Articles | Volume 15, issue 1
https://doi.org/10.5194/amt-15-149-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-15-149-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
06 Jan 2022
Research article |
| 06 Jan 2022
| 06 Jan 2022
A Bayesian parametric approach to the retrieval of the atmospheric number size distribution from lidar data
Alberto Sorrentino
CORRESPONDING AUTHOR
Dipartimento di Matematica, Università di Genova, Genova, Italy
Alessia Sannino
Dipartimento di Fisica, Università di Napoli Federico II, Napoli, Italy
Nicola Spinelli
Dipartimento di Fisica, Università di Napoli Federico II, Napoli, Italy
Michele Piana
Dipartimento di Matematica, Università di Genova, Genova, Italy
Antonella Boselli
CNR-IMAA, Potenza, Italy
Valentino Tontodonato
ALA Srl Advanced Lidar Applications, Napoli, Italy
Pasquale Castellano
ALA Srl Advanced Lidar Applications, Napoli, Italy
Xuan Wang
CNR-SPIN, Napoli, Italy
Related authors
No articles found.
Holger Baars, Albert Ansmann, Kevin Ohneiser, Moritz Haarig, Ronny Engelmann, Dietrich Althausen, Ingrid Hanssen, Michael Gausa, Aleksander Pietruczuk, Artur Szkop, Iwona S. Stachlewska, Dongxiang Wang, Jens Reichardt, Annett Skupin, Ina Mattis, Thomas Trickl, Hannes Vogelmann, Francisco Navas-Guzmán, Alexander Haefele, Karen Acheson, Albert A. Ruth, Boyan Tatarov, Detlef Müller, Qiaoyun Hu, Thierry Podvin, Philippe Goloub, Igor Veselovskii, Christophe Pietras, Martial Haeffelin, Patrick Fréville, Michaël Sicard, Adolfo Comerón, Alfonso Javier Fernández García, Francisco Molero Menéndez, Carmen Córdoba-Jabonero, Juan Luis Guerrero-Rascado, Lucas Alados-Arboledas, Daniele Bortoli, Maria João Costa, Davide Dionisi, Gian Luigi Liberti, Xuan Wang, Alessia Sannino, Nikolaos Papagiannopoulos, Antonella Boselli, Lucia Mona, Giuseppe D'Amico, Salvatore Romano, Maria Rita Perrone, Livio Belegante, Doina Nicolae, Ivan Grigorov, Anna Gialitaki, Vassilis Amiridis, Ourania Soupiona, Alexandros Papayannis, Rodanthi-Elisaveth Mamouri, Argyro Nisantzi, Birgit Heese, Julian Hofer, Yoav Y. Schechner, Ulla Wandinger, and Gelsomina Pappalardo
Atmos. Chem. Phys., 19, 15183–15198, https://doi.org/10.5194/acp-19-15183-2019, https://doi.org/10.5194/acp-19-15183-2019, 2019
Related subject area
Subject: Aerosols | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
The CALIPSO version 4.5 stratospheric aerosol subtyping algorithm
Volcanic cloud detection using Sentinel-3 satellite data by means of neural networks: the Raikoke 2019 eruption test case
The new MISR research aerosol retrieval algorithm: a multi-angle, multi-spectral, bounded-variable least squares retrieval of aerosol particle properties over both land and water
Algorithm for vertical distribution of boundary layer aerosol components in remote-sensing data
Atmospheric visibility inferred from continuous-wave Doppler wind lidar
Identification of smoke and sulfuric acid aerosol in SAGE III/ISS extinction spectra
Combining Mie–Raman and fluorescence observations: a step forward in aerosol classification with lidar technology
Effective uncertainty quantification for multi-angle polarimetric aerosol remote sensing over ocean
Employing relaxed smoothness constraints on imaginary part of refractive index in AERONET aerosol retrieval algorithm
Observation of bioaerosol transport using wideband integrated bioaerosol sensor and coherent Doppler lidar
Retrieval of UVB aerosol extinction profiles from the ground-based Langley Mobile Ozone Lidar (LMOL) system
Enhancing MAX-DOAS atmospheric state retrievals by multispectral polarimetry – studies using synthetic data
Assessing the benefits of Imaging Infrared Radiometer observations for the CALIOP version 4 cloud and aerosol discrimination algorithm
A semi-automated procedure for the emitter–receiver geometry characterization of motor-controlled lidars
Aerosol optical characteristics in the urban area of Rome, Italy, and their impact on the UV index
Aerosol models from the AERONET database: application to surface reflectance validation
Continuous mapping of fine particulate matter (PM2.5) air quality in East Asia at daily 6 × 6 km2 resolution by application of a random forest algorithm to 2011–2019 GOCI geostationary satellite data
Deep-learning-based post-process correction of the aerosol parameters in the high-resolution Sentinel-3 Level-2 Synergy product
Retrieval of UV–visible aerosol absorption using AERONET and OMI–MODIS synergy: spatial and temporal variability across major aerosol environments
Estimating cloud condensation nuclei concentrations from CALIPSO lidar measurements
Ash particle refractive index model for simulating the brightness temperature spectrum of volcanic ash clouds from satellite infrared sounder measurements
Retrieval of aerosol properties using relative radiance measurements from an all-sky camera
Optimization of Aeolus' aerosol optical properties by maximum-likelihood estimation
Biomass burning aerosol heating rates from the ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) 2016 and 2017 experiments
Aeolus L2A aerosol optical properties product: standard correct algorithm and Mie correct algorithm
Methodology to obtain highly resolved SO2 vertical profiles for representation of volcanic emissions in climate models
Inferring the absorption properties of organic aerosol in Siberian biomass burning plumes from remote optical observations
Mass concentration estimates of long-range-transported Canadian biomass burning aerosols from a multi-wavelength Raman polarization lidar and a ceilometer in Finland
Retrievals of dust-related particle mass and ice-nucleating particle concentration profiles with ground-based polarization lidar and sun photometer over a megacity in central China
Introducing the MISR level 2 near real-time aerosol product
Estimation of PM2.5 concentration in China using linear hybrid machine learning model
Species correlation measurements in turbulent flare plumes: considerations for field measurements
Retrieval of aerosol microphysical properties from atmospheric lidar sounding: an investigation using synthetic measurements and data from the ACEPOL campaign
Integration of GOCI and AHI Yonsei aerosol optical depth products during the 2016 KORUS-AQ and 2018 EMeRGe campaigns
Deriving boundary layer height from aerosol lidar using machine learning: KABL and ADABL algorithms
Efficient multi-angle polarimetric inversion of aerosols and ocean color powered by a deep neural network forward model
Quantitative comparison of measured and simulated O4 absorptions for one day with extremely low aerosol load over the tropical Atlantic
A Dark Target research aerosol algorithm for MODIS observations over eastern China: increasing coverage while maintaining accuracy at high aerosol loading
Optimal use of the Prede POM sky radiometer for aerosol, water vapor, and ozone retrievals
Analysis of simultaneous aerosol and ocean glint retrieval using multi-angle observations
Model-enforced post-process correction of satellite aerosol retrievals
Explicit and consistent aerosol correction for visible wavelength satellite cloud and nitrogen dioxide retrievals based on optical properties from a global aerosol analysis
Reducing cloud contamination in aerosol optical depth (AOD) measurements
Synergy processing of diverse ground-based remote sensing and in situ data using the GRASP algorithm: applications to radiometer, lidar and radiosonde observations
Retrieval of stratospheric aerosol size distribution parameters using satellite solar occultation measurements at three wavelengths
Relative sky radiance from multi-exposure all-sky camera images
An uncertainty-based protocol for the setup and measurement of soot–black carbon emissions from gas flares using sky-LOSA
A new measurement approach for validating satellite-based above-cloud aerosol optical depth
OMPS LP Version 2.0 multi-wavelength aerosol extinction coefficient retrieval algorithm
Simulated reflectance above snow constrained by airborne measurements of solar radiation: implications for the snow grain morphology in the Arctic
Jason L. Tackett, Jayanta Kar, Mark A. Vaughan, Brian J. Getzewich, Man-Hae Kim, Jean-Paul Vernier, Ali H. Omar, Brian E. Magill, Michael C. Pitts, and David M. Winker
Atmos. Meas. Tech., 16, 745–768, https://doi.org/10.5194/amt-16-745-2023, https://doi.org/10.5194/amt-16-745-2023, 2023
Short summary
Short summary
The accurate identification of aerosol types in the stratosphere is important to characterize their impacts on the Earth climate system. The space-borne lidar on board CALIPSO is well-posed to identify aerosols in the stratosphere from volcanic eruptions and major wildfire events. This paper describes improvements implemented in the version 4.5 CALIPSO data release to more accurately discriminate between volcanic ash, sulfate, and smoke within the stratosphere.
