scholarly journals Biomass burning events measured by lidars in EARLINET. Part I. Data analysis methodology

2020 ◽  
Author(s):  
Mariana Adam ◽  
Doina Nicolae ◽  
Iwona S. Stachlewska ◽  
Alexandros Papayannis ◽  
Dimitris Balis
Keyword(s):  
Biomass Burning ◽  
Companion Paper ◽  
Data Sets ◽  
Backscatter Coefficient ◽  
Analysis Methodology ◽  
Geographical Regions ◽  
Lidar Measurements ◽  
Range Transport ◽  
Linear Depolarization Ratio ◽  
Continental Origin

Abstract. The methodology of analysing the biomass burning events recorded in the database of the European Aerosol Research Lidar Network in the frame of Aerosol Cloud and Trace Gases Research Infrastructure is presented. The period of 2008–2017 was chosen to analyse all of the events stored in the database under Forest Fire category for a number of 14 stations available. The data provided ranged from complete data sets (particle backscatter, extinction and linear depolarization ratio profiles) to single profiles (particle backscatter coefficient profile). Smoke layers geometry was evaluated and the mean optical properties within each layer were computed. The backtrajectories technique was used to double check the source of all pollution layers. The biomass burning layers were identified taking into account the presence of the fires along the backtrajectory. The biomass burning events are analysed by the means of the intensive parameters. The analysis was structured in three directions: I) common biomass burning source (fire) recorded by at least two stations, II) long-range transport from N. America, III) analysis over four geographical regions (SE Europe, NE Europe, Central Europe and SW Europe). Based on backtrajectories calculations and fires’ location, the lidar measurements can be labelled either as measurements of ‘single fire’ or ‘mixed fires’ (case I), measurements of N America fires or measurements of mixed N America and local fires (case II). The histogram of the fires’ location reveals the smoke sources for each region. For each region, statistics on intensive parameters is performed. The sources’ origin of the intensive parameters is categorized based on the continental origin of the air-mass (European, African, Asian, N American or a combination of them). The methodology presented here is meant to provide a perspective to explore large amount of lidar data and deliver novel approaches to analyse the intensive parameters based on the assigned biomass burning sources. A thorough consideration of all potential fires’ sources reveals that most of the time the lidar measurements characterise the smoke from a mixture of fires. A comprehensive discussion over all results (based on the intensive parameters and the sources’ location), will be given in a companion paper submitted to ACP EARLINET special issue.

scholarly journals Biomass burning events measured by lidars in EARLINET – Part 1: Data analysis methodology

2020 ◽  
Vol 20 (22) ◽  
pp. 13905-13927
Author(s):  
Mariana Adam ◽  
Doina Nicolae ◽  
Iwona S. Stachlewska ◽  
Alexandros Papayannis ◽  
Dimitris Balis
Keyword(s):  
Eastern Europe ◽  
Biomass Burning ◽  
North American ◽  
Western Europe ◽  
Companion Paper ◽  
Back Trajectory ◽  
Backscatter Coefficient ◽  
Geographical Regions ◽  
Lidar Measurements ◽  
Linear Depolarization Ratio

