Ref. Ares(2018)1080776 - 26/02/2018
East European Centre for Atmospheric Remote Sensing
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D2.3 – Methodology for atmospheric
scene classification
Start date of project: January 1st, 2016
Deliverable Identifier
Workpackage
Due Date
Actual delivery date for this deliverable
Version
Responsible
Dissemination level
Author
Duration: 36 months
D 2.3
WP 2- Enhancing Scientific and
Technological Capacity
Month 26
Month 26
1
National Observatory of Athens
Public
National Observatory of Athens
This project has received funding from the European
Union’s Horizon 2020 Research and Innovation
Programme under Grant Agreement No 692014
East European Centre for Atmospheric Remote Sensing
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Lead Beneficiary:
National Observatory of Athens
Contributing organizations:
National Institute of Research and Development for Optoelectronics
1. Introduction
This is a technical deliverable emerging from all training and transfer of expertise tasks related to
algorithms, data processing and data synergy.
The deliverable is mainly focused on methodology for atmospheric scene classification to strength
INOE’s expertise in ground based, airborne and satellite remote sensing technology and data
exploitation by transferring knowledge from NOA.
This deliverable is designed as a “bullet-points” document describing the methodology to properly
characterize the structure (and composition) of the atmosphere using a synergy of instruments.
During the last ten years, INOE has invested considerable efforts to build high-quality ground-based
remote sensing infrastructure and to train a new generation of researchers in fundamentals and
applications. The Remote Sensing Department in INOE is involved in systematic collection, processing,
archiving and production and provision of the appropriate information and services in several European
and global networks (EARLINET, ACTRIS, AERONET), having full access to the technological novelties,
standard operation procedures, algorithms and data products. ECARS will raise the level of expertise and
innovation capacity of INOE involved in atmospheric remote sensing contributing in completing the
scientific and technological background and thus making the Remote Sensing department a key player in
atmospheric research at European level.
This document provides some basic steps to be followed to properly characterize the atmospheric
scene, as these came through the training and transfer of expertise visits. The document is made of
three components: 1) the presentation of the available instrumentation, 2) the collection of the data,
and 3) the data processing and analysis followed to describe the structure of the atmosphere.
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2. Tools used for the characterization of the atmosphere
The aim is to properly characterize the atmospheric properties. When we refer to atmosphere we mean
the characterization of the clouds and the aerosols, while properties can refer to their geographical
position, altitude, quantity, size, shape, type (for example cumulus, stratus, cirrus for clouds), origin (for
example continental, dust, polluted, marine, smoke, volcanic for aerosols), as well as their radiative
signature that plays a key role in the earth’s energy budget.
Depending on the purposes, different observational instruments are used. As each instrument gives
information about specific atmospheric properties, attention should be paid on the choice of the
particular observational data as well as on the synergy among the instruments that will provide a
complete picture of the atmosphere. So, it is important to know what information provides each
observational dataset, the goal of our study and correspondingly choose the correct set and
combination of observations. The observational instruments are distinguished in passive and active
sensors according to their source of illuminating energy. Another distinction of measuring the
atmosphere is with in situ measurements and remote sensing observations depending on the distance
between the instrument and the observational target. Remote sensors are located some distance away
from the subject of interest and mainly refer to measurements from satellites. Precisely:

Passive instruments detect natural energy that is reflected or emitted from the observed scene.
Passive instruments sense only radiation emitted by the object being viewed or reflected by the
object from a source other than the instrument. Reflected sunlight is the most common external
source of radiation sensed by passive instruments. Passive remote instruments are the
radiometers, the imaging radiometers, spectrometers, spectroradiometers.
In satellite remote sensing, the sensors are looking through a layer of atmosphere. The
atmospheric constituents cause wavelength dependent absorption and scattering of radiation.
Examples of passive sensors are presented in the following.
 MODIS (moderate-resolution imaging spectroradiometer, https://modis.gsfc.nasa.gov/) is a
major international Earth Science satellite mission on board the Terra Satellite in 1999 and
in 2002 on board the Aqua. It is named for the large amount of information being obtained
about water in the Earth system. MODIS instruments are designed to provide
measurements in large-scale global dynamics including changes in Earth’s cloud cover,
radiation budget and processes occurring in the lower atmosphere. The MODIS Level3
datasets available from NASA include the characterization of aerosols, cloud water vapor,
ozone, snow cover, land surface temperature and emissivity, thermal anomalies and fires
(Figure 1), surface reflectance, albedo and most of them are available in the HDF-EOS
format. More precisely the derived products are radiances (raw and calibrated), aerosol
product, total precipitable water, cloud product, atmospheric profiles, atmosphere joint
product, atmosphere gridded product, cloud mask, surface reflectance, thermal
anomalies/fires, evapotranspiration, Albedo, Snow cover, Sea Ice and Ice Surface
Temperature, Sea Surface Temperature, Remote Sensing Reflectance.
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Figure 1 Image of wildfires in Southern California taken by MODIS on the 22 October 2007


VIIRS
(Visible
Infrared
Imaging
Radiometer
Suite)
instrument
(https://jointmission.gsfc.nasa.gov/viirs.html) succeeded MODIS on board the Suomi NPP
satellite and was launched in 2011. VIIRS is a scanning radiometer the collects imagery and
radiometric measurements of the land, atmosphere, cryosphere, and oceans in the visible
and infrared bands of the electromagnetic spectrum. VIIRS data are used to measure clouds
and aerosol properties. L1 products include data of radiance, reflectance, brightness
temperature and L2 atmospheric products include data of cloud mask, aerosol optical
thickness, cloud optical properties, albedo, snow fraction.
MISR (Multi-angle Imaging SpectroRadiometer, https://www-misr.jpl.nasa.gov/ ) is on the
Terra satellite launched on 1999 designed to measure the intensity of solar radiation
reflected by the Earth in various directions and spectral bands (Figure 2). The data gathered
from MISR are useful in climatological studies concerning the disposition of the solar
radiation flux in the Earth’s system. MISR is specifically designed to monitor the monthly,
seasonal, and log-term trends of atmospheric aerosol particle concentrations including
those formed by natural sources and by human activities, upper winds and cloud cover, type
and height, sea and land surface temperature, ice motion and temperature, fires and Earth’s
albedo.
Figure 2 The MISR instrument

The Meteosat Second Generation (MSG) program came into operation at the start of 2004
to serve the needs of Nowcasting applications and numerical weather prediction (Figure 3).
The main optical payload of MSG is SEVIRI (Spinning Enhanced Visible and Infrared Imager).
Its operating principle is based on collecting radiation from a target area and focusing it on
detectors sensitive to 12 different bands of the electromagnetic spectrum by means of a
telescope. The 12 spectral channels of SEVIRI provide precise data throughout the
atmosphere, giving improved quality to the starting conditions for Numerical Weather
Prediction models. Eight of the channels are in the thermal infrared, providing, among other
information, permanent data about the temperatures of clouds, land and sea surfaces.
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Using channels that absorb ozone, water vapor and carbon dioxide, MSG satellites allows to
analyze the characteristics of atmospheric air masses. Also, the improved horizontal image
resolution helps weather forecasters in detecting and predicting the onset and end of
severe weather.
Figure 3 Meteosat Second Generation

Active instruments provide their own energy (electromagnetic radiation) to illuminate the
object or scene they observe. They send a pulse of energy from the sensor to the object and
then receive the radiation that is reflected or backscattered from that object. Different types of
active remote sensors are: the radar, scatterometer, lidar, laser altimeter.
 CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations, http://wwwcalipso.larc.nasa.gov/ ) is a satellite launched on 2006. The CALIPSO payload is composed of
two passive imagers in the visible and infrared spectral regions and a 2-wavelength
polarization sensitive lidar CALIOP that probes the vertical structure and properties of thin
clouds and aerosols over the globe in high-resolution (Figure 4). It also provides with a
qualitative classification of aerosol size (via the wavelength dependence of the backscatter).
The lidar data contain the lidar backscatter profiles, the aerosol layer, height, thickness, the
cloud height, thickness, the aerosol extinction and optical depth, the cloud extinction and
optical depth, the cloud ice/water phase, the cloud emissivity and the ice particle size.
Figure 4 Left image: total attenuated backscatter profile. Right image: vertical feature mask for
an orbit of the CALIPSO satellite