Ilaria Petracca, Davide De Santis, Matteo Picchiani, Stefano Corradini, Lorenzo Guerrieri, Fred Prata, Luca Merucci, Dario Stelitano, Fabio Del Frate, Giorgia Salvucci, and Giovanni Schiavon
Atmos. Meas. Tech., 15, 7195–7210, https://doi.org/10.5194/amt-15-7195-2022, https://doi.org/10.5194/amt-15-7195-2022, 2022
Short summary
Short summary
The authors propose a near-real-time procedure for the detection of volcanic clouds by means of Sentinel-3 satellite data and neural networks. The algorithm results in an automatic image classification where ashy pixels are distinguished from other surfaces with remarkable accuracy. The model is considerably faster if compared to other approaches which are time consuming, case specific, and not automatic. The algorithm can be significantly helpful for emergency management during eruption events.
James A. Limbacher, Ralph A. Kahn, and Jaehwa Lee
Atmos. Meas. Tech., 15, 6865–6887, https://doi.org/10.5194/amt-15-6865-2022, https://doi.org/10.5194/amt-15-6865-2022, 2022
Short summary
Short summary
Launched in December 1999, NASA’s Multi-angle Imaging SpectroRadiometer (MISR) has given researchers qualitative constraints on aerosol particle properties for the past 22 years. Here, we present a new MISR research aerosol retrieval algorithm (RA) that utilizes over-land surface reflectance data from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) to address limitations of the MISR operational aerosol retrieval algorithm and improve retrievals of aerosol particle properties.
Futing Wang, Ting Yang, Zifa Wang, Haibo Wang, Xi Chen, Yele Sun, Jianjun Li, Guigang Tang, and Wenxuan Chai
Atmos. Meas. Tech., 15, 6127–6144, https://doi.org/10.5194/amt-15-6127-2022, https://doi.org/10.5194/amt-15-6127-2022, 2022
Short summary
Short summary
We develop a new algorithm to get the vertical mass concentration profiles of fine aerosol components based on the synergy of ground-based remote sensing for the first time. The comparisons with in situ observations and chemistry transport models validate the performance of the algorithm. Uncertainties caused by input parameters are also assessed in this paper. We expected that the algorithm can provide a new idea for lidar inversion and promote the development of aerosol component profiles.
Manuel Queißer, Michael Harris, and Steven Knoop
Atmos. Meas. Tech., 15, 5527–5544, https://doi.org/10.5194/amt-15-5527-2022, https://doi.org/10.5194/amt-15-5527-2022, 2022
Short summary
Short summary
Visibility is how well we can see something. Visibility sensors, such as employed in meteorological observatories and airports, measure at a point at the instrument location, which may not be representative of visibilities further away, e.g. near the sea surface during sea spray. Light detecting and ranging (lidar) can measure visibility further away. We find wind lidar to be a viable tool to measure visibility with low accuracy, which could suffice for safety-uncritical applications.
Travis N. Knepp, Larry Thomason, Mahesh Kovilakam, Jason Tackett, Jayanta Kar, Robert Damadeo, and David Flittner
Atmos. Meas. Tech., 15, 5235–5260, https://doi.org/10.5194/amt-15-5235-2022, https://doi.org/10.5194/amt-15-5235-2022, 2022
Short summary
Short summary
We used aerosol profiles from the SAGE III/ISS instrument to develop an aerosol classification method that was tested on four case-study events (two volcanic, two fire) and supported with CALIOP aerosol products. The method worked well in identifying smoke and volcanic aerosol in the stratosphere for these events. Raikoke is presented as a demonstration of the limitations of this method.
Igor Veselovskii, Qiaoyun Hu, Philippe Goloub, Thierry Podvin, Boris Barchunov, and Mikhail Korenskii
Atmos. Meas. Tech., 15, 4881–4900, https://doi.org/10.5194/amt-15-4881-2022, https://doi.org/10.5194/amt-15-4881-2022, 2022
Short summary
Short summary
An approach to reveal variability in aerosol type at a high spatiotemporal resolution, by combining fluorescence and Mie–Raman lidar data, is presented. We applied this new classification scheme to lidar data obtained by LOA, University of Lille, in 2020–2021. It is demonstrated that the separation of the main particle types, such as smoke, dust, pollen, and urban, can be performed with a height resolution of 60 m and temporal resolution better than 10 min for the current lidar configuration.
Meng Gao, Kirk Knobelspiesse, Bryan A. Franz, Peng-Wang Zhai, Andrew M. Sayer, Amir Ibrahim, Brian Cairns, Otto Hasekamp, Yongxiang Hu, Vanderlei Martins, P. Jeremy Werdell, and Xiaoguang Xu
Atmos. Meas. Tech., 15, 4859–4879, https://doi.org/10.5194/amt-15-4859-2022, https://doi.org/10.5194/amt-15-4859-2022, 2022
Short summary
Short summary
In this work, we assessed the pixel-wise retrieval uncertainties on aerosol and ocean color derived from multi-angle polarimetric measurements. Standard error propagation methods are used to compute the uncertainties. A flexible framework is proposed to evaluate how representative these uncertainties are compared with real retrieval errors. Meanwhile, to assist operational data processing, we optimized the computational speed to evaluate the retrieval uncertainties based on neural networks.
Alexander Sinyuk, Brent N. Holben, Thomas F. Eck, David M. Giles, Ilya Slutsker, Oleg Dubovik, Joel S. Schafer, Alexander Smirnov, and Mikhail Sorokin
Atmos. Meas. Tech., 15, 4135–4151, https://doi.org/10.5194/amt-15-4135-2022, https://doi.org/10.5194/amt-15-4135-2022, 2022
Short summary
Short summary
This paper describes modification of smoothness constraints on the imaginary part of the refractive index employed in the AERONET aerosol retrieval algorithm. This modification is termed relaxed due to the weaker strength of this new smoothness constraint. Applying the modified version of the smoothness constraint results in a significant reduction of retrieved light absorption by brown-carbon-containing aerosols.
Dawei Tang, Tianwen Wei, Jinlong Yuan, Haiyun Xia, and Xiankang Dou
Atmos. Meas. Tech., 15, 2819–2838, https://doi.org/10.5194/amt-15-2819-2022, https://doi.org/10.5194/amt-15-2819-2022, 2022
Short summary
Short summary
During 11–20 March 2020, three aerosol transport events were investigated by a lidar system and an online bioaerosol detection system in Hefei, China.
Observation results reveal that the events not only contributed to high particulate matter pollution but also to the transport of external bioaerosols, resulting in changes in the fraction of fluorescent biological aerosol particles.
This detection method improved the time resolution and provided more parameters for aerosol detection.
Liqiao Lei, Timothy A. Berkoff, Guillaume Gronoff, Jia Su, Amin R. Nehrir, Yonghua Wu, Fred Moshary, and Shi Kuang
Atmos. Meas. Tech., 15, 2465–2478, https://doi.org/10.5194/amt-15-2465-2022, https://doi.org/10.5194/amt-15-2465-2022, 2022
Short summary
Short summary
Aerosol extinction in the UVB (280–315 nm) is difficult to retrieve using simple lidar techniques due to the lack of lidar ratios at those wavelengths. The 2018 Long Island Sound Tropospheric Ozone Study (LISTOS) in the New York City region provided the opportunity to characterize the lidar ratio for UVB aerosol retrieval for the Langley Mobile Ozone Lidar (LMOL). A 292 nm aerosol product comparison between the NASA Langley High Altitude Lidar Observatory (HALO) and LMOL was also carried out.
