A new method to retrieve the real part of the equivalent refractive index of atmospheric aerosols

This document is the Accepted Manuscript version of the following article: S. Vratolis, et al, ‘A new method to retrieve the real part of the equivalent refractive index of atmospheric aerosols’, Journal of Aerosol Science, Vol. 117: 54-62, March 2018. Under embargo until 29 December 2019. The final, published version is available online at DOI: https://doi.org/10.1016/j.jaerosci.2017.12.013.In the context of the international experimental campaign Hygroscopic Aerosols to Cloud Droplets (HygrA-CD, 15 May to 22 June 2014), dry aerosol size distributions were measured at Demokritos station (DEM) using a Scanning Mobility Particle Sizer (SMPS) in the size range from 10 to 550 nm (electrical mobility diameter), and an Optical Particle Counter (OPC model Grimm 107 operating at the laser wavelength of 660 nm) to acquire the particle size distribution in the size range of 250 nm to 2.5 μm optical diameter. This work describes a method that was developed to align size distributions in the overlapping range of the SMPS and the OPC, thus allowing us to retrieve the real part of the aerosol equivalent refractive index (ERI). The objective is to show that size distribution data acquired at in situ measurement stations can provide an insight to the physical and chemical properties of aerosol particles, leading to better understanding of aerosol impact on human health and earth radiative balance. The resulting ERI could be used in radiative transfer models to assess aerosol forcing direct effect, as well as an index of aerosol chemical composition. To validate the method, a series of calibration experiments were performed using compounds with known refractive index (RI). This led to a corrected version of the ERI values, (ERICOR). The ERICOR values were subsequently compared to model estimates of RI values, based on measured PM2.5 chemical composition, and to aerosol RI retrieved values by inverted lidar measurements on selected days.Peer reviewe

Comparison and complementary use of in situ and remote sensing aerosol measurements in the Athens Metropolitan Area

© 2020 Elsevier Ltd. All rights reserved. This manuscript is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence http://creativecommons.org/licenses/by-nc-nd/4.0/.In the summer of 2014 in situ and remote sensing instruments were deployed in Athens, in order to study the concentration, physical properties, and chemical composition of aerosols. In this manuscript we aim to combine the measurements of collocated in situ and remote sensing instruments by comparison and complementary use, in order to increase the accuracy of predictions concerning climate change and human health. We also develop a new method in order to select days when a direct comparison on in situ and remote sensing instruments is possible. On selected days that displayed significant turbulence up to approximately 1000 m above ground level (agl), we acquired the aerosol extinction or scattering coefficient by in situ instruments using three approaches. In the first approach the aerosol extinction coefficient was acquired by adding a Nephelometer scattering coefficient in ambient conditions and an Aethalometer absorption coefficient. The correlation between the in situ and remote sensing instruments was good (coefficient of determination R2 equal to 0.69). In the second approach we acquired the aerosol refractive index by fitting dry Nephelometer and Aethalometer measurements with Mie algorithm calculations of the scattering and absorption coefficients for the size distribution up to a maximum diameter of 1000 nm obtained by in situ instruments. The correlation in this case was relatively good (R2 equal to 0.56). Our next step was to compare the extinction coefficient acquired by remote sensing instruments to the scattering coefficient calculated by Mie algorithm using the size distribution up to a maximum diameter of 1000 nm and the equivalent refractive index (ERICOR), which is acquired by the comparison of the size distributions obtained by a Scanning Mobility Particle Sizer (SMPS) and an Optical Particle Counter (OPC). The agreement between the in situ and remote sensing instruments in this case was not satisfactory (R2 equal to 0.35). The last comparison for the selected days was between the aerosol extinction Ångström exponent acquired by in situ and remote sensing instruments. The correlation was not satisfactory (R2 equal to 0.4), probably due to differences in the number size distributions present in the air volumes measured by in situ and remote sensing instruments. We also present a day that a Saharan dust event occurred in Athens in order to demonstrate the information we obtain through the synergy of in situ and remote sensing instruments on how regional aerosol is added to local aerosol, especially during pollution events due to long range transport.Peer reviewe

A methodology for investigating dust model performance using synergistic EARLINET/AERONET dust concentration retrievals