Abstract. The methodology of analysing the biomass burning events recorded in the database of the European Aerosol Research Lidar Network in the framework of the Aerosol, Clouds and Trace Gases Research Infrastructure is presented. The period of 2008–2017 was chosen to analyse all of the events stored in the database under the Forest Fire category for a total of 14 stations available. The data provided ranged from complete datasets (particle backscatter, extinction and linear depolarization ratio profiles) to single profiles (particle backscatter coefficient profile). Smoke layers geometry was evaluated and the mean optical properties within each layer were computed. The back-trajectory technique was used to double-check the source of all pollution layers. The biomass burning layers were identified by taking into account the presence of the fires along the back trajectory. The biomass burning events are analysed by the means of the intensive parameters. The analysis was structured in three directions: (I) common biomass burning source (fire) recorded by at least two stations, (II) long-range transport from North America, and (III) analysis over four geographical regions (south-eastern Europe, north-eastern Europe, central Europe, and south-western Europe). Based on back-trajectory calculations and fire locations, the lidar measurements can be labelled either as measurements of a “single fire” or “mixed fires” (case I), measurements of North American fires, or measurements of mixed North American and local fires (case II). The histogram of the fire locations reveals the smoke sources for each region. For each region, statistics on intensive parameters are performed. The source origin of the intensive parameters is categorized based on the continental origin of the air mass (European, African, Asian, North American, or a combination of them). The methodology presented here is meant to provide a perspective to explore a large number of lidar data and deliver novel approaches to analyse the intensive parameters based on the assigned biomass burning sources. A thorough consideration of all potential fire sources reveals that most of the time the lidar measurements characterize the smoke from a mixture of fires. A comprehensive discussion of all the results (based on the intensive parameters and the source locations) will be given in a companion paper submitted to the ACP EARLINET special issue.

scholarly journals Biomass Burning Measurements in Earlinet

2020 ◽  
Vol 237 ◽  
pp. 05005
Author(s):  
Mariana Adam ◽  
Doina Nicolae ◽  
Livio Belegante ◽  
Iwona S. Stachlewska ◽  
Dominika Szczepanik ◽  
...  
Keyword(s):  
Quality Control ◽  
Biomass Burning ◽  
Long Range Transport ◽  
Data Sets ◽  
Depolarization Ratio ◽  
Backscatter Coefficient ◽  
Backward Trajectory ◽  
Range Transport ◽  
The Mean ◽  
Linear Depolarization Ratio

The biomass burning events are analyzed using the EARLINET-ACTRIS atmospheric profiling of aerosols using lidars. The period of 2008-2017 was chosen to analyze all the events assigned in the EARLINET database under Forest Fire category. A number of fourteen stations were considered. The data provided, ranged from complete data sets (backscatter, extinction and particle linear depolarization ratio) to single profiles (backscatter coefficient). A thorough quality control was performed. Smoke layers geometry was evaluated and the mean properties within each layer were computed. The Hysplit backward-trajectory technique and the FIRMS fire database were used to double check the source of each layer. Discussions were made under the following scenarios: fire events seen by two stations, long range transport from North America, and geographical clusters.

scholarly journals Biomass burning events measured by lidars in EARLINET. Part II. Results and discussions

2020 ◽  
Author(s):  
Mariana Adam ◽  
Doina Nicolae ◽  
Livio Belegante ◽  
Iwona S. Stachlewska ◽  
Lucja Janicka ◽  
...  
Keyword(s):  
North America ◽  
Long Range ◽  
Biomass Burning ◽  
Long Range Transport ◽  
Depolarization Ratio ◽  
Smoke Particles ◽  
Geographical Regions ◽  
Range Transport ◽  
Ångström Exponent ◽  
Lidar Ratio