Jan-Lukas Tirpitz, Udo Frieß, Robert Spurr, and Ulrich Platt
Atmos. Meas. Tech., 15, 2077–2098, https://doi.org/10.5194/amt-15-2077-2022, https://doi.org/10.5194/amt-15-2077-2022, 2022
Short summary
Short summary
MAX-DOAS is a widely used measurement technique for the remote detection of atmospheric aerosol and trace gases. It relies on the analysis of ultra-violet and visible radiation spectra of skylight. To date, information contained in the skylight's polarisation state has not been utilised. On the basis of synthetic data, we carried out sensitivity analyses to assess the potential of polarimetry for MAX-DOAS applications.
Thibault Vaillant de Guélis, Gérard Ancellet, Anne Garnier, Laurent C.-Labonnote, Jacques Pelon, Mark A. Vaughan, Zhaoyan Liu, and David M. Winker
Atmos. Meas. Tech., 15, 1931–1956, https://doi.org/10.5194/amt-15-1931-2022, https://doi.org/10.5194/amt-15-1931-2022, 2022
Short summary
Short summary
A new IIR-based cloud and aerosol discrimination (CAD) algorithm is developed using the IIR brightness temperature differences for cloud and aerosol features confidently identified by the CALIOP version 4 CAD algorithm. IIR classifications agree with the majority of V4 cloud identifications, reduce the ambiguity in a notable fraction of
not confidentV4 cloud classifications, and correct a few V4 misclassifications of cloud layers identified as dense dust or elevated smoke layers by CALIOP.
Marco Di Paolantonio, Davide Dionisi, and Gian Luigi Liberti
Atmos. Meas. Tech., 15, 1217–1231, https://doi.org/10.5194/amt-15-1217-2022, https://doi.org/10.5194/amt-15-1217-2022, 2022
Short summary
Short summary
A procedure for the characterization of the lidar transmitter–receiver geometry was developed. This characterization is currently implemented in the Rome RMR lidar to optimize the telescope/beam alignment, retrieve the overlap function, and estimate the absolute and relative tilt of the laser beam. This procedure can be potentially used to complement the standard EARLINET quality assurance tests.
Monica Campanelli, Henri Diémoz, Anna Maria Siani, Alcide di Sarra, Anna Maria Iannarelli, Rei Kudo, Gabriele Fasano, Giampietro Casasanta, Luca Tofful, Marco Cacciani, Paolo Sanò, and Stefano Dietrich
Atmos. Meas. Tech., 15, 1171–1183, https://doi.org/10.5194/amt-15-1171-2022, https://doi.org/10.5194/amt-15-1171-2022, 2022
Short summary
Short summary
The aerosol optical depth (AOD) characteristics in an urban area of Rome were retrieved over a period of 11 years (2010–2020) to determine, for the first time, their effect on the incoming ultraviolet (UV) solar radiation. The surface forcing efficiency shows that the AOD is the primary parameter affecting the surface irradiance in Rome, and it is found to be greater for smaller zenith angles and for larger and more absorbing particles in the UV range (such as, e.g., mineral dust).
Jean-Claude Roger, Eric Vermote, Sergii Skakun, Emilie Murphy, Oleg Dubovik, Natacha Kalecinski, Bruno Korgo, and Brent Holben
Atmos. Meas. Tech., 15, 1123–1144, https://doi.org/10.5194/amt-15-1123-2022, https://doi.org/10.5194/amt-15-1123-2022, 2022
Short summary
Short summary
From measurements of the sky performed by AERONET, we determined the microphysical properties of the atmospheric particles (aerosols) for each AERONET site. We used the aerosol optical thickness and its variation over the visible spectrum. This allows us to determine an aerosol model useful for (but not only) the validation of the surface reflectance satellite-derived product. The impact of the aerosol model uncertainties on the surface reflectance validation has been found to be 1 % to 3 %.
Drew C. Pendergrass, Shixian Zhai, Jhoon Kim, Ja-Ho Koo, Seoyoung Lee, Minah Bae, Soontae Kim, Hong Liao, and Daniel J. Jacob
Atmos. Meas. Tech., 15, 1075–1091, https://doi.org/10.5194/amt-15-1075-2022, https://doi.org/10.5194/amt-15-1075-2022, 2022
Short summary
Short summary
This paper uses a machine learning algorithm to infer high-resolution maps of particulate air quality in eastern China, Japan, and the Korean peninsula, using data from a geostationary satellite along with meteorology. We then perform an extensive evaluation of this inferred air quality and use it to diagnose trends in the region. We hope this paper and the associated data will be valuable to other scientists interested in epidemiology, air quality, remote sensing, and machine learning.
Antti Lipponen, Jaakko Reinvall, Arttu Väisänen, Henri Taskinen, Timo Lähivaara, Larisa Sogacheva, Pekka Kolmonen, Kari Lehtinen, Antti Arola, and Ville Kolehmainen
Atmos. Meas. Tech., 15, 895–914, https://doi.org/10.5194/amt-15-895-2022, https://doi.org/10.5194/amt-15-895-2022, 2022
Short summary
Short summary
We have developed a machine-learning-based model that can be used to correct the Sentinel-3 satellite-based aerosol parameter data of the Synergy data product. The strength of the model is that the original satellite data processing does not have to be carried out again but the correction can be carried out with the data already available. We show that the correction significantly improves the accuracy of the satellite aerosol parameters.
Vinay Kayetha, Omar Torres, and Hiren Jethva
Atmos. Meas. Tech., 15, 845–877, https://doi.org/10.5194/amt-15-845-2022, https://doi.org/10.5194/amt-15-845-2022, 2022
Short summary
Short summary
Existing measurements of spectral aerosol absorption are limited, particularly in the UV region. We use the synergy of satellite and ground measurements to derive spectral single scattering albedo of aerosols from the UV–visible spectrum. The resulting spectral SSAs are used to investigate seasonality in absorption for carbonaceous, dust, and urban aerosols. Regional aerosol absorption models that could be used to make reliable assumptions in satellite remote sensing of aerosols are derived.
Goutam Choudhury and Matthias Tesche
Atmos. Meas. Tech., 15, 639–654, https://doi.org/10.5194/amt-15-639-2022, https://doi.org/10.5194/amt-15-639-2022, 2022
Short summary
Short summary
Aerosols are tiny particles suspended in the atmosphere. A fraction of these particles can form clouds and are called cloud condensation nuclei (CCN). Measurements of such aerosol particles are necessary to study the aerosol–cloud interactions and reduce the uncertainty in our future climate predictions. We present a novel methodology to estimate global 3D CCN concentrations from the CALIPSO satellite measurements. The final data set will be used to study the aerosol–cloud interactions.
Hiroshi Ishimoto, Masahiro Hayashi, and Yuzo Mano
Atmos. Meas. Tech., 15, 435–458, https://doi.org/10.5194/amt-15-435-2022, https://doi.org/10.5194/amt-15-435-2022, 2022
Short summary
Short summary
Using data from the Infrared Atmospheric Sounding Interferometer (IASI) measurements of volcanic ash clouds (VACs) and radiative transfer calculations, we attempt to simulate the measured brightness temperature spectra (BTS) of volcanic ash aerosols in the infrared region. In particular, the dependence on the ash refractive index (RI) model is investigated.
Roberto Román, Juan C. Antuña-Sánchez, Victoria E. Cachorro, Carlos Toledano, Benjamín Torres, David Mateos, David Fuertes, César López, Ramiro González, Tatyana Lapionok, Marcos Herreras-Giralda, Oleg Dubovik, and Ángel M. de Frutos
Atmos. Meas. Tech., 15, 407–433, https://doi.org/10.5194/amt-15-407-2022, https://doi.org/10.5194/amt-15-407-2022, 2022
Short summary
Short summary
An all-sky camera is used to obtain the relative sky radiance, and this radiance is used as input in an inversion code to obtain aerosol properties. This paper is really interesting because it pushes forward the use and capability of sky cameras for more advanced science purposes. Enhanced aerosol properties can be retrieved with accuracy using only an all-sky camera, but synergy with other instruments providing aerosol optical depth could even increase the power of these low-cost instruments.
