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.
During the aviation crisis related to the volcanic eruption of
Eyjafjallajökull, Iceland, in 2010, the European Aerosol Research Lidar
Network
The lessons learned from the Eyjafjallajökull crisis emphasized
the vulnerability of air transportation to natural hazards
Furthermore, the Eyjafjallajökull eruption highlighted the gap in
the availability of real-time measurements and monitoring information
for airborne hazards. Specifically, the lack of
height-resolved information, a key aspect in flight planning and
mitigation strategies, became evident. In the frame of the Horizon 2020 research project
EUNADICS-AV (European Natural Disaster Coordination and Information System for
Aviation;
A polarization lidar is an important tool to characterize the
different aerosols. This system permits the discrimination of light-depolarizing coarse-mode particles such as volcanic and desert dust
and fine-mode particles such as smoke particles and anthropogenic
pollution
During the last years, EARLINET has strongly increased its observing
capacity with the addition of new stations and a system upgrade, namely,
the installation of depolarization channels. In addition, the further
development of the Single Calculus Chain (SCC)
In Sect.
The European Aerosol Research Lidar Network
The EARLINET network in Europe. The green squares indicate the active stations, the yellow squares indicate the joining stations, and the red square indicates the non-active Finokalia, Greece, station.
To ensure a homogeneous, traceable, and quality-controlled analysis of
raw lidar data across the network, a centralized and fully automated
analysis tool, called the Single Calculus Chain (SCC), has been
developed within EARLINET
The cloud screening module is responsible for cloud identification
in uncalibrated lidar signals and especially with low clouds since such
clouds do not permit the aerosol optical property retrieval by ELDA.
Note that the cloud removal is also essential in our EWS methodology.
The input of the algorithm is the high-resolution preprocessed
signals produced by the SCC HiRELPP (High-Resolution EARLINET
Pre-Processor) module. The current cloud screening detects clouds as
bins with irregularly high values in signal and edge strength
The calibrated high-resolution data along with the cloud screening
output are essential for the proposed methodology and are used in the
EWS. The methodology to derive the particle high-resolution data that are described in Sect.
The EARLINET component of NOA (National Observatory of Athens) for the
period of April 2017 until May 2018 was deployed through the NOA lidar system
on the north coast of Crete. The Finokalia Atmospheric Observatory
(35.34
The NOA lidar system Polly
For the detection of the desert dust plume, satellite imagery from the
Spinning Enhanced Visible Infrared Imager (SEVIRI) is used. SEVIRI is
a line-by-line scanning radiometer on board the Meteosat Second
Generation (MSG) geostationary satellite. It provides data in 12 spectral bands every 15 min for the full Earth disk area. The spatial
resolution is around 3
For the detection of the volcanic dust, we use the Lagrangian transport
model FLEXPART
The delivery of an alert using EARLINET data is based on a two-step
approach. In the first step, the high-resolution calibrated data are
used to estimate the particle backscatter coefficient and the particle
linear depolarization ratio. In order to retrieve the particle
backscatter coefficient, an iterative methodology is adapted. The
methodology, described in
The method is similar to that of
The calibrated attenuated backscatter coefficient provided by the SCC
can be written as
In an initial step, the attenuation in the atmosphere is neglected,
The particle extinction coefficient is estimated by multiplying
We found that fewer than 10 steps are required for a difference of 1 % for the cases examined herein.
The particle depolarization ratio at 532
The input lidar ratio value used in the retrieval could significantly
affect the results.
The performance of the iterative method for
In the second step, the location and the intensity of the volcanic
dust and desert dust event are identified.
The particle depolarization ratio is used to separate the nonspherical
particles contribution to the particle backscatter coefficient.
Until the aviation crisis in 2010, planes were advised to avoid the
volcanic plumes regardless of the aerosol concentration
The methodology proposed by
The scatter plot indicates the mean and the standard deviation of the conversion factor,
The term
The code used in Fig.
