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A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Atmospheric Techniques, Instruments, and Modeling".

Deadline for manuscript submissions: closed (25 October 2020) | Viewed by 24634

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Special Issue Editor

Dr. John E. Yorks

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Guest Editor

NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
Interests: lidar remote sensing; lidar systems development; cloud radiative effects; aerosol distributions; aerosol transport; cirrus properties

Special Issue Information

Dear Colleagues,

Aerosols have several effects on the Earth’s radiation budget yet remain a significant uncertainty in predicting the future climate system. In addition to their impact on the Earth’s climate system, aerosols from volcanic eruptions, wildfires, anthropogenic pollution events, and dust storms are hazardous to human health and transportation safety. Aerosol vertical distribution is essential to improving our understanding of (1) aerosol impacts on the climate system (aerosol–radiation interactions and aerosol–cloud interactions), (2) aerosol emission, transport, and deposition, and (3) the surface concentration of particulate matter (PM). Lidar vertical profile measurements from space-based platforms, aircraft, and ground networks provide the science community with the aerosol vertical structure that is necessary to complement passive aerosol retrievals and examine aerosol impacts on climate and air quality.

The objective of this Special Issue is to highlight new and exciting aerosol research results in the field of active lidar remote sensing. More specifically, the Special Issue aims to address the following topics:

Recent advances in lidar technologies that enable a better understanding of aerosol distributions and radiative properties;
Novel techniques that show the potential of combining lidar observations with passive remote sensing observations for studying aerosols;
Original scientific results from analysis of lidar data, alone or in synergy with other observations or models, which cover aerosol applications such as aerosol distributions and lifecycle (emissions, transport, deposition), aerosol type, air quality, aerosol absorption, aerosol radiative effects, and upper troposphere–lower stratosphere (UTLS) aerosols.

Dr. John E. Yorks
Guest Editor

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Keywords

lidar
remote sensing
aerosol distributions
air quality
aerosol radiative effects

Published Papers (7 papers)

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Research

27 pages, 4702 KiB

Open AccessEditor’s ChoiceArticle

Aerosol and Cloud Detection Using Machine Learning Algorithms and Space-Based Lidar Data

by John E. Yorks, Patrick A. Selmer, Andrew Kupchock, Edward P. Nowottnick, Kenneth E. Christian, Daniel Rusinek, Natasha Dacic and Matthew J. McGill

Atmosphere 2021, 12(5), 606; https://0-doi-org.brum.beds.ac.uk/10.3390/atmos12050606 - 07 May 2021

Cited by 18 | Viewed by 5247

Abstract

Clouds and aerosols play a significant role in determining the overall atmospheric radiation budget, yet remain a key uncertainty in understanding and predicting the future climate system. In addition to their impact on the Earth’s climate system, aerosols from volcanic eruptions, wildfires, man-made pollution events and dust storms are hazardous to aviation safety and human health. Space-based lidar systems provide critical information about the vertical distributions of clouds and aerosols that greatly improve our understanding of the climate system. However, daytime data from backscatter lidars, such as the Cloud-Aerosol Transport System (CATS) on the International Space Station (ISS), must be averaged during science processing at the expense of spatial resolution to obtain sufficient signal-to-noise ratio (SNR) for accurately detecting atmospheric features. For example, 50% of all atmospheric features reported in daytime operational CATS data products require averaging to 60 km for detection. Furthermore, the single-wavelength nature of the CATS primary operation mode makes accurately typing these features challenging in complex scenes. This paper presents machine learning (ML) techniques that, when applied to CATS data, (1) increased the 1064 nm SNR by 75%, (2) increased the number of layers detected (any resolution) by 30%, and (3) enabled detection of 40% more atmospheric features during daytime operations at a horizontal resolution of 5 km compared to the 60 km horizontal resolution often required for daytime CATS operational data products. A Convolutional Neural Network (CNN) trained using CATS standard data products also demonstrated the potential for improved cloud-aerosol discrimination compared to the operational CATS algorithms for cloud edges and complex near-surface scenes during daytime. Full article