Abstract. Biomass burning events are analysed using the European Aerosol Research Lidar Network database for atmospheric profiling of aerosols by lidars. Atmospheric profiles containing forest fires layers were identified in data collected by fourteen stations during 2008–2017. The data ranged from complete data sets (particle backscatter coefficient, extinction coefficient and linear depolarization ratio) to single profiles (particle backscatter coefficient). The data analysis methodology was described in Part I (Biomass burning events measured by lidars in EARLINET. Part I. Data analysis methodology, under discussions to ACP, the EARLINET special issue). The results are analysed by means of intensive parameters in three directions: (I) common biomass burning source (fire) recorded by at least two stations, (II) long range transport of smoke particles from North America (here, we divided the events into pure North America and mixed-North America and local) smoke groups, and (III) analysis of smoke particles over four geographical regions (SE Europe, NE Europe, Central Europe and SW Europe). Five events were found for case (I), while 24 events were determined for case (II). A statistical analysis over the four geographical regions considered revealed that smoke originated from different regions. The smoke detected in the Central Europe region (Cabauw, Leipzig, and Hohenpeißenberg) was mostly brought over from North America (87 % of the fires), by long range transport. The smoke in the South West region (Barcelona, Evora, and Granada) came mostly from the Iberian Peninsula and North Africa, the long-range transport from North America accounting for only 9 % here. The smoke in the North Europe region (Belsk, Minsk, and Warsaw) originated mostly in East Europe (Ukraine and Russia), and had a 31 % contribution from smoke by long-range transport from North America. For the South East region (Athens, Bucharest, Potenza, Sofia, Thessaloniki) the origin of the smoke was mostly located in SE Europe (only 3 % from North America). Specific features for the lidar-derived intensive parameters based on smoke continental origin were determined for each region. Based on the whole dataset, the following signatures were observed: (i) the colour ratio of the lidar ratio and the backscatter Ångström exponent increase with travel time, while the extinction Ångström exponent and the colour ratio of the particle depolarization ratio decrease; (ii) an increase of the colour ratio of the particle depolarization ratio corresponds to both a decrease of the colour ratio of the lidar ratios and an increase of the extinction Ångström exponent; (iii) the measured smoke originating from all continental regions is characterized in average as aged smoke, except for a few cases; (iv) in general, the local smoke shows a smaller lidar ratio while the long range transported smoke shows a higher lidar ratio; and (v) the depolarization is smaller for long range transported smoke. A complete characterization of the smoke particles type (either fresh or aged) is presented for each of the four geographical regions versus different continental source regions.

scholarly journals Experimental calibration assessment of a MPLNET/Micro-Pulse Lidar system in comparison with EARLINET lidar measurements for aerosol optical properties retrieval

2020 ◽  
Author(s):  
Carmen Córdoba-Jabonero ◽  
Albert Ansmann ◽  
Cristofer Jiménez ◽  
Holger Baars ◽  
María-Ángeles López-Cayuela ◽  
...  
Keyword(s):  
Optical Properties ◽  
Depolarization Ratio ◽  
Backscatter Coefficient ◽  
Experimental Calibration ◽  
Experimental Procedure ◽  
Lidar Measurements ◽  
Total Particle ◽  
Linear Depolarization Ratio ◽  
Micro Pulse Lidar ◽  
The Impact

Abstract. Simultaneous observations of a polarized Micro-Pulse Lidar (P-MPL) system, currently operative within MPLNET (NASA Micro-Pulse Lidar Network), with two referenced EARLINET (European Aerosol Research Lidar Network) lidars, running at Leipzig site (Germany, 51.4º N 12.4º E, 125 m a.s.l.), were performed during a comprehensive two-month field campaign in summer 2019. A calibration assessment regarding the overlap (OVP) correction of the P-MPL signal profiles and its impact in the retrieval of the optical properties is achieved, describing also the experimental procedure used. The optimal lidar-specific OVP function for correcting the P-MPL measurements is experimentally determined, highlighting that the OVP function as delivered by the P-MPL manufacturer cannot be used. Among the OVP functions examined, the averaged one between those obtained from the comparison of the P-MPL observations with those of the other two referenced lidars seems to be the best proxy at both near- and far-field ranges. In addition, the impact of the OVP function in the accuracy of the retrieved profiles of the total particle backscatter coefficient (PBC) and the particle linear depolarization ratio (PLDR) is examined. First, the volume linear depolarization ratio (VLDR) profile is obtained and compared to the reference lidars, showing it needs to be corrected by a small offset value within a good accuracy. Once P-MPL measurements are optimally OVP-corrected, the PBC profiles (and hence the PLDR ones) can be derived using the Klett-Fernald approach. In addition, an alternative method based on the separation of the total PBC into their aerosol components is presented in order to estimate the total particle extinction coefficient (PEC) profile, and hence the Aerosol Optical Depth, from elastic P-MPL measurements. A dust event as observed at Leipzig in June 2019 is used for illustration. In overall, an adequate OVP function is needed to be determined in a regular basis to calibrate the P-MPL system in order to derive suitable aerosol products.

