth).

Calibration and retrieval of aerosol optical properties measured with Coherent Doppler Lidar

A practical method for instrumental calibration and aerosol optical properties retrieval based on Coherent Doppler Lidar (CDL) and sun-photometer is presented in this paper. To verify its feasibility and accuracy, this method is applied into three field experiments in 2019 and 2020. In this method, multi-wavelength (440 nm, 670 nm, 870 nm and 1020 nm) Aerosol Optical Depth (AOD) from sun-photometer measurements are used to estimate AOD at 1550 nm and calibrate integrated CDL backscatter signal. Then, it is validated by comparing the retrieved calibrated AOD at 1550 nm from CDL signal and that from sun-photometer measurements. Well agreement between them with the correlation of 0.96, the RMSE of 0.0085 and the mean relative error of 22% is found. From the comparison results of these three experiments, sun-photometer is verified to be an effective reference instrument for the calibration of CDL return signal and the aerosol optical properties measurement with CDL is feasible. It is expected to promote the study on the aerosol flux and transport mechanism in the planetary boundary layer with the widely deployed CDLs.

Comparison of seasonal evapotranspiration of temperate coniferous forests with Copernicus Sentinel-1 time series

<p>A changing climate accompanied by an increasing number of extreme weather events puts pressure on ecosystems around the globe. Evapotranspiration is one of the key metrics for understanding vegetation dynamics and changes in an ecosystem. Due to its complex nature, evapotranspiration is difficult to determine on a larger scale.<br>Existing approaches to correlate evapotranspiration measurements and radar backscatter signals were completed in boreal forests using ground-based scatterometers for short time series (several months) with much higher temporal resolution (multiple observations per hour) for small test sites. Our goal is to build upon this research to establish a broader understanding on the influences of evapotranspiration on the signal of the widely used Copernicus Sentinel-1 C-Band SAR for managed temperate coniferous forests. Variations of the observed backscatter signals (VV, VH) over several growing seasons and years (2016-2020) are investigated.<br>Besides wind, temperature or precipitation as some of the influencing parameters on the C-band SAR signal, we focus our analyses on the influence of evapotranspiration on the Sentinel-1 C-band signal. Therefore, we recorded long time series of Sentinel-1 data to investigate and estimate the correlation between forest evapotranspiration dynamics and SAR signal variations. For this purpose, Sentinel-1 and weather data from July 2016 to December 2020 were obtained for forested areas in the southeastern part of the Free State of Thuringia, central Germany.<br>We use four different weather station datasets with daily measurements to calculate evapotranspiration values following the Penman-Monteith approach and apply regression analyses to gain a better understanding about the influence on the SAR signal. To obtain regions with speckle-suppressed backscatter for in situ comparison, forest areas in a radius of five kilometers around the four weather stations are considered. For the analysis, radar datasets are differentiated in co- and cross-polarized data as well as descending and ascending flight directions. It seems also important to distinguish between frozen and no-frozen conditions as we discover strong changes in the C-band SAR signal but only minor changes in evapotranspiration values for temperatures below freezing level. Excluding frozen conditions, in situ evapotranspiration measurements and the SAR backscatter variations over four years directly correlate with R2-values up to 0.48 without any parameterization or calibration on both sides (SAR & in situ). Currently we are investigating statistical methods for in-depth analysis of the correlation between the two datasets. As the SAR backscatter signal at C-band is not a direct and sole function of evapotranspiration, future work will combine the modelling of the different influence parameters of the environment on the SAR backscatter signal and aim at quantifying their respective influence on the signal to better understand the seasonal signal variations.</p>

ATILD cloud/aerosol algorithms applied to ALADIN

<p><span><span>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. </span></span><span><span>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. </span></span></p><p><span><span>ATLID (</span></span><span><span>Atmospheric Lidar) </span></span><span><span>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. </span></span></p><p><span><span>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.</span></span></p><p><span><span>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)</span></span></p><p><span><span>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 </span></span><span><span>profiles.</span></span></p><p><span><span>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 L2</span></span><span><span>a</span></span><span><span> operational processor. In this presentation AEL-FM and AEL-PRO will be described and preliminary results presented and discussed.</span></span></p><p> </p>