CloudSat (http://cloudsat.atmos.colostate.edu/) is a NASA satellite launched on 2006. It
uses the Cloud Profiling Radar (CPR) instrument, a 94-GHz nadir-looking radar to advance
our understanding on cloud abundance, distribution, structure and radiative properties.
What makes CloudSat’s radar special is its sensitivity – it can see both small cloud particles
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
as well as larger raindrops and snowflakes. The CloudSat’s products are the radar
backscatter profiles, the cloud geometric profiles, cloud classification, cloud water content,
cloud optical depth, fluxes and heating rates, precipitation retrieval, ice microphysical
retrieval, snowfall retrieval. CloudSat provides statistics on the vertical structure of clouds
around the globe, provides estimates of the percentage of Earth’s clouds that produce rain,
provides vertically-resolved estimates of how much water and ice are contained in Earth’s
clouds, detects snowfall from space, estimates how efficiently the atmosphere produces
rain from condensates, and provides an indirect estimate of how much clouds and aerosols
contribute to atmospheric warming.
All the individual means of atmospheric sensing when used in synergy can provide a
complete description of the atmospheric scene. Thus, it is important to use complementary
information from different sensors describing the same scene. For that reason, we perform
the collocation of different data: in-situ with satellite data, passive with active remote
sensors, lidar with radar, etc. Attention is paid so that the synergy of different sensors is
facilitated. For example, the A-train is a constellation of six satellites (OCO-2, GCOM-W1,
Aqua, CloudSat, CALIPSO and Aura) that are spaced a few minutes apart from each other, so
their collective observations may be used to build high-definition three-dimensional images
of the atmosphere. This information would be inaccessible with the observations of a single
sensor. The orbit of each platform is maintained to provide space-time coincidence with
observations from other satellites of the constellation. CloudSat maintains a close formation
with Aqua and a particularly close formation with CALIPSO, providing near-simultaneous and
collocated observations with the instruments on these two platforms. The complementarity
of different sensors is shown with the example of the lidar CALIOP and the radar CloudSat in
Figure 5. The overlap of the CloudSat footprint and the CALIPSO footprint is within 15 sec.
Lidar (λ =532 nm) detects optically thin clouds and clouds optically thick (τ < 3), while radar
(λ =3.2mm) detects large particles or particles in high density. The combination of the two
instruments in the 2B-GEOPROF-LIDAR product can detect both thick clouds and the
integrity of optically thick clouds above the top of whom the lidar signal is attenuated (Fig.
5c).
Figure 5 An atmospheric scene as observed with radar CloudSat (top image), lidar CALIOP
(middle image) and the combined lidar-radar product GEOPROF (bottom image)
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