scholarly journals Using Machine Learning Methods to Identify Particle Types from Doppler Lidar Measurements in Iceland

2021 ◽  
Vol 13 (13) ◽  
pp. 2433
Author(s):  
Shu Yang ◽  
Fengchao Peng ◽  
Sibylle von Löwis ◽  
Guðrún Nína Petersen ◽  
David Christian Finger
Keyword(s):  
Machine Learning ◽  
Weather Conditions ◽  
Dust Storms ◽  
Machine Learning Algorithms ◽  
Lidar Data ◽  
Data Sets ◽  
Doppler Lidar ◽  
Lidar Measurements ◽  
Using Data ◽  
Filter Noise

Doppler lidars are used worldwide for wind monitoring and recently also for the detection of aerosols. Automatic algorithms that classify the lidar signals retrieved from lidar measurements are very useful for the users. In this study, we explore the value of machine learning to classify backscattered signals from Doppler lidars using data from Iceland. We combined supervised and unsupervised machine learning algorithms with conventional lidar data processing methods and trained two models to filter noise signals and classify Doppler lidar observations into different classes, including clouds, aerosols and rain. The results reveal a high accuracy for noise identification and aerosols and clouds classification. However, precipitation detection is underestimated. The method was tested on data sets from two instruments during different weather conditions, including three dust storms during the summer of 2019. Our results reveal that this method can provide an efficient, accurate and real-time classification of lidar measurements. Accordingly, we conclude that machine learning can open new opportunities for lidar data end-users, such as aviation safety operators, to monitor dust in the vicinity of airports.

scholarly journals Benefit of depolarization ratio at λ = 1064 nm for the retrieval of the aerosol microphysics from lidar measurements

2014 ◽  
Vol 7 (11) ◽  
pp. 3773-3781 ◽  
Author(s):  
J. Gasteiger ◽  
V. Freudenthaler
Keyword(s):  
Optical Properties ◽  
Mass Concentration ◽  
Volcanic Ash ◽  
Single Scattering ◽  
Depolarization Ratio ◽  
Complex Relation ◽  
Microphysical Properties ◽  
Lidar Measurements ◽  
Increased Sensitivity ◽  
Linear Depolarization Ratio

Abstract. A better quantification of aerosol properties is required for improving the modelling of aerosol effects on weather and climate. This task is methodologically demanding due to the diversity of the microphysical properties of aerosols and the complex relation between their microphysical and optical properties. Advanced lidar systems provide spatially and temporally resolved information on the aerosol optical properties that is sufficient for the retrieval of important aerosol microphysical properties. Recently, the mass concentration of transported volcanic ash, which is relevant for the flight safety of aeroplanes, was retrieved from measurements of such lidar systems in southern Germany. The relative uncertainty of the retrieved mass concentration was on the order of ±50%. The present study investigates improvements of the retrieval accuracy when the capability of measuring the linear depolarization ratio at 1064 nm is added to the lidar setup. The lidar setups under investigation are based on those of MULIS and POLIS of the Ludwig-Maximilians-Universität in Munich (Germany) which measure the linear depolarization ratio at 355 and 532 nm with high accuracy. The improvements are determined by comparing uncertainties from retrievals applied to simulated measurements of this lidar setup with uncertainties obtained when the depolarization at 1064 nm is added to this setup. The simulated measurements are based on real lidar measurements of transported Eyjafjallajökull volcano ash. It is found that additional 1064 nm depolarization measurements significantly reduce the uncertainty of the retrieved mass concentration and effective particle size. This significant improvement in accuracy is the result of the increased sensitivity of the lidar setup to larger particles. The size dependence of the depolarization does not vary strongly with refractive index, thus we expect similar benefits for the retrieval in case of measurements of other volcanic ash compositions and also for transported desert dust. For the retrieval of the single scattering albedo, which is relevant to the radiative transfer in aerosol layers, no significant improvements were found.

scholarly journals Graphics algorithm for deriving atmospheric boundary layer heights from CALIPSO data