The EARLINET alert delivery scheme for aviation. The particle backscatter coefficient and depolarization ratio are used to estimate the coarse backscatter coefficient (one-step POLIPHON method). Three levels are considered that correspond to “Low alert” for particle concentrations higher than 0.2
The conversion factor for the coarse particles (i.e., volcanic and
desert dust) varies strongly with the distance from the source and, in
the case of volcanic eruptions, with the eruption type.
Figure
In this section, we apply the described methodology to potential perilous events recently detected by the stations of Finokalia and Antikythera, Greece. The observations refer to the same lidar system that was initially deployed in Finokalia and later moved to the island of Antikythera. The aim is not to present a detailed analysis of investigated cases but instead to demonstrate the potential of this methodology to be integrated as a tailored EARLINET product for the fast alerting of airborne hazards relevant to flight operations.
During March 2018, frequent intense dust storms affected Greece with
the region of Libya being the originating source
The dust SEVIRI product
WRF-Chem dust aerosol optical depth (AOD) on 21 March 2018 12:00 UTC.
Here we focus on 21 March when the dust cloud initially appeared
over Crete. Figure
The coarse particle backscatter coefficient, the particle
depolarization ratio at 532
EARLINET observations at Finokalia on 21 March 2018:
As the event was aggravated in the following hours, the lidar signal is
most likely attenuated which highlights the limitation of the methodology.
However, the alert delivery could act as a pre-alerting tool for
aviation by pinpointing the specific aerosol conditions. A similar
approach for airport operations has been developed using automatic
lidars and ceilometers for the prediction of fog formation
The eruption of the volcano Mount Etna which began in the early hours of
30 May 2019 injected ash into the atmosphere at an altitude of
3.5–4.0
EARLINET observations at Antikythera on 2–3 June 2019:
Aerosol particles of possibly volcanic origin were monitored with the
multi-wavelength lidar of NOA over Antikythera, Greece.
The eastward advection of volcanic particles from Mount Etna presents a common pathway and has been previously investigated by means of active remote sensing
EARLINET stations that participated in the EUNADICS-AV exercise during 5–6 March 2019. The percentage of the measurements made for the 2 consecutive days and the specific temporal windows is reported. The “X” denotes the stations for which it was possible to derive the alert for aviation – i.e., the availability of a calibrated backscatter coefficient and depolarization ratio of 532
The
The identification of the source of the two aerosol layers is made
through an analysis of FLEXPART and WRF-Chem simulations. Figure
FLEXPART vertically integrated volcanic ash particles (arbitrary values) originating from Mount Etna on 3 June 2019 at 00:00
FLEXPART vertical cross section of the simulated volcanic particles (in arbitrary values) over the greater Antikythera region. The exact location of the cross section is indicated by the red line in Fig.
The application of the EWS and the timely delivery of the EARLINET
data were tested in real time during the EUNADICS-AV exercise, in which
EARLINET stations performed synchronous measurements. The EUNADICS-AV
demonstration exercise in March 2019, based on a fictitious volcanic
eruption, demonstrated that tailored observations, as well as model
services, can profitably support aviation stakeholders
In particular, 13 EARLINET stations contributed to the exercise
according to a predefined measurement schedule – i.e., from 11:00 to
17:00
WRF-Chem time–height cross section of simulated dust concentration (
Overall, the raw lidar data were streamed and processed in less than 30 min from the measurement, enabling the timely delivery of the lidar data and the tailored product when possible. Furthermore, the demonstration exercise was the first occasion in which the proposed methodology was tested in NRT, and the obtained results suggest that the network could actively support stakeholders in decision-making during an aviation crisis.
A tailored product for aviation hazards by means of high-resolution
lidar data has been proposed for the first time to our knowledge.