(This article belongs to the Special Issue Lidar Remote Sensing Techniques for Atmospheric Aerosols)

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12 pages, 6956 KiB

Open AccessArticle

Optical Characteristics and Radiative Properties of Aerosols in Harbin, Heilongjiang Province during 2017

by Jiemei Liu, Wenxiang Shen, Yuan Yuan and Shikui Dong

Atmosphere 2021, 12(4), 463; https://0-doi-org.brum.beds.ac.uk/10.3390/atmos12040463 - 07 Apr 2021

Viewed by 1922

Abstract

This study considers aerosol optical properties and direct radiative forcing over Harbin (126.63° E, 45.75° N), the highest latitude city in Northeast China, during 2017. Observations based on the CE-318 sun-photometer show that the annual mean values of the aerosol optical depth (AOD) [...] Read more.

0.26 \pm 0.20

and

1.36 \pm 0.26

. Aerosol loading is the highest in the spring followed by winter, and the lowest loading is in autumn.

{AE}_{440 - 870}

is the highest in summer, second highest in winter, and lowest in autumn. The Santa Barbara DISORT Atmospheric Radiative Transfer (SBDART) model is used to estimate the shortwave aerosol radiative forcing at the top of the atmosphere, on the Earth’s surface and in the atmosphere, and the annual mean values are

- 16.36 \pm 18.42 {Wm}^{- 2}

- 71.01 \pm 27.37 {Wm}^{- 2}

and

54.65 \pm 30.62 {Wm}^{- 2}

, respectively, which indicate that aerosols cause climate effects of cooling the earth-atmosphere system, cooling the earth’s surface and heating the atmosphere. Four main aerosol types in Harbin are classified via AOD and AE. Specifically, clean continental, mixed type, biomass burning and urban industry, and desert dust aerosols accounted for 51%, 38%, 9%, and 2% of the total, respectively. Aerosol radiative forcing varies greatly in different seasons, and the aerosol load and type from different emission sources have an important influence on the seasonal variation of radiative forcing. Full article

(This article belongs to the Special Issue Lidar Remote Sensing Techniques for Atmospheric Aerosols)

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15 pages, 6396 KiB

Open AccessArticle

ATmospheric LIDar (ATLID): Pre-Launch Testing and Calibration of the European Space Agency Instrument That Will Measure Aerosols and Thin Clouds in the Atmosphere

by João Pereira do Carmo, Geraud de Villele, Kotska Wallace, Alain Lefebvre, Kaustav Ghose, Thomas Kanitz, François Chassat, Bertrand Corselle, Thomas Belhadj and Paolo Bravetti

Atmosphere 2021, 12(1), 76; https://0-doi-org.brum.beds.ac.uk/10.3390/atmos12010076 - 06 Jan 2021

Cited by 19 | Viewed by 3950

Abstract

ATLID (ATmospheric LIDar) is the atmospheric backscatter Light Detection and Ranging (LIDAR) instrument on board of the Earth Cloud, Aerosol and Radiation Explorer (EarthCARE) mission, the sixth Earth Explorer Mission of the European Space Agency (ESA) Living Planet Programme. ATLID’s purpose is to provide vertical profiles of optically thin cloud and aerosol layers, as well as the altitude of cloud boundaries, with a resolution of 100 m for altitudes of 0 to 20 km, and a resolution of 500 m for 20 km to 40 km. In order to achieve this objective ATLID emits short duration laser pulses in the ultraviolet, at a repetition rate of 51 Hz, while pointing in a near nadir direction along track of the satellite trajectory. The atmospheric backscatter signal is then collected by its 620 mm aperture telescope, filtered through the optics of the instrument focal plane assembly, in order to separate and measure the atmospheric Mie and Rayleigh scattering signals. With the completion of the full instrument assembly in 2019, ATLID has been subjected to an ambient performance test campaign, followed by a successful environmental qualification test campaign, including performance calibration and characterization in thermal vacuum conditions. In this paper the design and operational principle of ATLID is recalled and the major performance test results are presented, addressing the main key receiver and emitter characteristics. Finally, the estimated instrument, in-orbit, flight predictions are presented; these indicate compliance of the ALTID instrument performance against its specification and that it will meet its mission science objectives for the EarthCARE mission, to be launched in 2023. Full article