Systematic measurements of dust concentration profiles at a continental scale were recently made possible by the development of synergistic retrieval algorithms using combined lidar and sun photometer data and the establishment of robust remote-sensing networks in the framework of Aerosols, Clouds, and Trace gases Research InfraStructure Network (ACTRIS)/European Aerosol Research Lidar Network (EARLINET). We present a methodology for using these capabilities as a tool for examining the performance of dust transport models. The methodology includes considerations for the selection of a suitable data set and appropriate metrics for the exploration of the results. The approach is demonstrated for four regional dust transport models (BSC-DREAM8b v2, NMMB/BSC-DUST, DREAMABOL, DREAM8-NMME-MACC) using dust observations performed at 10 ACTRIS/EARLINET stations. The observations, which include coincident multi-wavelength lidar and sun photometer measurements, were processed with the Lidar-Radiometer Inversion Code (LIRIC) to retrieve aerosol concentration profiles. The methodology proposed here shows advantages when compared to traditional evaluation techniques that utilize separately the available measurements such as separating the contribution of dust from other aerosol types on the lidar profiles and avoiding model assumptions related to the conversion of concentration fields to aerosol extinction values. When compared to LIRIC retrievals, the simulated dust vertical structures were found to be in good agreement for all models with correlation values between 0.5 and 0.7 in the 1–6 km range, where most dust is typically observed. The absolute dust concentration was typically underestimated with mean bias values of -40 to -20 μg m−3 at 2 km, the altitude of maximum mean concentration. The reported differences among the models found in this comparison indicate the benefit of the systematic use of the proposed approach in future dust model evaluation studies

A methodology for investigating dust model performance using synergistic EARLINET/AERONET dust concentration retrievals

Systematic measurements of dust concentration profiles at a continental scale were recently made possible by the development of synergistic retrieval algorithms using combined lidar and sun photometer data and the establishment of robust remote-sensing networks in the framework of Aerosols, Clouds, and Trace gases Research Infra-Structure Network (ACTRIS)/European Aerosol Research Lidar Network (EARLINET). We present a methodology for using these capabilities as a tool for examining the performance of dust transport models. The methodology includes considerations for the selection of a suitable data set and appropriate metrics for the exploration of the results. The approach is demonstrated for four regional dust transport models (BSC-DREAM8b v2, NMMB/BSC-DUST, DREAM-ABOL, DREAM8-NMME-MACC) using dust observations performed at 10 ACTRIS/EARLINET stations. The observations, which include coincident multi-wavelength lidar and sun photometer measurements, were processed with the Lidar-Radiometer Inversion Code (LIRIC) to retrieve aerosol concentration profiles. The methodology proposed here shows advantages when compared to traditional evaluation techniques that utilize separately the available measurements such as separating the contribution of dust from other aerosol types on the lidar profiles and avoiding model assumptions related to the conversion of concentration fields to aerosol extinction values. When compared to LIRIC retrievals, the simulated dust vertical structures were found to be in good agreement for all models with correlation values between 0.5 and 0.7 in the 1-6 km range, where most dust is typically observed. The absolute dust concentration was typically underestimated with mean bias values of -40 to -20 mu g m(-3) at 2 km, the altitude of maximum mean concentration. The reported differences among the models found in this comparison indicate the benefit of the systematic use of the proposed approach in future dust model evaluation studies