There are also future missions from ESA. The EarthCARE is a planned joint
European/Japanese satellite that is expected to launch in 2019. It will contain a combination
of active (lidar and radar) and passive (radiometers and imagers) instruments that will
enable EarthCARE to simultaneously measure the vertical and horizontal distribution of
clouds and atmospheric aerosols along with Top-Of-Atmosphere Long- and Short-wave
fluxes. ADM-Aeolus will be the first satellite with equipment capable of providing global
wind-component-profile observation improving thus weather forecasting and is due for
launch in 2018. Its payload is ALADIN instrument, a direct detection lidar that will produce
line-of-sight wind-component profiles above thick clouds or down to the surface in clear air
along the satellite track, as well as information on other elements like clouds and aerosols.
In situ measurements require that the instrumentation be located directly at the point of
interest and in contact with the subject of interest. Within the planetary boundary layer, in situ
instrumentation includes ground based and airborne measurements (data collection platforms,
radiosondes, UAVs, research aircrafts).
In terms of measuring atmospheric aerosols for example the surface and in-situ instrumentation
can provide complete aerosol characterization in terms of optical, physical and chemical
properties. However, they refer at the surface, thus in-situ instrumentation is not useful for
radiative transfer calculations in the atmospheric column. Passive remote sensing from ground
can retrieve accurate AODs and SSA and size distribution via inversion of multi-wavelength data.
There are also polarization techniques under development. The active remote sensing from
ground, such as the lidar, is excellent at retrieving vertical distribution. Both passive and active
measurements from ground provide point measurements only. Satellite remote sensing is only
able to retrieve a subset of aerosol properties (e.g. AOD) with high uncertainties (especially over
bright surfaces and cloudy atmospheres). Moreover, sampling is spatially or temporally limited
depending on the satellite orbit.
There are currently a few global based networks with sites distributed globally measuring the
aerosol chemical, optical or physical properties. Such networks are AERONET and EARLINET (Fig.
6).
 AERONET (Aerosol Robotic NETwork) is a network of ground-based sun photometers which
measure atmospheric aerosol properties (https://aeronet.gsfc.nasa.gov/). The
measurement system is a solar-powered CIMEL spectral radiometer that measures sun and
sky radiances at several fixed wavelengths within the visible and near-infrared spectrum.
AERONET provides continuous cloud-screened observations of spectral aerosol properties
(AOD), precipitable water, and inversion aerosol products in diverse aerosol regimes.
Inversions products are retrieved from almucantar scans of radiance as a function of
scattering angle and include products such as aerosol volume size distribution, aerosol
complex refractive index, optical absorption (single scattering albedo) and the aerosol
scattering phase function. All these products represent an average of the total aerosol
column within the atmosphere.
 GALION (GAW Aerosol Lidar Observations Network) is a global aerosol lidar network that
has the specific objective to provide the vertical component of the spatio-temporal
distribution of aerosol properties through advanced laser remote sensing in a network of
ground-based stations. GALION includes EARLINET (a European research network) consisting
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of
27
lidar
stations
hosting
more
than
(https://www.earlinet.org/index.php?id=earlinet_homepage).
44000
profiles
Figure 6 AERONET (left) and EARLINET (right) network stations

Airborne measurements can include in-situ instrumentation and airborne lidar. For
example, the FAAM Atmospheric Research Aircraft (Figure 7) has contributed in works on
radiative transfer studies in clear and cloudy air, tropospheric chemistry measurements,
cloud physics and dynamic studies, dynamics of mesoscale weather systems, boundary layer
and turbulence studies, remote sensing: verification of ground based instruments, satellite
ground truth: radiometric measurements and winds, and satellite instruments test-bed.
Figure 7 FAAM research aircraft
3. Collection of data
Depending on the goal of our study in each case we should choose the sensors the most appropriate for
our case. For example, if we want to study the properties of aerosols or clouds, their spatial or temporal
variability, their horizontal or vertical distribution, on a fine or a coarse scale, regionally or globally or for
a case study.

A useful tool appropriate especially for case studies is the Worldview created by NASA
(https://worldview.earthdata.nasa.gov/). The Worldview tool (Figure 8) provides the
capability to interactively browse global, full-resolution satellite imagery and then download
the underlying data. It also enables to view current natural hazards and events using the
Events tab which reveals a list of natural events, including wildfires, tropical storms, and
volcanic eruptions.
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Figure 8 The Worldview tool from NASA

Most of the satellite data can be obtained from the satellite’s site. There you will find a data
ordering tool where you will register as a New User, if you have not already registered.
Archives of data can be downloaded through ftp. You can also find documentation (a Data
User’s Guide) of the data products that is recommended to be read, or even software
routine examples to read the data. Usually you will have access to browse images that are
useful for data visualization or when searching for a particular scene.
For example, information about the CALISPO data as well as lidar browse images you can
find on CALIPSO web site (http://www-calipso.larc.nasa.gov/). Through the ASDC HTML Tool
(https://eosweb.larc.nasa.gov/HORDERBIN/HTML_Start.cgi, Figure 9) you can download
data from CALIPSO, CATS, CERES, MISR. Another ordering tool is through the ICARE site
(http://www.icare.univ-lille1.fr// ) where you can find POLDER/PARASOL, CALIPSO and
CloudSat data but also the CALTRACKS products that contain the coincidences issued of
many sensors (MODIS, PARASOL, CloudSat) under the CALIOP subtrack. Information of
CloudSat data products and download tool can be found in the CloudSat site
(http://www.cloudsat.cira.colostate.edu/) while information about downloading MODIS and
VIIRS data can be found here ( https://ladsweb.nascom.nasa.gov/ ).
Figure 9 The ASDC HTML Ordering Tool
(https://eosweb.larc.nasa.gov/HORDERBIN/HTML_Start.cgi)