Frithjof Ehlers, Thomas Flament, Alain Dabas, Dimitri Trapon, Adrien Lacour, Holger Baars, and Anne Grete Straume-Lindner
Atmos. Meas. Tech., 15, 185–203, https://doi.org/10.5194/amt-15-185-2022, https://doi.org/10.5194/amt-15-185-2022, 2022
Short summary
Short summary
The Aeolus satellite observes the Earth and can vertically detect any kind of particles (aerosols or clouds) in the atmosphere below it. These observations are typically very noisy, which needs to be accounted for. This work dampens the noise in Aeolus' aerosol and cloud data, which are provided publicly by the ESA, so that the scientific community can make better use of it. This makes the data potentially more useful for weather prediction and climate research.
Sabrina P. Cochrane, K. Sebastian Schmidt, Hong Chen, Peter Pilewskie, Scott Kittelman, Jens Redemann, Samuel LeBlanc, Kristina Pistone, Michal Segal Rozenhaimer, Meloë Kacenelenbogen, Yohei Shinozuka, Connor Flynn, Rich Ferrare, Sharon Burton, Chris Hostetler, Marc Mallet, and Paquita Zuidema
Atmos. Meas. Tech., 15, 61–77, https://doi.org/10.5194/amt-15-61-2022, https://doi.org/10.5194/amt-15-61-2022, 2022
Short summary
Short summary
This work presents heating rates derived from aircraft observations from the 2016 and 2017 field campaigns of ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS). We separate the total heating rates into aerosol and gas (primarily water vapor) absorption and explore some of the co-variability of heating rate profiles and their primary drivers, leading to the development of a new concept: the heating rate efficiency (HRE; the heating rate per unit aerosol extinction).
Thomas Flament, Dimitri Trapon, Adrien Lacour, Alain Dabas, Frithjof Ehlers, and Dorit Huber
Atmos. Meas. Tech., 14, 7851–7871, https://doi.org/10.5194/amt-14-7851-2021, https://doi.org/10.5194/amt-14-7851-2021, 2021
Short summary
Short summary
This paper presents the main algorithms of the Aeolus Level 2 aerosol optical properties product. The processing chain was developed under contract with ESA.
We show that the ALADIN instrument, although primarily designed to retrieve atmospheric winds, is also able to provide valuable information about aerosol and cloud optical properties. The algorithms are detailed, and validation on simulated and real examples is shown.
Oscar S. Sandvik, Johan Friberg, Moa K. Sporre, and Bengt G. Martinsson
Atmos. Meas. Tech., 14, 7153–7165, https://doi.org/10.5194/amt-14-7153-2021, https://doi.org/10.5194/amt-14-7153-2021, 2021
Short summary
Short summary
A method to form SO2 profiles in the stratosphere with high vertical resolution following volcanic eruptions is introduced. The method combines space-based high-resolution vertical aerosol profiles and SO2 measurements the first 2 weeks after an eruption with air mass trajectory analyses. The SO2 is located at higher altitude than in most previous studies. The detailed resolution of the SO2 profile is unprecedented compared to other methods.
Igor B. Konovalov, Nikolai A. Golovushkin, Matthias Beekmann, Mikhail V. Panchenko, and Meinrat O. Andreae
Atmos. Meas. Tech., 14, 6647–6673, https://doi.org/10.5194/amt-14-6647-2021, https://doi.org/10.5194/amt-14-6647-2021, 2021
Short summary
Short summary
The absorption of solar light by organic matter, known as brown carbon (BrC), contributes significantly to the radiative budget of the Earth’s atmosphere, but its representation in atmospheric models is uncertain. This paper advances a methodology to constrain model parameters characterizing BrC absorption of atmospheric aerosol originating from biomass burning with the available remote ground-based observations of atmospheric aerosol.
Xiaoxia Shang, Tero Mielonen, Antti Lipponen, Elina Giannakaki, Ari Leskinen, Virginie Buchard, Anton S. Darmenov, Antti Kukkurainen, Antti Arola, Ewan O'Connor, Anne Hirsikko, and Mika Komppula
Atmos. Meas. Tech., 14, 6159–6179, https://doi.org/10.5194/amt-14-6159-2021, https://doi.org/10.5194/amt-14-6159-2021, 2021
Short summary
Short summary
The long-range-transported smoke particles from a Canadian wildfire event were observed with a multi-wavelength Raman polarization lidar and a ceilometer over Kuopio, Finland, in June 2019. The optical properties and the mass concentration estimations were reported for such aged smoke aerosols over northern Europe.
Yun He, Yunfei Zhang, Fuchao Liu, Zhenping Yin, Yang Yi, Yifan Zhan, and Fan Yi
Atmos. Meas. Tech., 14, 5939–5954, https://doi.org/10.5194/amt-14-5939-2021, https://doi.org/10.5194/amt-14-5939-2021, 2021
Short summary
Short summary
The POLIPHON method can retrieve the height profiles of dust-related particle mass and ice-nucleating particle (INP) concentrations. Applying a dust case data set screening scheme based on the lidar-derived depolarization ratio (rather than Ångström exponent for 440–870 nm and AOD at 532 nm), the mixed-dust-related conversion factors are retrieved from sun photometer observations over Wuhan, China. This method may potentially be extended to regions influenced by mixed dust.
Marcin L. Witek, Michael J. Garay, David J. Diner, Michael A. Bull, Felix C. Seidel, Abigail M. Nastan, and Earl G. Hansen
Atmos. Meas. Tech., 14, 5577–5591, https://doi.org/10.5194/amt-14-5577-2021, https://doi.org/10.5194/amt-14-5577-2021, 2021
Short summary
Short summary
This article documents the development and testing of a new near real-time (NRT) aerosol product from the MISR instrument on NASA’s Terra platform. The NRT product capitalizes on the unique attributes of the MISR retrieval approach, which leads to a high-quality and reliable aerosol data product. Several modifications are described that allow for rapid product generation within a 3 h window following acquisition. Implications for the product quality and consistency are discussed.
Zhihao Song, Bin Chen, Yue Huang, Li Dong, and Tingting Yang
Atmos. Meas. Tech., 14, 5333–5347, https://doi.org/10.5194/amt-14-5333-2021, https://doi.org/10.5194/amt-14-5333-2021, 2021
Short summary
Short summary
The linear hybrid machine learning model achieves the expected target well. The overall inversion accuracy (R2) of the model is 0.84, and the RMSE is 12.92 µg m−3. R2 was above 0.7 in more than 70 % of the sites, whereas RMSE and mean absolute error were below 20 and 15 µg m−3, respectively. There was severe pollution in winter with an average PM2.5 concentration of 62.10 µg m−3. However, there was only slight pollution in summer with an average PM2.5 concentration of 47.39 µg m−3.
Scott P. Seymour and Matthew R. Johnson
Atmos. Meas. Tech., 14, 5179–5197, https://doi.org/10.5194/amt-14-5179-2021, https://doi.org/10.5194/amt-14-5179-2021, 2021
Short summary
Short summary
Field measurements of gas flare emissions often assume that combustion species are spatially and temporally correlated in the plume. By measuring black carbon (BC) and water vapour in turbulent lab-scale flare plumes, this study shows that the well-correlated species assumption is not universally valid and that field measurements may be subject to large added uncertainty. Further analysis suggests that this uncertainty is easily avoided, and initial guidance is provided on sampling protocols.
William G. K. McLean, Guangliang Fu, Sharon P. Burton, and Otto P. Hasekamp
Atmos. Meas. Tech., 14, 4755–4771, https://doi.org/10.5194/amt-14-4755-2021, https://doi.org/10.5194/amt-14-4755-2021, 2021
Short summary
Short summary
In this study, we present results from aerosol retrievals using both synthetic and real lidar datasets, including measurements from the ACEPOL (Aerosol Characterization from Polarimeter and Lidar) campaign, a combined initiative between NASA and SRON (the Netherlands Institute for Space Research). Aerosol microphysical retrievals were performed using the High Spectral Resolution Lidar-2 (HSRL-2) setup, alongside several others, with the ACEPOL retrievals also compared to polarimeter retrievals.
Hyunkwang Lim, Sujung Go, Jhoon Kim, Myungje Choi, Seoyoung Lee, Chang-Keun Song, and Yasuko Kasai
Atmos. Meas. Tech., 14, 4575–4592, https://doi.org/10.5194/amt-14-4575-2021, https://doi.org/10.5194/amt-14-4575-2021, 2021
Short summary
Short summary
Aerosol property observations by satellites from geostationary Earth orbit (GEO) in particular have advantages of frequent sampling better than 1 h in addition to broader spatial coverage. This study provides data fusion products of aerosol optical properties from four different algorithms for two different GEO satellites: GOCI and AHI. The fused aerosol products adopted ensemble-mean and maximum-likelihood estimation methods. The data fusion provides improved results with better accuracy.
