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An EARLINET early warning system for atmospheric aerosol aviation hazards
A stand-alone lidar-based method for detecting airborne hazards for aviation in near real time (NRT) is presented. A polarization lidar allows for the identification of irregular-shaped particles such as volcanic dust and desert dust. The Single Calculus Chain (SCC) of the European Aerosol Research Lidar Network (EARLINET) delivers high-resolution preprocessed data: the calibrated total attenuated backscatter and the calibrated volume linear depolarization ratio time series. From these calibrated lidar signals, the particle backscatter coefficient and the particle depolarization ratio can be derived in temporally high resolution and thus provide the basis of the NRT early warning system (EWS). In particular, an iterative method for the retrieval of the particle backscatter is implemented. This improved capability was designed as a pilot that will produce alerts for imminent threats for aviation. The method is applied to data during two diverse aerosol scenarios: first, a record breaking desert dust intrusion in March 2018 over Finokalia, Greece, and, second, an intrusion of volcanic particles originating from Mount Etna, Italy, in June 2019 over Antikythera, Greece. Additionally, a devoted observational period including several EARLINET lidar systems demonstrates the network's preparedness to offer insight into natural hazards that affect the aviation sector. © 2020 Author(s)
An EARLINET early warning system for atmospheric aerosol aviation hazards
A stand-alone lidar-based method for detecting
airborne hazards for aviation in near real time (NRT) is
presented. A polarization lidar allows for the identification
of irregular-shaped particles such as volcanic dust and
desert dust. The Single Calculus Chain (SCC) of the European
Aerosol Research Lidar Network (EARLINET) delivers
high-resolution preprocessed data: the calibrated total
attenuated backscatter and the calibrated volume linear
depolarization ratio time series. From these calibrated lidar
signals, the particle backscatter coefficient and the particle
depolarization ratio can be derived in temporally high resolution
and thus provide the basis of the NRT early warning
system (EWS). In particular, an iterative method for the retrieval
of the particle backscatter is implemented. This improved
capability was designed as a pilot that will produce alerts for imminent threats for aviation. The method is applied
to data during two diverse aerosol scenarios: first, a
record breaking desert dust intrusion in March 2018 over Finokalia,
Greece, and, second, an intrusion of volcanic particles
originating from Mount Etna, Italy, in June 2019 over
Antikythera, Greece. Additionally, a devoted observational
period including several EARLINET lidar systems demonstrates
the networkâs preparedness to offer insight into natural
hazards that affect the aviation sector.ACTRIS-2
654109ACTRIS preparatory phase
739530EUNADICS-AV
723986E-shape (EuroGEOSS Showcases: Applications Powered by Europe)
820852Ministry of Research and Innovation, Ontario
19PFE/17.10.2018Romanian National Core Program
18N/2019European Commission
European Commission Joint Research Centre
72569
Determinants of farmers\u2019 adaptation decisions under changing climate : the case of Fars province in Iran
Variation of Aerosol Optical Properties over Cluj-Napoca, Romania, Based on 10 Years of AERONET Data and MODIS MAIAC AOD Product
Aerosols play an important role in Earthâs climate system, and thus long-time ground- based measurements of aerosol optical properties are useful in understanding this role. Ten years of quality-assured measurements between 2010 and 2020 are used to investigate the aerosol climatology in the Cluj-Napoca area, in North-Western Romania. In this study, we analyze the aerosol optical depth (AOD), single scattering albedo (SSA) and angstrom exponent obtained by the CIMEL sun photometer, part of the aerosol robotic network (AERONET), to extract the seasonality of aerosols in the region and investigate the aerosol climatology of the area. Higher aerosol loads are found during July and August. The angstrom exponent has the lowest values in April and May, and the highest in August. The classification of aerosols using AERONET data is performed to separate dust, biomass burning, polluted urban, marine and continental-dominant aerosol mixtures. In addition, the study presents the validation efforts of the Multi-Angle Implementation of Atmospheric Correction (MAIAC) dataset against AERONET AOD over a 10-year period
SEVIRI Aerosol Optical Depth Validation Using AERONET and Intercomparison with MODIS in Central and Eastern Europe
This paper presents the validation results of Aerosol Optical Depth (AOD) retrieved from the Spinning Enhanced Visible Infrared Radiometer (SEVIRI) data using the near-real-time algorithm further developed in the frame of the Satellite-based Monitoring Initiative for Regional Air quality (SAMIRA) project. The SEVIRI AOD was compared against multiple data sources: six stations of the Aerosol Robotic Network (AERONET) in Romania and Poland, three stations of the Aerosol Research Network in Poland (PolandâAOD) and Moderate Resolution Imaging Spectroradiometer (MODIS) data overlapping Romania, Czech Republic and Poland. The correlation values between a four-month dataset (JuneâSeptember 2014) from SEVIRI and the closest temporally available data for both ground-based and satellite products were identified. The comparison of the SEVIRI AOD with the AERONET AOD observations generally shows a good correlation (r = 0.48â0.83). The mean bias is 0.10â0.14 and the root mean square error RMSE is between 0.11 and 0.15 for all six stations cases. For the comparison with PolandâAOD correlation values are 0.55 to 0.71. The mean bias is 0.04â0.13 and RMSE is between 0.10 and 0.14. As for the intercomparison to MODIS AOD, correlations values were generally lower (r = 0.33â0.39). Biases of â0.06 to 0.24 and RMSE of 0.04 to 0.28 were in good agreement with the groundâstations retrievals. The validation of SEVIRI AOD with AERONET results in the best correlations followed by the PolandâAOD network and MODIS retrievals. The average uncertainty estimates are evaluated resulting in most of the AOD values falling above the expected error range. A revised uncertainty estimate is proposed by including the observed bias form the AERONET validation efforts