(This article belongs to the Special Issue Lidar Remote Sensing Techniques for Atmospheric Aerosols)

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19 pages, 5009 KiB

Open AccessArticle

Identifying Aerosol Subtypes from CALIPSO Lidar Profiles Using Deep Machine Learning

by Shan Zeng, Ali Omar, Mark Vaughan, Macarena Ortiz, Charles Trepte, Jason Tackett, Jeremy Yagle, Patricia Lucker, Yongxiang Hu, David Winker, Sharon Rodier and Brian Getzewich

Atmosphere 2021, 12(1), 10; https://0-doi-org.brum.beds.ac.uk/10.3390/atmos12010010 - 24 Dec 2020

Cited by 7 | Viewed by 3353

Abstract

The Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP), on-board the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) platform, is an elastic backscatter lidar that has been providing vertical profiles of the spatial, optical, and microphysical properties of clouds and aerosols since June 2006. Distinguishing between feature types (i.e., clouds vs. aerosol) and subtypes (e.g., ice clouds vs. water clouds and dust aerosols from smoke) in the CALIOP measurements is currently accomplished using layer-integrated measurements acquired by co-polarized (parallel) and cross-polarized (perpendicular) 532 nm channels and a single 1064 nm channel. Newly developed deep machine learning (DML) semantic segmentation methods now have the ability to combine observations from multiple channels with texture information to recognize patterns in data. Instead of focusing on a limited set of layer integrated values, our new DML feature classification technique uses the full scope of range-resolved information available in the CALIOP attenuated backscatter profiles. In this paper, one of the convolutional neural networks (CNN), SegNet, a fast and efficient DML model, is used to distinguish aerosol subtypes directly from the CALIOP profiles. The DML method is a 2D range bin-to-range bin aerosol subtype classification algorithm. We compare our new DML results to the classifications generated by CALIOP’s 1D layer-to-layer operational retrieval algorithm. These two methods, which take distinctly different approaches to aerosol classification, agree in over 60% of the comparisons. Higher levels of agreement are found in homogeneous scenes containing only a single aerosol type (i.e., marine, stratospheric aerosols). Disagreement between the two techniques increases in regions containing mixture of different aerosol types. The multi-dimensional texture information leveraged by the DML method shows advantages in differentiating between aerosol types based on their classification scores, as well as in distinguishing vertical distributions of aerosol types within individual layers. However, untangling mixtures of aerosol subtypes is still challenging for both the DML and operational algorithms. Full article

(This article belongs to the Special Issue Lidar Remote Sensing Techniques for Atmospheric Aerosols)

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23 pages, 16302 KiB

Open AccessEditor’s ChoiceArticle

Monitoring Dust Events Using Doppler Lidar and Ceilometer in Iceland

by Shu Yang, Jana Preißler, Matthias Wiegner, Sibylle von Löwis, Guðrún Nína Petersen, Michelle Maree Parks and David Christian Finger

Atmosphere 2020, 11(12), 1294; https://0-doi-org.brum.beds.ac.uk/10.3390/atmos11121294 - 30 Nov 2020

Cited by 15 | Viewed by 3630

Abstract

Ground-based lidars and ceilometers are widely used for dust and volcanic ash observation around the world. This is particularly interesting in Iceland where high-altitude dust events occur frequently during strong wind conditions and volcanic eruptions. To explore the possible application of such technologies in Iceland for monitoring dust events, we used a combination of Doppler wind lidars with depolarization channels, ceilometers, and other instruments, to monitor two dust events that occurred in Iceland during summer 2019. We applied a verified ceilometer data processing procedure with customized local corrections and developed a new procedure to process Doppler lidar data for aerosols measurements. Both lidar and ceilometer observations can be used to detect the dust layer and reveal the temporal and vertical distribution of dust aerosols in Iceland. The depolarization ratio measurements indicate that the weather conditions, e.g., relative humidity, could have a significant impact on lidar measurements. We conclude that using Doppler wind lidar and ceilometer measurements to monitor volcanic and sedimentary aerosols is possible and may be used to provide important information to the scientific community. Full article