Thomas Rieutord, Sylvain Aubert, and Tiago Machado
Atmos. Meas. Tech., 14, 4335–4353, https://doi.org/10.5194/amt-14-4335-2021, https://doi.org/10.5194/amt-14-4335-2021, 2021
Short summary
Short summary
This article describes two methods to estimate the height of the very first layer of the atmosphere. It is measured with aerosol lidars, and the two new methods are based on machine learning. Both are open source and available under free licenses. A sensitivity analysis and a 2-year evaluation against meteorological balloons were carried out. One method has a good agreement with balloons but is limited by training, and the other has less good agreement with balloons but is more flexible.
Meng Gao, Bryan A. Franz, Kirk Knobelspiesse, Peng-Wang Zhai, Vanderlei Martins, Sharon Burton, Brian Cairns, Richard Ferrare, Joel Gales, Otto Hasekamp, Yongxiang Hu, Amir Ibrahim, Brent McBride, Anin Puthukkudy, P. Jeremy Werdell, and Xiaoguang Xu
Atmos. Meas. Tech., 14, 4083–4110, https://doi.org/10.5194/amt-14-4083-2021, https://doi.org/10.5194/amt-14-4083-2021, 2021
Short summary
Short summary
Multi-angle polarimetric measurements can retrieve accurate aerosol properties over complex atmosphere and ocean systems; however, most retrieval algorithms require high computational costs. We propose a deep neural network (NN) forward model to represent the radiative transfer simulation of coupled atmosphere and ocean systems and then conduct simultaneous aerosol and ocean color retrievals on AirHARP measurements. The computational acceleration is 103 times with CPU or 104 times with GPU.
Thomas Wagner, Steffen Dörner, Steffen Beirle, Sebastian Donner, and Stefan Kinne
Atmos. Meas. Tech., 14, 3871–3893, https://doi.org/10.5194/amt-14-3871-2021, https://doi.org/10.5194/amt-14-3871-2021, 2021
Short summary
Short summary
We compare measured and simulated O4 absorptions for conditions of extremely low aerosol optical depth, for which the uncertainties related to imperfect knowledge of aerosol properties do not significantly affect the comparison results. The simulations underestimate the measurements by 15 % to 20 %. Even if no aerosols are considered, the simulated O4 absorptions are systematically lower than the measurements. Our results indicate a fundamental inconsistency between simulations and measurements.
Yingxi R. Shi, Robert C. Levy, Leiku Yang, Lorraine A. Remer, Shana Mattoo, and Oleg Dubovik
Atmos. Meas. Tech., 14, 3449–3468, https://doi.org/10.5194/amt-14-3449-2021, https://doi.org/10.5194/amt-14-3449-2021, 2021
Short summary
Short summary
Due to fast industrialization and development, China has been experiencing haze pollution episodes with both high frequencies and severity over the last 3 decades. This study improves the accuracy and data coverage of measured aerosol from satellites, which help quantify, characterize, and understand the impact of the haze phenomena over the entire East Asia region.
Rei Kudo, Henri Diémoz, Victor Estellés, Monica Campanelli, Masahiro Momoi, Franco Marenco, Claire L. Ryder, Osamu Ijima, Akihiro Uchiyama, Kouichi Nakashima, Akihiro Yamazaki, Ryoji Nagasawa, Nozomu Ohkawara, and Haruma Ishida
Atmos. Meas. Tech., 14, 3395–3426, https://doi.org/10.5194/amt-14-3395-2021, https://doi.org/10.5194/amt-14-3395-2021, 2021
Short summary
Short summary
A new method, Skyrad pack MRI version 2, was developed to retrieve aerosol physical and optical properties, water vapor, and ozone column concentrations from the sky radiometer, a filter radiometer deployed in the SKYNET international network. Our method showed good performance in a radiative closure study using surface solar irradiances from the Baseline Surface Radiation Network and a comparison using aircraft in situ measurements of Saharan dust events during the SAVEX-D 2015 campaign.
Kirk Knobelspiesse, Amir Ibrahim, Bryan Franz, Sean Bailey, Robert Levy, Ziauddin Ahmad, Joel Gales, Meng Gao, Michael Garay, Samuel Anderson, and Olga Kalashnikova
Atmos. Meas. Tech., 14, 3233–3252, https://doi.org/10.5194/amt-14-3233-2021, https://doi.org/10.5194/amt-14-3233-2021, 2021
Short summary
Short summary
We assessed atmospheric aerosol and ocean surface wind speed remote sensing capability with NASA's Multi-angle Imaging SpectroRadiometer (MISR), using synthetic data and a Bayesian inference technique called generalized nonlinear retrieval analysis (GENRA). We found success using three aerosol parameters plus wind speed. This shows that MISR can perform an atmospheric correction for the Moderate Resolution Imaging Spectroradiometer (MODIS) on the same spacecraft (Terra).
Antti Lipponen, Ville Kolehmainen, Pekka Kolmonen, Antti Kukkurainen, Tero Mielonen, Neus Sabater, Larisa Sogacheva, Timo H. Virtanen, and Antti Arola
Atmos. Meas. Tech., 14, 2981–2992, https://doi.org/10.5194/amt-14-2981-2021, https://doi.org/10.5194/amt-14-2981-2021, 2021
Short summary
Short summary
We have developed a new computational method to post-process-correct the satellite aerosol retrievals. The proposed method combines the conventional satellite aerosol retrievals relying on physics-based models and machine learning. The results show significantly improved accuracy in the aerosol data over the operational satellite data products. The correction can be applied to the existing satellite aerosol datasets with no need to fully reprocess the much larger original radiance data.
Alexander Vasilkov, Nickolay Krotkov, Eun-Su Yang, Lok Lamsal, Joanna Joiner, Patricia Castellanos, Zachary Fasnacht, and Robert Spurr
Atmos. Meas. Tech., 14, 2857–2871, https://doi.org/10.5194/amt-14-2857-2021, https://doi.org/10.5194/amt-14-2857-2021, 2021
Short summary
Short summary
To explicitly account for aerosol effects in the OMI cloud and nitrogen dioxide algorithms, we use a model of aerosol optical properties from a global aerosol assimilation system and radiative transfer computations. Accounting for anisotropic reflection of Earth's surface is an important feature of the approach. Comparisons of the cloud and tropospheric nitrogen dioxide retrievals with implicit and explicit aerosol corrections are carried out for a selected area with high pollution.
Verena Schenzinger and Axel Kreuter
Atmos. Meas. Tech., 14, 2787–2798, https://doi.org/10.5194/amt-14-2787-2021, https://doi.org/10.5194/amt-14-2787-2021, 2021
Short summary
Short summary
When measuring the aerosol optical depth of the atmosphere, clouds in front of the sun lead to erroneously high values. Therefore, measurements that are potentially affected by clouds need to be removed from the dataset by an automatic process. As the currently used algorithm cannot reliably identify thin clouds, we developed a new one based on a method borrowed from machine learning. Tests with 10 years of data show improved performance of the new routine and therefore higher data quality.
Anton Lopatin, Oleg Dubovik, David Fuertes, Georgiy Stenchikov, Tatyana Lapyonok, Igor Veselovskii, Frank G. Wienhold, Illia Shevchenko, Qiaoyun Hu, and Sagar Parajuli
Atmos. Meas. Tech., 14, 2575–2614, https://doi.org/10.5194/amt-14-2575-2021, https://doi.org/10.5194/amt-14-2575-2021, 2021
Short summary
Short summary
The article presents novelties in characterizing fine particles suspended in the air by means of combining various measurements that observe light propagation in atmosphere. Several non-coincident observations (some of which require sunlight, while others work only at night) could be united under the assumption that aerosol properties do not change drastically at nighttime. It also proposes how to describe particles' composition in a simplified manner that uses new types of observations.