In particular, the methodology employs single-wavelength EARLINET high-resolution data (i.e., 532
One of the key challenges for a NRT automated alert delivery is the calibration of the backscatter and depolarization profiles as the elastic and depolarization channels are used. The EARLINET SCC ensures the absolute calibration of the lidar signals. As a source of high uncertainties in the retrieval of the particle backscatter coefficient, the inference of the lidar ratio was acknowledged. Accordingly, an iterative method has been developed to work with high-resolution lidar data, which compares well with particle backscatter coefficient profiles retrieved with the Raman method.
Additionally, and equally important in the alert delivery approach,
there is the conversion factor with which the mass concentration thresholds
are converted into a particle backscatter coefficient. The AERONET-derived conversion factors are known to be restricted by the AERONET
data inversion scheme and to underestimate large to giant particles.
Therefore, the selected conversion factor was chosen (i.e.,
The NRT operation of EARLINET during the EUNADICS-AV exercise was successfully demonstrated. The successful application of the method in NRT has been achieved during the EUNADICS-AV exercise. The raw data, upon being uploaded to the SCC server, were automatically processed and became freely accessible through the EARLINET portal and available in order to initiate the alert delivery. The exercise demonstrated the strength of the network, which, if promptly triggered, can enable measurements in the case of natural hazards for aviation.
In addition, a similar approach can be extended to lidar systems operated by the European volcano observatories. Two examples of such observatories in Europe are the Istituto Nazionale di Geofisica e Vulcanologia – Osservatorio Etneo (INGV-OE) and the Icelandic Meteorological Office (IMO). INGV-OE is responsible for monitoring Mount Etna, while IMO is responsible for monitoring all volcanic activity in Iceland.
This method is highly versatile as it can adapt to other wavelengths, and the aerosol backscatter thresholds can be set to accommodate different volcanic and desert dust scenarios by adjusting the conversion factor, the lidar ratio, the bulk density, and the mass concentration levels. In addition, even if developed on the basis of EARLINET, it can be applied to such lidar systems as those that are part of Galion (AD-Net, LALINET, MPLNET), as well as to current (CALIPSO; Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) and future (EarthCARE; Earth Clouds, Aerosols and Radiation Explorer) lidar-based satellite missions.
The code is available upon request (contact mail: nikolaos.papagiannopoulos@imaa.cnr.it).
The EARLINET data are accessible through the EARLINET database web portal
The conceptualization and design of this study were carried out by NP and LM. GD is the lead scientist and curator of the EARLINET SCC data. IM and IB created the calibration and cloud mask module for the EARLINET SCC, respectively. VA and AG are the principal investigator (PI) and data originator for the EARLINET stations of Finokalia and Antikythera, respectively. SS and AK performed FLEXPART model simulations for the Antikythera case study. AF retrieved the dust product from SEVIRI data for the Finokalia case study. AA, AC, AP, ARG, DD, DM, FM, HB, IM, LAA, NA, PF, VM, and ZM are either the PIs or the key personnel of the stations involved in the measurements exercise and ensured the high-quality operation of the respective lidars. The interpretation of results was determined from discussions involving all authors. The original draft of the paper was written by NP and reviewed and edited by all the coauthors.
The authors declare that they have no conflict of interest.
This article is part of the special issue “EARLINET aerosol profiling: contributions to atmospheric and climate research”. It is not associated with a conference.
The authors acknowledge EARLINET for providing aerosol lidar profiles (
This research has been supported by the ACTRIS-2 (grant no. 654109), the ACTRIS preparatory phase (grant no. 739530), the EUNADICS-AV (grant no. 723986), the E-shape (EuroGEOSS Showcases: Applications Powered by Europe) (grant no. 820852), the Ministry of Research and Innovation through Program I – Development of the National Research-Development System, Subprogram 1.2 – Institutional Performance – Projects of Excellence Financing in RDI (grant no. 19PFE/17.10.2018), the Romanian National Core Program (grant no. 18N/2019), and the European Commission, H2020 Research Infrastructures (D-TECT (grant no. 725698)).
This paper was edited by Eduardo Landulfo and reviewed by three anonymous referees.