Raman lidar observations of aged Siberian and Canadian forest fire smoke in the free troposphere over Germany in 2003: Microphysical particle characterization

Smoke aerosols play an important role in the atmospheric chemistry in terms of direct and indirect radiative forcing. Despite this, their properties in free troposphere and stratosphere are still insufficiently studied. When the smoke reaches these altitudes can be transported over transcontinental distances. During the transport of particles important transforming processes, such as coagulation, condensation, and gas-to-particle conversion occur, thus affecting environment and climate. The optical properties of smoke plumes have been usually analyzed by ground-based radiometers and satellite. However, these techniques cannot characterize accurately the high variability of the vertical structure of smoke aerosol. Raman lidar systems&#160; are characterized by high temporal and vertical resolutions and have demonstrated a strong capability to study long-range transport, optical properties and vertical structure of forest fire smoke.&#160;In the 2020 California&#8217;s fire season was exceptionally catastrophic. 23rd October, the immense Sonoma fire, in few days scorched 31000 hectares. The deep convection lifted the smoke from these fires to great heights. After reaching the free troposphere and stratosphere, the forest fire smoke was transported over great distances and reached the south of Italy, as evinced by the map of biomass burning aerosol optical depth at 550 nm, provided by the Copernicus Atmosphere Monitoring Service (CAMS).This work reports measurements carried out in the frame of the project CAMS21b by the Raman lidar system MUSA deployed at&#160;CNR-IMAA Atmospheric Observatory (CIAO) in Potenza. CAMS21b&#160;aims to design, test and set up the provisioning to CAMS of ACTRIS/EARLINET products in real time and near real time.&#160;In the case study of 26 October 2020, from to 10:13 to 13:45 UTC, measurements of particle backscattering coefficient at 355, 532 nm and 1064 and of the particle extinction coefficient at 355 nm and 532nm, show the presence of two distinct aerosol layers. A lower one extending from 6 km to 8 km and an upper one extending from 10 km to 12 km. The back-trajectory analysis reveals that the air masses originated over California, overpassed the Atlantic sea before reaching the measurement site.The values of the particle depolarization ratio are similar to those found in literature for smoke aerosols. In the first layer, values lower than 0.05 are indicative for small and spherical smoke particles. The moderately increased depolarization ratios in the second layer indicate the possible presence of partly coated smoke particles.More results from this measurement effort will be reported and discussed at the Conference.

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Transport of Canadian forest fire smoke over the UK as observed by lidar

10.5194/acp-2017-1181 ◽

2018 ◽

Cited By ~ 1

Author(s):

Geraint Vaughan ◽

Adam P. Draude ◽

Hugo M. A. Ricketts ◽

David M. Schultz ◽

Mariana Adam ◽

...

Keyword(s):

Forest Fire ◽

Forest Fires ◽

Western Canada ◽

North West ◽

Fire Smoke ◽

Range Transport ◽

Lidar Ratio ◽

The Uk ◽

Lidar Observations ◽

Aerosol Properties

Abstract. Layers of aerosol at heights between 2 and 11 km were observed with Raman lidars in the UK between 23 and 31 May 2016. A network of such lidars, supported by ceilometer observations, is used to map the extent of the aerosol and its optical properties. Spaceborne lidar profiles show that the aerosol originated from forest fires over Western Canada around 17 May, and indeed the aerosol properties – weak depolarisation and a lidar ratio at 355 nm in the range 35–65 sr – were consistent with long-range transport of forest fire smoke. The event was unusual in its persistence – the smoke plume was drawn into an atmospheric block that kept it above North-west Europe for nine days. Lidar observations show how the smoke layers became optically thinner during this period, but the lidar ratio and aerosol depolarisation showed little change.

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Forest Fire Smoke Layers Observed in the Free Troposphere over Portugal with a Multiwavelength Raman Lidar: Optical and Microphysical Properties

The Scientific World JOURNAL ◽

10.1155/2014/421838 ◽

2014 ◽

Vol 2014 ◽

pp. 1-11 ◽

Cited By ~ 12

Author(s):

Sérgio Nepomuceno Pereira ◽

Jana Preißler ◽

Juan Luis Guerrero-Rascado ◽

Ana Maria Silva ◽

Frank Wagner

Keyword(s):

Single Scattering ◽

Effective Radius ◽

Free Troposphere ◽

Depolarization Ratio ◽

Raman Lidar ◽

Extinction Coefficients ◽

Smoke Particles ◽

Microphysical Properties ◽

Fire Smoke ◽

Lidar Ratio

Vertically resolved optical and microphysical properties of biomass burning aerosols, measured in 2011 with a multiwavelength Raman lidar, are presented. The transportation time, within 1-2 days (or less), pointed towards the presence of relatively fresh smoke particles over the site. Some strong layers aloft were observed with particle backscatter and extinction coefficients (at 355 nm) greater than 5 Mm−1 sr−1and close to 300 Mm−1, respectively. The particle intensive optical properties showed features different from the ones reported for aged smoke, but rather consistent with fresh smoke. The Ångström exponents were generally high, mainly above 1.4, indicating a dominating accumulation mode. Weak depolarization values, as shown by the small depolarization ratio of 5% or lower, were measured. Furthermore, the lidar ratio presented no clear wavelength dependency. The inversion of the lidar signals provided a set of microphysical properties including particle effective radius below 0.2 μm, which is less than values previously observed for aged smoke particles. Real and imaginary parts of refractive index of about 1.5-1.6 and 0.02i, respectively, were derived. The single scattering albedo was in the range between 0.85 and 0.93; these last two quantities indicate the nonnegligible absorbing characteristics of the observed particles.