(This article belongs to the Special Issue Lidar Remote Sensing Techniques for Atmospheric Aerosols)

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28 pages, 6959 KiB

Open AccessArticle

Comparison of Backscatter Coefficient at 1064 nm from CALIPSO and Ground–Based Ceilometers over Coastal and Non–Coastal Regions

by Thaize Baroni, Praveen Pandey, Jana Preissler, Gary Gimmestad and Colin O’Dowd

Atmosphere 2020, 11(11), 1190; https://0-doi-org.brum.beds.ac.uk/10.3390/atmos11111190 - 03 Nov 2020

Cited by 3 | Viewed by 2504

Abstract

This study investigates the direct comparison of backscatter coefficient profiles at 1064 nm which were measured by CALIOP (Cloud–Aerosol Lidar with Orthogonal Polarization) and by ground–based ceilometers located in coastal and non–coastal regions. The study uses data recorded between 2013 and 2016 to investigate the challenges involved in performing such a comparison in different environments. The standard Level 2 CALIOP Aerosol Profile version 4 product is evaluated against data from two ground–based Jenoptik CHM15K ceilometers: One at Mace Head (western Ireland) and the other at Harzgerode (central Germany). A statistical analysis from a series of CALIOP overpasses within 100 km distance from the ground–stations is presented considering different along–track averages in CALIOP data (5 km, 15 km, 25 km, 35 km, and 100 km) at the closest approach. The mean bias calculated from the correlative measurements between CALIOP and the ground–based ceilometers shows negative bias for 80% of the cases analyzed at Mace Head and positive bias for 68% of the cases investigated at Harzgerode, considering both daytime and nighttime measurements in cloud–free scenarios. The correlation of these results with HYSPLIT shows that different air samples play a role in the comparison. To our knowledge, this is the first study that addresses the limitations and capabilities in comparing CALIOP data with ground–based ceilometers at 1064 nm wavelength in different environments. Full article

(This article belongs to the Special Issue Lidar Remote Sensing Techniques for Atmospheric Aerosols)

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19 pages, 5843 KiB

Open AccessArticle

Differences in the Evolution of Pyrocumulonimbus and Volcanic Stratospheric Plumes as Observed by CATS and CALIOP Space-Based Lidars

by Kenneth Christian, John Yorks and Sampa Das

Atmosphere 2020, 11(10), 1035; https://0-doi-org.brum.beds.ac.uk/10.3390/atmos11101035 - 27 Sep 2020

Cited by 11 | Viewed by 2705

Abstract

Recent fire seasons have featured volcanic-sized injections of smoke aerosols into the stratosphere where they persist for many months. Unfortunately, the aging and transport of these aerosols are not well understood. Using space-based lidar, the vertical and spatial propagation of these aerosols can be tracked and inferences can be made as to their size and shape. In this study, space-based CATS and CALIOP lidar were used to track the evolution of the stratospheric aerosol plumes resulting from the 2019–2020 Australian bushfire and 2017 Pacific Northwest pyrocumulonimbus events and were compared to two volcanic events: Calbuco (2015) and Puyehue (2011). The pyrocumulonimbus and volcanic aerosol plumes evolved distinctly, with pyrocumulonimbus plumes rising upwards of 10 km after injection to altitudes of 30 km or more, compared to small to modest altitude increases in the volcanic plumes. We also show that layer-integrated depolarization ratios in these large pyrocumulonimbus plumes have a strong altitude dependence with more irregularly shaped particles in the higher altitude plumes, unlike the volcanic events studied. Full article

(This article belongs to the Special Issue Lidar Remote Sensing Techniques for Atmospheric Aerosols)

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