The impact of droplets’ clustering on the radar backscattering from water clouds

The goal of this thesis is to investigate the clustering effect of droplets in the water clouds. The initial motivation came from the incapability of millimeter-wave cloud radars to always detect low-level liquid water clouds. The observed discrepancy between the value of radar reflectivity factor estimated by radar measurements and the theoretical one predicted by the standard radar scattering theory set under question the applicability of the incoherent Rayleigh scattering assumption in the case of backscattering from water clouds. A possible reason for the violation of the assumption for incoherent Rayleigh scattering is the formation of clusters inside the cloud volume, which behave as coherent structures. Droplets’ clustering has been investigated through three cases: droplets inside the cloud volume totally correlated form one cluster; droplets inside the cloud volume partially correlated form more than one cluster; droplets inside the cloud volume completely uncorrelated do not form any cluster. The patterns of radar backscattered electric field were visualised in the complex plane by using the Random Walks approach which is a tool for studying the statistical properties of electromagnetic waves backscattered by objects containing few scattering centers. To simulate turbulence inside the cloud volume, four different types of motion for the clusters were introduced. Thus, the turbulence model allows the clusters to shift either horizontally, vertically, towards any direction in 3-dimensional space or rotate about a vertical central axis. The induced velocity and acceleration define clusters’ maximum displacement. The particular motions characterized by the maximum displacement, denote various length scales and intensities of turbulence. The impact of these motions on the pattern of backscattered electric field is investigated for the case of totally correlated droplets which form a single cluster. Low degrees of clustering are explored through the patterns of the radar backscattered electric field, but also from the distribution of power resulting from the coherent summation of the individual backscattered fields. The computational tests showed that the probability density function of coherent power backscattered by n=1, 2, 3 clusters is consistent with the probability density function of the distance from the origin in a single-, two-, three-step isotropic Pearson’s random walk over the two-dimensional phase space respectively. Clusters have been considered in alignment to X-axis, Y-axis, Z-axis or randomly arranged in the cloud volume. An analysis on the statistical properties of the backscattered electric field for different arrangements of clusters inside the cloud volume is presented. The computational results of this research showed that the clustering of droplets results in a radar response which deviates from the one predicted by the standard radar theory. The probability density function of the power backscattered by partially correlated droplets differs from the well-known exponential distribution of completely uncorrelated droplets in the absence of clustering. The systematic discrepancies between the mean coherent and incoherent power imply that radar reflectivity sensitivity of cloud radars may not be sufficient for detecting backscattered signals from water clouds.Remote Sensing of the EnvironmentTelecommunicationsElectrical Engineering, Mathematics and Computer Scienc

The operational cloud retrieval algorithms from TROPOMI on board Sentinel-5 Precursor

This paper presents the operational cloud retrieval algorithms for the TROPOspheric Monitoring Instrument (TROPOMI) on board the European Space Agency Sentinel-5 Precursor (S5P) mission scheduled for launch in 2017. Two algorithms working in tandem are used for retrieving cloud properties: OCRA (Optical Cloud Recognition Algorithm) and ROCINN (Retrieval of Cloud Information using Neural Networks). OCRA retrieves the cloud fraction using TROPOMI measurements in the ultraviolet (UV) and visible (VIS) spectral regions, and ROCINN retrieves the cloud top height (pressure) and optical thickness (albedo) using TROPOMI measurements in and around the oxygen A-band in the near infrared (NIR). Cloud parameters from TROPOMI/S5P will be used not only for enhancing the accuracy of trace gas retrievals but also for extending the satellite data record of cloud information derived from oxygen A-band measurements, a record initiated with the Global Ozone Monitoring Experiment (GOME) on board the second European Remote-Sensing Satellite (ERS-2) over 20 years ago. The OCRA and ROCINN algorithms are integrated in the S5P operational processor UPAS (Universal Processor for UV/VIS/NIR Atmospheric Spectrometers), and we present here UPAS cloud results using the Ozone Monitoring Instrument (OMI) and GOME-2 measurements. In addition, we examine anticipated challenges for the TROPOMI/S5P cloud retrieval algorithms, and we discuss the future validation needs for OCRA and ROCINN

CCN activity, variability and influence on droplet formation during the HygrA-Cd Campaign in Athens

Measurements of cloud condensation nuclei (CCN) concentrations (cm-3) at five levels of supersaturation between 0.2-1%, together with remote sensing profiling and aerosol size distributions, were performed at an urban background site of Athens during the Hygroscopic Aerosols to Cloud Droplets (HygrA-CD) campaign. The site is affected by local emissions and long-range transport, as portrayed by the aerosol size, hygroscopicity and mixing state. Application of a state-of-the-art droplet parameterization is used to link the observed size distribution measurements, bulk composition, and modeled boundary layer dynamics with potential supersaturation, droplet number, and sensitivity of these parameters for clouds forming above the site. The sensitivity is then used to understand the source of potential droplet number variability. We find that the importance of aerosol particle concentration levels associated with the background increases as vertical velocities increase. The updraft velocity variability was found to contribute 58-90% (68.6% on average) to the variance of the cloud droplet number, followed by the variance in aerosol number (6-32%, average 23.2%). Therefore, although local sources may strongly modulate CCN concentrations, their impact on droplet number is limited by the atmospheric dynamics expressed by the updraft velocity regime. © 2017 by the authors