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Vertical aerosol distribution in the Southern hemispheric Midlatitudes as observed with lidar at Punta Arenas, Chile (53.2° S and 70.9° W) during ALPACA

10.5194/acp-2018-1124 ◽

2018 ◽

Author(s):

Andreas Foth ◽

Thomas Kanitz ◽

Ronny Engelmann ◽

Holger Baars ◽

Martin Radenz ◽

...

Keyword(s):

Long Range Transport ◽

Free Troposphere ◽

Marine Aerosol ◽

Raman Lidar ◽

Aerosol Distribution ◽

Range Transport ◽

Aerosol Sources ◽

Ground Based Lidar ◽

Lidar Observations ◽

Measurement Period

Abstract. Within this publication, lidar observations of the vertical aerosol distribution above Punta Arenas, Chile (53.2° S and 50.9° W) which have been performed with the Raman lidar PollyXT from December 2009 to April 2010 are presented. Pristine marine aerosol conditions related to the prevailing westerly circulation dominated the measurements. Lofted aerosol layers could only be observed eight times during the whole measurement period. Two case studies are presented showing long-range transport of smoke from biomass burning in Australia and regionally transported dust from the Patagonian Desert, respectively. The aerosol sources are identified by trajectory analyses with HYSPLIT and FLEXPART. However, seven of the eight analysed cases with lofted layers show an aerosol optical thickness of less than 0.05. From the lidar observations a mean planetary boundary layer (PBL) top height of 1150 ± 350 m was determined. An analysis of particle backscatter coefficients confirms that the majority of the aerosol is attributed to the PBL while the free troposphere is characterized by a very low background aerosol concentration. The ground-based lidar observations at 532 and 1064 nm are supplemented by the AERONET Sun photometers and the space-borne lidar CALIOP on board of CALIPSO. The averaged AOT determined by CALIOP was 0.02 ± 0.01 at Punta Arenas from 2009 to 2010.

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Comprehensive particle characterization from three-wavelength Raman-lidar observations: case study

Applied Optics ◽

10.1364/ao.40.004863 ◽

2001 ◽

Vol 40 (27) ◽

pp. 4863 ◽

Cited By ~ 77

Author(s):

Detlef Müller ◽

Ulla Wandinger ◽

Dietrich Althausen ◽

Markus Fiebig

Keyword(s):

Particle Characterization ◽

Raman Lidar ◽

Lidar Observations

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A Collaborative Region Detection and Grading Framework for Forest Fire Smoke Using Weakly Supervised Fine Segmentation and Lightweight Faster-RCNN

Forests ◽

10.3390/f12060768 ◽

2021 ◽

Vol 12 (6) ◽

pp. 768

Author(s):

Jin Pan ◽

Xiaoming Ou ◽

Liang Xu

Keyword(s):

Forest Fire ◽

Forest Fires ◽

Learning Strategy ◽

Early Stage ◽

Fire Detection ◽

Surveillance Systems ◽

Region Detection ◽

Decision Network ◽

Fire Smoke ◽

Weakly Supervised

Forest fires are serious disasters that affect countries all over the world. With the progress of image processing, numerous image-based surveillance systems for fires have been installed in forests. The rapid and accurate detection and grading of fire smoke can provide useful information, which helps humans to quickly control and reduce forest losses. Currently, convolutional neural networks (CNN) have yielded excellent performance in image recognition. Previous studies mostly paid attention to CNN-based image classification for fire detection. However, the research of CNN-based region detection and grading of fire is extremely scarce due to a challenging task which locates and segments fire regions using image-level annotations instead of inaccessible pixel-level labels. This paper presents a novel collaborative region detection and grading framework for fire smoke using a weakly supervised fine segmentation and a lightweight Faster R-CNN. The multi-task framework can simultaneously implement the early-stage alarm, region detection, classification, and grading of fire smoke. To provide an accurate segmentation on image-level, we propose the weakly supervised fine segmentation method, which consists of a segmentation network and a decision network. We aggregate image-level information, instead of expensive pixel-level labels, from all training images into the segmentation network, which simultaneously locates and segments fire smoke regions. To train the segmentation network using only image-level annotations, we propose a two-stage weakly supervised learning strategy, in which a novel weakly supervised loss is proposed to roughly detect the region of fire smoke, and a new region-refining segmentation algorithm is further used to accurately identify this region. The decision network incorporating a residual spatial attention module is utilized to predict the category of forest fire smoke. To reduce the complexity of the Faster R-CNN, we first introduced a knowledge distillation technique to compress the structure of this model. To grade forest fire smoke, we used a 3-input/1-output fuzzy system to evaluate the severity level. We evaluated the proposed approach using a developed fire smoke dataset, which included five different scenes varying by the fire smoke level. The proposed method exhibited competitive performance compared to state-of-the-art methods.

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