SEVIRI Aerosol Optical Depth Validation Using AERONET and Intercomparison with MODIS in Central and Eastern Europe
This paper presents the validation results of Aerosol Optical Depth (AOD) retrieved from the Spinning Enhanced Visible Infrared Radiometer (SEVIRI) data using the near-real-time algorithm further developed in the frame of the Satellite-based Monitoring Initiative for Regional Air quality (SAMIRA) project. The SEVIRI AOD was compared against multiple data sources: six stations of the Aerosol Robotic Network (AERONET) in Romania and Poland, three stations of the Aerosol Research Network in Poland (PolandâAOD) and Moderate Resolution Imaging Spectroradiometer (MODIS) data overlapping Romania, Czech Republic and Poland. The correlation values between a four-month dataset (JuneâSeptember 2014) from SEVIRI and the closest temporally available data for both ground-based and satellite products were identified. The comparison of the SEVIRI AOD with the AERONET AOD observations generally shows a good correlation (r = 0.48â0.83). The mean bias is 0.10â0.14 and the root mean square error RMSE is between 0.11 and 0.15 for all six stations cases. For the comparison with PolandâAOD correlation values are 0.55 to 0.71. The mean bias is 0.04â0.13 and RMSE is between 0.10 and 0.14. As for the intercomparison to MODIS AOD, correlations values were generally lower (r = 0.33â0.39). Biases of â0.06 to 0.24 and RMSE of 0.04 to 0.28 were in good agreement with the groundâstations retrievals. The validation of SEVIRI AOD with AERONET results in the best correlations followed by the PolandâAOD network and MODIS retrievals. The average uncertainty estimates are evaluated resulting in most of the AOD values falling above the expected error range. A revised uncertainty estimate is proposed by including the observed bias form the AERONET validation efforts
Comparative consequence analysis of the BLEVE phenomena in the context on Land Use Planning; Case study: The Feyzin accident
SAMIRA-SAtellite Based Monitoring Initiative for Regional Air Quality
The satellite based monitoring initiative for regional air quality (SAMIRA) initiative was set up to demonstrate the exploitation of existing satellite data for monitoring regional and urban scale air quality. The project was carried out between May 2016 and December 2019 and focused on aerosol optical depth (AOD), particulate matter (PM), nitrogen dioxide (NO2), and sulfur dioxide (SO2). SAMIRA was built around several research tasks: 1. The spinning enhanced visible and infrared imager (SEVIRI) AOD optimal estimation algorithm was improved and geographically extended from Poland to Romania, the Czech Republic and Southern Norway. A near real-time retrieval was implemented and is currently operational. Correlation coefficients of 0.61 and 0.62 were found between SEVIRI AOD and ground-based sun-photometer for Romania and Poland, respectively. 2. A retrieval for ground-level concentrations of PM2.5 was implemented using the SEVIRI AOD in combination with WRF-Chem output. For representative sites a correlation of 0.56 and 0.49 between satellite-based PM2.5 and in situ PM2.5 was found for Poland and the Czech Republic, respectively. 3. An operational algorithm for data fusion was extended to make use of various satellite-based air quality products (NO2, SO2, AOD, PM2.5 and PM10). For the Czech Republic inclusion of satellite data improved mapping of NO2 in rural areas and on an annual basis in urban background areas. It slightly improved mapping of rural and urban background SO2. The use of satellites based AOD or PM2.5 improved mapping results for PM2.5 and PM10. 4. A geostatistical downscaling algorithm for satellite-based air quality products was developed to bridge the gap towards urban-scale applications. Initial testing using synthetic data was followed by applying the algorithm to OMI NO2 data with a direct comparison against high-resolution TROPOMI NO2 as a reference, thus allowing for a quantitative assessment of the algorithm performance and demonstrating significant accuracy improvements after downscaling. We can conclude that SAMIRA demonstrated the added value of using satellite data for regional- and urban-scale air quality monitoring
Synergetic observations by ground-based and space lidar systems and aeronet sun-radiometers: a step to advanced regional monitoring of large scale aerosol changes
The financial support by the European Union's Horizon 2020 research and innovation program (ACTRIS-2, grant agreement no. 654109) is gratefully acknowledged. The investigation was supported by the Belarusian Republican Foundation for Fundamental, Research Agreement No. F18EA-011, by the Russian Foundation for Basic Research (grant No. 18-55-81001), and also by Vietnam Academy of Science and Technology (project code QTRU05.01/18-20). The Portuguese team acknowledges the support from the Portuguese Science Foundation, in the frame of the European Regional Development Fund -COMPETE 2020, under the projects UID/GEO/04683/2013 (POCI-01-0145-FEDER-007690)The paper presents the preliminary results of the lidar&radiometer measurement campaign (LRMC2017), estimation of statistical relations between aerosol mode concentrations retrieved from CALIOP and ground-based lidar stations and case study of fire smoke events in the Eurasian regions using combined ground-based and space lidar and radiometer observations.European Union's Horizon 2020 research and innovation program (ACTRIS-2)
654109Belarusian Republican Foundation for Fundamental, Research
F18EA-011Russian Foundation for Basic Research (RFBR)
18-55-81001Vietnam Academy of Science and Technology
QTRU05.01/18-20Portuguese Science Foundation, in the frame of the European Regional Development Fund -COMPETE 2020
UID/GEO/04683/2013 (POCI-01-0145-FEDER-007690