Planetary boundary layer height variability over Athens, Greece, based on the synergy of Raman lidar and radiosonde data: application of the Kalman filter and other techniques (2011-2016)

The temporal evolution of the Planetary Boundary Layer height over Athens, Greece for a 5-year period (2011-2016) is presented. Using the EOLE Raman lidar system, the range-corrected lidar signals were selected around 12:00 UTC and 00:00 UTC for a total of 332 cases (165 days and 167 nights). The Kalman filter and other techniques were used to determine PBL height. The mean PBL height was found to be around 1617±324 m (12:00 UTC) and 892±130 m (00:00 UTC).Peer Reviewe

Planetary boundary layer height variability over Athens, Greece, based on the synergy of Raman lidar and radiosonde data: application of the Kalman filter and other techniques (2011-2016)

The temporal evolution of the Planetary Boundary Layer height over Athens, Greece for a 5-year period (2011-2016) is presented. Using the EOLE Raman lidar system, the range-corrected lidar signals were selected around 12:00 UTC and 00:00 UTC for a total of 332 cases (165 days and 167 nights). The Kalman filter and other techniques were used to determine PBL height. The mean PBL height was found to be around 1617±324 m (12:00 UTC) and 892±130 m (00:00 UTC).Peer Reviewe

An improved TROPOMI tropospheric NO2 research product over Europe

Launched in October 2017, the TROPOspheric Monitoring Instrument (TROPOMI) aboard Sentinel-5 Precursor provides the potential to monitor air quality over point sources across the globe with a spatial resolution as high as 5.5 km × 3.5 km (7 km × 3.5 km before 6 August 2019). The DLR nitrogen dioxide (NO2) retrieval algorithm for the TROPOMI instrument consists of three steps: the spectral fitting of the slant column, the separation of stratospheric and tropospheric contributions, and the conversion of the slant column to a vertical column using an air mass factor (AMF) calculation. In this work, an improved DLR tropospheric NO2 retrieval algorithm from TROPOMI measurements over Europe is presented. The stratospheric estimation is implemented using the STRatospheric Estimation Algorithm from Mainz (STREAM), which was developed as a verification algorithm for TROPOMI and does not require chemistry transport model data as input. A directionally dependent STREAM (DSTREAM) is developed to correct for the dependency of the stratospheric NO2 on the viewing geometry by up to 2×10^14 molec/cm2. Applied to synthetic TROPOMI data, the uncertainty in the stratospheric column is 3.5×10^14 molec/cm2 in the case of significant tropospheric sources. Applied to actual measurements, the smooth variation of stratospheric NO2 at low latitudes is conserved, and stronger stratospheric variation at higher latitudes is captured. For AMF calculation, the climatological surface albedo data are replaced by geometry-dependent effective Lambertian equivalent reflectivity (GE_LER) obtained directly from TROPOMI measurements with a high spatial resolution. Mesoscale-resolution a priori NO2 profiles are obtained from the regional POLYPHEMUS/DLR chemistry transport model with the TNO-MACC emission inventory. Based on the latest TROPOMI operational cloud parameters, a more realistic cloud treatment is provided by a Clouds-As-Layers (CAL) model, which treats the clouds as uniform layers of water droplets, instead of the Clouds-As-Reflecting-Boundaries (CRB) model, in which clouds are simplified as Lambertian reflectors. For the error analysis, the tropospheric AMF uncertainty, which is the largest source of NO2 uncertainty for polluted scenarios, ranges between 20 % and 50 %, leading to a total uncertainty in the tropospheric NO2 column in the 30 %–60 % range. From a validation performed with ground-based multi-axis differential optical absorption spectroscopy (MAX-DOAS) measurements, the new DLR tropospheric NO2 data show good correlations for nine European urban/suburban stations, with an average correlation coefficient of 0.78. The implementation of the algorithm improvements leads to a decrease of the relative difference from −55.3 % to −34.7 % on average in comparison with the DLR reference retrieval. When the satellite averaging kernels are used to remove the contribution of a priori profile shape, the relative difference decreases further to  −20 %