Felix Wrana, Christian von Savigny, Jacob Zalach, and Larry W. Thomason
Atmos. Meas. Tech., 14, 2345–2357, https://doi.org/10.5194/amt-14-2345-2021, https://doi.org/10.5194/amt-14-2345-2021, 2021
Short summary
Short summary
In this paper, we describe a new method for calculating the size of naturally occurring droplets (aerosols) made mostly of sulfuric acid and water that can be found roughly at 20 km altitude in the atmosphere. We use data from the instrument SAGE III/ISS that is mounted on the International Space Station. We show that our method works well, and that the size parameters we calculate are reasonable and can be a valuable addition for a better understanding of aerosols and their effect on climate.
Juan C. Antuña-Sánchez, Roberto Román, Victoria E. Cachorro, Carlos Toledano, César López, Ramiro González, David Mateos, Abel Calle, and Ángel M. de Frutos
Atmos. Meas. Tech., 14, 2201–2217, https://doi.org/10.5194/amt-14-2201-2021, https://doi.org/10.5194/amt-14-2201-2021, 2021
Short summary
Short summary
This paper presents a new technique to exploit the potential of all-sky cameras. The sky radiance at three effective wavelengths is calculated and compared with alternative measurements and simulated data. The proposed method will be useful for the retrieval of aerosol and cloud properties.
Bradley M. Conrad and Matthew R. Johnson
Atmos. Meas. Tech., 14, 1573–1591, https://doi.org/10.5194/amt-14-1573-2021, https://doi.org/10.5194/amt-14-1573-2021, 2021
Short summary
Short summary
A general uncertainty analysis (GUA) is performed for the sky-LOSA technique used to remotely measure soot emissions from gas flares. GUA data are compiled in an open-source software tool to help sky-LOSA users select critical setup and acquisition parameters while giving quantitative visual feedback on anticipated uncertainties for a specific measurement. The software tool enables easy acquisition of optimal measurement data, significantly increasing the accessibility of the sky-LOSA technique.
Charles K. Gatebe, Hiren Jethva, Ritesh Gautam, Rajesh Poudyal, and Tamás Várnai
Atmos. Meas. Tech., 14, 1405–1423, https://doi.org/10.5194/amt-14-1405-2021, https://doi.org/10.5194/amt-14-1405-2021, 2021
Short summary
Short summary
The retrieval of aerosol parameters from passive satellite instruments in cloudy scenes is very challenging, partly because clouds and cloud-related processes significantly modify the aerosol properties and the 3D radiative effects. This study shows simultaneous retrieval of above-cloud aerosol optical depth and aerosol-corrected cloud optical depth from airborne measurements, thereby demonstrating a novel approach for assessing satellite retrievals of aerosols above clouds.
Ghassan Taha, Robert Loughman, Tong Zhu, Larry Thomason, Jayanta Kar, Landon Rieger, and Adam Bourassa
Atmos. Meas. Tech., 14, 1015–1036, https://doi.org/10.5194/amt-14-1015-2021, https://doi.org/10.5194/amt-14-1015-2021, 2021
Short summary
Short summary
This work describes the newly released OMPS LP aerosol extinction profile multi-wavelength Version 2.0 algorithm and dataset. It is shown that the V2.0 aerosols exhibit significant improvements in OMPS LP retrieval performance in the Southern Hemisphere and at lower altitudes. The new product is compared to the SAGE III/ISS, OSIRIS and CALIPSO missions and shown to be of good quality and suitable for scientific studies.
Soheila Jafariserajehlou, Vladimir V. Rozanov, Marco Vountas, Charles K. Gatebe, and John P. Burrows
Atmos. Meas. Tech., 14, 369–389, https://doi.org/10.5194/amt-14-369-2021, https://doi.org/10.5194/amt-14-369-2021, 2021
Short summary
Short summary
In this work, we study retrieval of snow grain morphologies and their impact on the reflectance in a coupled snow–atmosphere system. We present a sensitivity study to highlight the importance of having adequate information about snow and atmosphere. A novel two-stage algorithm for retrieving the size and shape of snow grains is presented. The reflectance simulation results are compared to that of airborne measurements; high correlations of 0.98 at IR and 0.88–0.98 at VIS are achieved.
Cited articles
Alados-Arboledas, L., Müller, D., Guerrero-Rascado, J., Navas-Guzmán,
F., Pérez-Ramírez, D., and Olmo, F.: Optical and microphysical
properties of fresh biomass burning aerosol retrieved by Raman lidar, and
star-and sun-photometry, Geophys. Res. Lett., 38, L01807, https://doi.org/10.1029/2010GL045999, 2011. a, b
Ansmann, A., Riebesell, M., and Weitkamp, C.: Measurement of atmospheric
aerosol extinction profiles with a Raman lidar, Opt. Lett., 15, 746–748,
1990. a
Ansmann, A., Riebesell, M., Wandinger, U., Weitkamp, C., Voss, E., Lahmann, W.,
and Michaelis, W.: Combined Raman elastic-backscatter lidar for vertical
profiling of moisture, aerosol extinction, backscatter, and lidar ratio,
Appl. Phys. B-Lasers O., 55, 18–28, 1992. a
Balis, D., Giannakaki, E., Müller, D., Amiridis, V., Kelektsoglou, K.,
Rapsomanikis, S., and Bais, A.: Estimation of the microphysical aerosol
properties over Thessaloniki, Greece, during the SCOUT-O3 campaign with the
synergy of Raman lidar and Sun photometer data, J. Geophys.
Res.-Atmos., 115, D08202, https://doi.org/10.1029/2009JD013088, 2010. a
Böckmann, C.: Hybrid regularization method for the ill-posed inversion of
multiwavelength lidar data in the retrieval of aerosol size distributions,
Appl. Optics, 40, 1329–1342, 2001. a
Böckmann, C., Mironova, I., Müller, D., Schneidenbach, L., and Nessler,
R.: Microphysical aerosol parameters from multiwavelength lidar, J. Opt. Soc. Am. A, 22,
518–528, 2005. a
Burton, S. P., Chemyakin, E., Liu, X., Knobelspiesse, K., Stamnes, S., Sawamura, P., Moore, R. H., Hostetler, C. A., and Ferrare, R. A.: Information content and sensitivity of the 3β + 2α lidar measurement system for aerosol microphysical retrievals, Atmos. Meas. Tech., 9, 5555–5574, https://doi.org/10.5194/amt-9-5555-2016, 2016. a
Chazette, P.: Aerosol optical properties as observed from an ultralight aircraft over the Strait of Gibraltar, Atmos. Meas. Tech., 13, 4461–4477, https://doi.org/10.5194/amt-13-4461-2020, 2020. a
Chemyakin, E., Müller, D., Burton, S., Kolgotin, A., Hostetler, C., and
Ferrare, R.: Arrange and average algorithm for the retrieval of aerosol
parameters from multiwavelength high-spectral-resolution lidar/Raman lidar
data, Appl. Optics, 53, 7252–7266, 2014. a
Chemyakin, E., Burton, S., Kolgotin, A., Müller, D., Hostetler, C., and
Ferrare, R.: Retrieval of aerosol parameters from multiwavelength lidar:
investigation of the underlying inverse mathematical problem, Appl. Optics,
55, 2188–2202, 2016. a
De Rosa, B., Di Girolamo, P., and Summa, D.: Determination of aerosol size and
microphiysical proprierties based on multi-wavelength raman lidar
measurements in the framework of HyMeX-SOP1, in: EGU General Assembly
Conference Abstracts, May 2020, p. 17218, 2020. a
Denevi, G., Garbarino, S., and Sorrentino, A.: Iterative algorithms for a
non-linear inverse problem in atmospheric lidar, Inverse Probl., 33,
085010, https://doi.org/10.1088/1361-6420/aa7904, 2017. a
Di, H., Wang, Q., Hua, H., Li, S., Yan, Q., Liu, J., Song, Y., and Hua, D.:
Aerosol microphysical particle parameter inversion and error analysis based
on remote sensing data, Remote Sensing, 10, 1753, https://doi.org/10.3390/rs10111753, 2018a. a, b
Di, H., Zhao, J., Zhao, X., Zhang, Y., Wang, Z., Wang, X., Wang, Y., Zhao, H.,
and Hua, D.: Parameterization of aerosol number concentration distributions
from aircraft measurements in the lower troposphere over Northern China,
J. Quant. Spectrosc. Ra., 218, 46–53,
2018b. a
Di Girolamo, P., Summa, D., Bhawar, R., Di Iorio, T., Cacciani, M.,
Veselovskii, I., Dubovik, O., and Kolgotin, A.: Raman lidar observations of a
Saharan dust outbreak event: characterization of the dust optical properties
and determination of particle size and microphysical parameters, Atmos.