2018 ◽  
Vol 11 (9) ◽  
pp. 5075-5085 ◽  
Author(s):  
Boming Liu ◽  
Yingying Ma ◽  
Jiqiao Liu ◽  
Wei Gong ◽  
Wei Wang ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Atmospheric Boundary Layer ◽  
Weather Forecasting ◽  
Correlation Coefficients ◽  
Horizontal Resolution ◽  
Backscatter Coefficient ◽  
Distribution Method ◽  
Boundary Layer Height ◽  
Lidar Measurements ◽  
Ground Based Lidar

Abstract. The atmospheric boundary layer is an important atmospheric feature that affects environmental health and weather forecasting. In this study, we proposed a graphics algorithm for the derivation of atmospheric boundary layer height (BLH) from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) data. Owing to the differences in scattering intensity between molecular and aerosol particles, the total attenuated backscatter coefficient 532 and attenuated backscatter coefficient 1064 were used simultaneously for BLH detection. The proposed algorithm transformed the gradient solution into graphics distribution solution to overcome the effects of large noise and improve the horizontal resolution. This method was then tested with real signals under different horizontal smoothing numbers (1, 3, 15 and 30). Finally, the results of BLH obtained by CALIPSO data were compared with the results retrieved by the ground-based lidar measurements. Under the horizontal smoothing number of 15, 12 and 9, the correlation coefficients between the BLH derived by the proposed algorithm and ground-based lidar were both 0.72. Under the horizontal smoothing number of 6, 3 and 1, the correlation coefficients between the BLH derived by graphics distribution method (GDM) algorithm and ground-based lidar were 0.47, 0.14 and 0.12, respectively. When the horizontal smoothing number was large (15, 12 and 9), the CALIPSO BLH derived by the proposed method demonstrated a good correlation with ground-based lidar. The algorithm provided a reliable result when the horizontal smoothing number was greater than 9. This finding indicated that the proposed algorithm can be applied to the CALIPSO satellite data with 3 and 5 km horizontal resolution.

Impact of field biomass burning on local pollution and long-range transport of PM2.5 in Northeast Asia

2019 ◽  
Vol 244 ◽  
pp. 414-422 ◽  
Author(s):  
Katsushige Uranishi ◽  
Fumikazu Ikemori ◽  
Hikari Shimadera ◽  
Akira Kondo ◽  
Seiji Sugata
Keyword(s):  
Long Range ◽  
Biomass Burning ◽  
Northeast Asia ◽  
Long Range Transport ◽  
Local Pollution ◽  
Range Transport

scholarly journals Tropospheric CO vertical profiles measured by IAGOS aircraft in 2002–2017 and the role of biomass burning

2018 ◽  
pp. 1-41
Author(s):  
Hervé Petetin ◽  
Bastien Sauvage ◽  
Mark Parrington ◽  
Hannah Clark ◽  
Alain Fontaine ◽  
...  
Keyword(s):  
East Asia ◽  
Long Range ◽  
Biomass Burning ◽  
South India ◽  
Long Range Transport ◽  
Anthropogenic Emissions ◽  
South East Asia ◽  
Vertical Profiles ◽  
Range Transport

<p><strong>Abstract.</strong> This study investigates the role of biomass burning and long-range transport in the anomalies of carbon monoxide (CO) regularly observed along the tropospheric vertical profiles measured in the framework of IAGOS. Considering the high interannual variability of biomass burning emissions and the episodic nature of pollution long-range transport, one strength of this study is the amount of data taken into account, namely 30,000 vertical profiles at 9 clusters of airports in Europe, North America, Asia, India and southern Africa over the period 2002&amp;ndash;2017. </p> <p> As a preliminary, a brief overview of the spatio-temporal variability, latitudinal distribution, interannual variability and trends of biomass burning CO emissions from 14 regions is provided. The distribution of CO mixing ratios at different levels of the troposphere is also provided based on the entire IAGOS database (125 million CO observations). </p> <p> This study focuses on the free troposphere (altitudes above 2<span class="thinspace"></span>km) where the long-range transport of pollution is favoured. Anomalies at a given airport cluster are here defined as departures from the local seasonally-averaged climatological vertical profile. The intensity of these anomalies varies significantly depending on the airport, with maximum (minimum) CO anomalies of 110&amp;ndash;150 (48)<span class="thinspace"></span>ppbv in Asia (Europe). Looking at the seasonal variation of the frequency of occurrence, the 25<span class="thinspace"></span>% strongest CO anomalies appears reasonably well distributed along the year, in contrast to the 5<span class="thinspace"></span>% or 1<span class="thinspace"></span>% strongest anomalies that exhibit a strong seasonality with for instance more frequent anomalies during summertime in northern United-States, during winter/spring in Japan, during spring in South-east China, during the non-monsoon seasons in south-east Asia and south India, and during summer/fall at Windhoek, Namibia. Depending on the location, these strong anomalies are observed in different parts of the free troposphere. </p> <p> In order to investigate the role of biomass burning emissions in these anomalies, we used the SOFT-IO v1.0 IAGOS added-value products that consist of FLEXPART 20-days backward simulations along all IAGOS aircraft trajectories, coupled with anthropogenic (MACCity) and biomass burning (GFAS) CO emission inventories and vertical injections. SOFT-IO estimates the contribution (in ppbv) of the recent (less than 20 days) primary worldwide CO emissions, tagged per source region. Biomass burning emissions are found to play an important role in the strongest CO anomalies observed at most airport clusters. The regional tags indicate a large contribution from boreal regions at airport clusters in Europe and North America during summer season. In both Japan and south India, the anthropogenic emissions dominate all along the year, except for the strongest summertime anomalies observed in Japan that are due to Siberian fires. The strongest CO anomalies at airport clusters located in south-east Asia are induced by fires burning during spring in south-east Asia and during fall in equatorial Asia. In southern Africa, the Windhoek airport was mainly impacted by fires in southern hemisphere Africa and South America. </p> <p> To our knowledge, no other studies have used such a large dataset of in situ vertical profiles for deriving a climatology of the impact of biomass burning versus anthropogenic emissions on the strongest CO anomalies observed in the troposphere, in combination with information on the source regions. This study therefore provides both qualitative and quantitative information for interpreting the highly variable CO vertical distribution in several regions of interest.</p>

scholarly journals Sources of light-absorbing aerosol in arctic snow and their seasonal variation

2010 ◽  
Vol 10 (6) ◽  
pp. 13755-13796 ◽  
Author(s):  
D. A. Hegg ◽  
S. G. Warren ◽  
T. C. Grenfell ◽  
S. J. Doherty ◽  
A. D. Clarke
Keyword(s):  
Seasonal Variation ◽  
Biomass Burning ◽  
Chemical Constituents ◽  
Dominant Role ◽  
Data Sets ◽  
Source Attribution ◽  
Data Set ◽  
Pmf Model ◽  
The North ◽  
Absorbing Aerosols

Abstract. Two data sets consisting of measurements of light absorbing aerosols (LAA) in arctic snow together with suites of other corresponding chemical constituents are presented; the first from Siberia, Greenland and near the North Pole obtained in 2008, and the second from the Canadian arctic obtained in 2009. A preliminary differentiation of the LAA into black carbon (BC) and non-BC LAA is done. Source attribution of the light absorbing aerosols was done using a positive matrix factorization (PMF) model. Four sources were found for each data set (crop and grass burning, boreal biomass burning, pollution and marine). For both data sets, the crops and grass biomass burning was the main source of both LAA species, suggesting the non-BC LAA was brown carbon. Depth profiles at most of the sites allowed assessment of the seasonal variation in the source strengths. The biomass burning sources dominated in the spring but pollution played a more significant (though rarely dominant) role in the fall, winter and, for Greenland, summer. The PMF analysis is consistent with trajectory analysis and satellite fire maps.

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