Environ., 50, 66–78, 2012. a
Dubovik, O., Sinyuk, A., Lapyonok, T., Holben, B. N., Mishchenko, M., Yang, P.,
Eck, T. F., Volten, H., Munoz, O., Veihelmann, B., van der Zande, W. J., Leon, J.-F., Sorokin, M., and Slutsker, I.: Application of
spheroid models to account for aerosol particle nonsphericity in remote
sensing of desert dust, J. Geophys. Res.-Atmos., 111, D11208, https://doi.org/10.1029/2005JD006619,
2006. a
Garbarino, S., Sorrentino, A., Massone, A. M., Sannino, A., Boselli, A., Wang,
X., Spinelli, N., and Piana, M.: Expectation maximization and the retrieval
of the atmospheric extinction coefficients by inversion of Raman lidar data,
Opt. Express, 24, 21497–21511, 2016. a
Giannakaki, E., Balis, D. S., Amiridis, V., and Zerefos, C.: Optical properties of different aerosol types: seven years of combined Raman-elastic backscatter lidar measurements in Thessaloniki, Greece, Atmos. Meas. Tech., 3, 569–578, https://doi.org/10.5194/amt-3-569-2010, 2010. a
Giannakaki, E., van Zyl, P. G., Müller, D., Balis, D., and Komppula, M.: Optical and microphysical characterization of aerosol layers over South Africa by means of multi-wavelength depolarization and Raman lidar measurements, Atmos. Chem. Phys., 16, 8109–8123, https://doi.org/10.5194/acp-16-8109-2016, 2016. a
Granados-Muñoz, M., Guerrero-Rascado, J., Bravo-Aranda, J.,
Navas-Guzmán, F., Valenzuela, A., Lyamani, H., Chaikovsky, A., Wandinger,
U., Ansmann, A., Dubovik, O., Grudo, J. O., and Alados-Arboledas, L.: Retrieving aerosol microphysical
properties by Lidar-Radiometer Inversion Code (LIRIC) for different aerosol
types, J. Geophys. Res.-Atmos., 119, 4836–4858, 2014. a
Holben, B. N., Eck, T. F., Slutsker, I. a., Tanre, D., Buis, J., Setzer, A.,
Vermote, E., Reagan, J. A., Kaufman, Y., Nakajima, T., Lavenu, F., Jankowiak, I., and Smirnov, A.: AERONET – A
federated instrument network and data archive for aerosol characterization,
Remote Sens. Environ., 66, 1–16, https://doi.org/10.1016/S0034-4257(98)00031-5, 1998. a
Kolgotin, A. and Müller, D.: Theory of inversion with two-dimensional
regularization: profiles of microphysical particle properties derived from
multiwavelength lidar measurements, Appl. Optics, 47, 4472–4490, 2008. a
Kolgotin, A., Müller, D., Chemyakin, E., and Romanov, A.: Improved
identification of the solution space of aerosol microphysical properties
derived from the inversion of profiles of lidar optical data, part 1: theory,
Appl. Optics, 55, 9839–9849, 2016. a
Lee, K. H. and Wong, M. S.: Vertical Profiling of Aerosol Optical Properties
From LIDAR Remote Sensing, Surface Visibility, and Columnar Extinction
Measurements, in: Remote Sensing of Aerosols, Clouds, and Precipitation, pp.
23–43, Elsevier, https://doi.org/10.1016/B978-0-12-810437-8.00002-5, 2018. a
Luria, G., Duran, D., Visani, E., Sommariva, S., Rotondi, F., Sebastiano,
D. R., Panzica, F., Piana, M., and Sorrentino, A.: Bayesian multi-dipole
modelling in the frequency domain, J. Neurosci. Meth., 312,
27–36, 2019. a
McLean, W. G. K., Fu, G., Burton, S. P., and Hasekamp, O. P.: Retrieval of aerosol microphysical properties from atmospheric lidar sounding: an investigation using synthetic measurements and data from the ACEPOL campaign, Atmos. Meas. Tech., 14, 4755–4771, https://doi.org/10.5194/amt-14-4755-2021, 2021. a
Molero, F., Pujadas, M., and Artíñano, B.: Study of the Effect of
Aerosol Vertical Profile on Microphysical Properties Using GRASP Code with
Sun/Sky Photometer and Multiwavelength Lidar Measurements, Remote Sensing,
12, 4072, https://doi.org/10.3390/rs12244072, 2020. a
Müller, D., Wandinger, U., Althausen, D., Mattis, I., and Ansmann, A.:
Retrieval of physical particle properties from lidar observations of
extinction and backscatter at multiple wavelengths, Appl. Optics, 37,
2260–2263, 1998. a
Müller, D., Wandinger, U., and Ansmann, A.: Microphysical particle
parameters from extinction and backscatter lidar data by inversion with
regularization: theory, Appl. Optics, 38, 2346–2357, 1999. a
Müller, D., Wandinger, U., Althausen, D., and Fiebig, M.: Comprehensive
particle characterization from three-wavelength Raman-lidar observations:
case study, Appl. Optics, 40, 4863–4869, 2001. a
Müller, D., Franke, K., Ansmann, A., Althausen, D., and Wagner, F.:
Indo-Asian pollution during INDOEX: Microphysical particle properties and
single-scattering albedo inferred from multiwavelength lidar observations,
J. Geophys. Res.-Atmos., 108, 4600, https://doi.org/10.1029/2003JD003538, 2003. a
Müller, D., Tesche, M., Eichler, H., Engelmann, R., Althausen, D., Ansmann,
A., Cheng, Y., Zhang, Y., and Hu, M.: Strong particle light absorption over
the Pearl River Delta (south China) and Beijing (north China) determined from
combined Raman lidar and Sun photometer observations, Geophys. Res.
Lett., 33, L20811, https://doi.org/10.1029/2006GL027196, 2006. a
Müller, D., Böckmann, C., Kolgotin, A., Schneidenbach, L., Chemyakin, E., Rosemann, J., Znak, P., and Romanov, A.: Microphysical particle properties derived from inversion algorithms developed in the framework of EARLINET, Atmos. Meas. Tech., 9, 5007–5035, https://doi.org/10.5194/amt-9-5007-2016, 2016. a
Murayama, T., Müller, D., Wada, K., Shimizu, A., Sekiguchi, M., and
Tsukamoto, T.: Characterization of Asian dust and Siberian smoke with
multi-wavelength Raman lidar over Tokyo, Japan in spring 2003, Geophys.
Res. Lett., 31, L23103, https://doi.org/10.1029/2004GL021105, 2004. a
NASA: Aeronet, NASA [data set], available at: https://aeronet.gsfc.nasa.gov/, last access: August 2020. a
Navas-Guzman, F., Bravo-Aranda, J. A., Guerrero-Rascado, J. L.,
Granados-Munoz, M. J., and Alados-Arboledas, L.: Statistical analysis of aerosol
optical properties retrieved by Raman lidar over Southeastern Spain, Tellus
B, 65, 21234, https://doi.org/10.3402/tellusb.v65i0.21234, 2013. a
Nicolae, D., Nemuc, A., Müller, D., Talianu, C., Vasilescu, J., Belegante,
L., and Kolgotin, A.: Characterization of fresh and aged biomass burning
events using multiwavelength Raman lidar and mass spectrometry, J.
Geophys. Res.-Atmos., 118, 2956–2965, 2013. a
Noh, Y. M., Müller, D., Shin, D. H., Lee, H., Jung, J. S., Lee, K. H.,
Cribb, M., Li, Z., and Kim, Y. J.: Optical and microphysical properties of
severe haze and smoke aerosol measured by integrated remote sensing
techniques in Gwangju, Korea, Atmos. Environ., 43, 879–888, 2009. a
Ortiz-Amezcua, P., Guerrero-Rascado, J. L., Granados-Muñoz, M. J., Benavent-Oltra, J. A., Böckmann, C., Samaras, S., Stachlewska, I. S., Janicka, Ł., Baars, H., Bohlmann, S., and Alados-Arboledas, L.: Microphysical characterization of long-range transported biomass burning particles from North America at three EARLINET stations, Atmos. Chem. Phys., 17, 5931–5946, https://doi.org/10.5194/acp-17-5931-2017, 2017. a
Osterloh, L., Böckmann, C., Mamouri, R., and Papayannis, A.: An adaptive
base point algorithm for the retrieval of aerosol microphysical properties,
Open Atmospheric Science Journal, 5, 61–73, 2011. a
Pérez-Ramírez, D., Whiteman, D. N., Veselovskii, I., Kolgotin, A., Korenskiy, M., and Alados-Arboledas, L.: Effects of systematic and random errors on the retrieval of particle microphysical properties from multiwavelength lidar measurements using inversion with regularization, Atmos. Meas. Tech., 6, 3039–3054, https://doi.org/10.5194/amt-6-3039-2013, 2013. a
Pérez-Ramírez, D., Whiteman, D. N., Veselovskii, I., Colarco, P.,
Korenski, M., and da Silva, A.: Retrievals of aerosol single scattering
albedo by multiwavelength lidar measurements: Evaluations with NASA Langley
HSRL-2 during discover-AQ field campaigns, Remote Sens. Environ.,
222, 144–164, 2019. a
Pérez-Ramírez, D., Whiteman, D. N., Veselovskii, I., Korenski, M.,
Colarco, P. R., and da Silva, A. M.: Optimized profile retrievals of aerosol
microphysical properties from simulated spaceborne multiwavelength lidar,
J. Quant. Spectrosc. Ra., 246, 106932, https://doi.org/10.1016/j.jqsrt.2020.106932,
2020. a
Pornsawad, P., Böckmann, C., Ritter, C., and Rafler, M.: Ill-posed
retrieval of aerosol extinction coefficient profiles from Raman lidar data by
regularization, Appl. Optics, 47, 1649–1661, 2008. a
Ritter, C., Angeles Burgos, M., Böckmann, C., Mateos, D., Lisok, J.,
Markowicz, K., Moroni, B., Cappelletti, D., Udisti, R., Maturilli, M.,
et al.: Microphysical properties and radiative impact of an intense biomass
burning aerosol event measured over Ny-Ålesund, Spitsbergen in July 2015,
Tellus B, 70, 1–23, https://doi.org/10.1080/16000889.2018.1539618, 2018. a
Samaras, S., Nicolae, D., Böckmann, C., Vasilescu, J., Binietoglou, I.,
Labzovskii, L., Toanca, F., and Papayannis, A.: Using Raman-lidar-based
regularized microphysical retrievals and Aerosol Mass Spectrometer
measurements for the characterization of biomass burning aerosols, J.
Comput. Phys., 299, 156–174, 2015. a
Sawamura, P., Müller, D., Hoff, R. M., Hostetler, C. A., Ferrare, R. A., Hair, J. W., Rogers, R. R., Anderson, B. E., Ziemba, L. D., Beyersdorf, A. J., Thornhill, K. L., Winstead, E. L., and Holben, B. N.: Aerosol optical and microphysical retrievals from a hybrid multiwavelength lidar data set – DISCOVER-AQ 2011, Atmos. Meas. Tech., 7, 3095–3112, https://doi.org/10.5194/amt-7-3095-2014, 2014. a, b
Sciacchitano, F., Lugaro, S., and Sorrentino, A.: Sparse bayesian imaging of
solar flares, SIAM J. Imaging Sci., 12, 319–343, 2019. a
Shcherbakov, V.: Regularized algorithm for Raman lidar data processing, Appl.
Optics, 46, 4879–4889, 2007. a
Sorrentino, A., Luria, G., and Aramini, R.: Bayesian multi-dipole modelling of
a single topography in MEG by adaptive sequential Monte Carlo samplers,
Inverse Probl., 30, 045010, https://doi.org/10.1088/0266-5611/30/4/045010, 2014. a, b
Stelitano, D., Di Girolamo, P., Scoccione, A., Summa, D., and Cacciani, M.: Characterization of atmospheric aerosol optical properties based on the combined use of a ground-based Raman lidar and an airborne optical particle counter in the framework of the Hydrological Cycle in the Mediterranean Experiment – Special Observation Period 1, Atmos. Meas. Tech., 12, 2183–2199, https://doi.org/10.5194/amt-12-2183-2019, 2019. a
Sun, X., Yin, Y., Sun, Y., Sun, Y., Liu, W., and Han, Y.: Seasonal and vertical
variations in aerosol distribution over Shijiazhuang, China, Atmos.
Environ., 81, 245–252, 2013. a
Tesche, M., Müller, D., Ansmann, A., Hu, M., and Zhang, Y.: Retrieval of
microphysical properties of aerosol particles from one-wavelength Raman lidar
and multiwavelength Sun photometer observations, Atmos. Environ., 42,
6398–6404, 2008. a
Tesche, M., Kolgotin, A., Haarig, M., Burton, S. P., Ferrare, R. A., Hostetler, C. A., and Müller, D.:
Veselovskii, I., Kolgotin, A., Griaznov, V., Müller, D., Wandinger, U., and
Whiteman, D. N.: Inversion with regularization for the retrieval of
tropospheric aerosol parameters from multiwavelength lidar sounding, Appl.
Optics, 41, 3685–3699, 2002. a
Veselovskii, I., Kolgotin, A., Griaznov, V., Müller, D., Franke, K., and
Whiteman, D. N.: Inversion of multiwavelength Raman lidar data for retrieval
of bimodal aerosol size distribution, Appl. Optics, 43, 1180–1195, 2004. a
Veselovskii, I., Whiteman, D., Kolgotin, A., Andrews, E., and Korenskii, M.:
Demonstration of aerosol property profiling by multiwavelength lidar under
varying relative humidity conditions, J. Atmos. Ocean.
Tech., 26, 1543–1557, 2009. a
Veselovskii, I., Whiteman, D. N., Korenskiy, M., Kolgotin, A., Dubovik, O., Perez-Ramirez, D., and Suvorina, A.: Retrieval of spatio-temporal distributions of particle parameters from multiwavelength lidar measurements using the linear estimation technique and comparison with AERONET, Atmos. Meas. Tech., 6, 2671–2682, https://doi.org/10.5194/amt-6-2671-2013, 2013. a
Viani, A., Luria, G., Bornfleth, H., and Sorrentino, A.: Where Bayes tweaks
Gauss: Conditionally Gaussian priors for stable multi-dipole estimation,
Inverse Probl. Imag., 15, 1099–1119, https://doi.org/10.3934/ipi.2021030, 2021. a
Wagner, R., Ajtai, T., Kandler, K., Lieke, K., Linke, C., Müller, T., Schnaiter, M., and Vragel, M.: Complex refractive indices of Saharan dust samples at visible and near UV wavelengths: a laboratory study, Atmos. Chem. Phys., 12, 2491–2512, https://doi.org/10.5194/acp-12-2491-2012, 2012.
a
Wandinger, U., Müller, D., Böckmann, C., Althausen, D., Matthias, V.,
Bösenberg, J., Weiß, V., Fiebig, M., Wendisch, M., Stohl, A., and Ansmann, A.:
Optical and microphysical characterization of biomass-burning and
industrial-pollution aerosols from-multiwavelength lidar and aircraft
measurements, J. Geophys. Res.-Atmos., 107, 8125, https://doi.org/10.1029/2000JD000202,
2002. a
Weitkamp, C.: Lidar: Range-resolved optical remote sensing of the atmosphere,
vol. 102, Springer Science & Business, ISBN 0387400753, 2006. a
Whiteman, D. N., Pérez-Ramírez, D., Veselovskii, I., Colarco, P., and
Buchard, V.: Retrievals of aerosol microphysics from simulations of
spaceborne multiwavelength lidar measurements, J. Quant.
Spectrosc. Ra., 205, 27–39, 2018. a
Short summary
We present a novel approach that can be used to obtain microphysical properties of atmospheric aerosol, up to several kilometers in the atmosphere, from lidar measurements taken from the ground. Our approach provides accurate reconstructions under many different experimental conditions. Our results can contribute to the expansion of the use of remote sensing techniques for air quality monitoring and atmospheric science in general.
We present a novel approach that can be used to obtain microphysical properties of atmospheric...