Most of datasets are stored as HDF or netCDF files and are organized by processing level and
sensor. Hierarchical Data Format (HDF) is a set of file formats (HDF4, HDF5) designed to
store and organize large amounts of data. HDFview is a free, easy-to-use visual tool from the
non-profit
HDF
Group
for
browsing
and
editing
HDF
files:
http://hdfeos.org/software/tool.php#HDFVIEW. NetCDF (Network Common Data Form) is a
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set of software libraries and self-describing, machine-independent data formats that
support the creation, access, and sharing of array-oriented scientific data that were
originally developed by NASA. Panoply is a neCDf and HDF file viewer developed by NASA
which focuses on presentation of geo-gridded data. It is written in Java and thus platform
independent. Panoply can be downloaded at https://www.giss.nasa.gov/tools/panoply/
(Figure 10). Panoply offers a wide variety of maps projections and ability to work with
different scale color tables.
Figure 10 The Panoply platform (https://www.giss.nasa.gov/tools/panoply/)

Especially for the exploitation of MIRS data MINX (MISR Interactive eXplorer) was designed,
a GUI-based program designed to display images from MISR’s nine cameras and allows to
determine the height and speed of motion of aerosol plumes (smoke, dust and volcanic
plumes) and clouds on those images. MINX firstly used MODIS fire pixels to locate areas in
the NISR field of view where plumes are likely to be found. The source and the boundaries
of each plume, and the plume direction of transport are identified by the MINX user who
digitizes the periphery of the plume core with the mouse. The transport direction is used by
the MINX algorithm to help compute the wind speed. During the analysis phase, MINX
automatically computes smoke heights (Figure 11), wind speeds and albedos. MINX also
reads aerosol properties (e.g. Angstrom exponent, single-scattering albedo) from the MISR
aerosol standard product, and total fire radiative power and brightness temperatures from
the MODIS thermal anomaly product. The MINX tool is available at
https://github.com/nasa/MINX .
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Figure 11 Digitized plumes of a) SO2, b) oil well in nadir view, c) height profile of SO2 plume, and
d) height profile of oil plume detected by MISR on 21st October 2016
4. Case study of dust intrusion from Sahara over SE of Europe.
In this section it will be presented a case study of Saharan dust intrusion detected at the Greek
atmospheric observatory of Finokalia in Crete on the 29/04/2017 during the PRE-TECT campaign
(http://pre-tect.space.noa.gr/) that was held on that period and we examine how different sensors and
different techniques are used to characterize the particular atmospheric scene.

Firstly, the measurements from the FINOKALIA AERONET station from the sunphotometer
(Figure 12) is examined. The sunphotometer’s measurements took place only during 4:00-7:00
UTC time, then at about 9:00-9:30 and 12:30-14:00. The intermediate missing values are
probably due to the presence of clouds that block the solar radiation and thus prevent the
sunphotometer from taking measuremeants. The AOD values range from 0.15 to 0.25 and the
Angstrom exponent is 0.25-0.55 indicating the detection of desert dust. This is also evident from
the retrieval of aerosol size distribution where it is observed the important contribution of
coarse mode particles.
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Figure 12 AERONET measurements and retrievals for the Finokalia station on the 29/04/2017.
Aerosol optical depth (left image), Angstrom exponent (center image), and Aerosol size
distribution (right image)

A more complete picture of the dust scene is given with the complementarity of the PollyXT
lidar measurements (Figure 13), being part of the EARLINT network. The lidar detects a uniform
layer extending from the surface up to 2km and the depolarization measurements (~25%)
confirm that the detected layer is dust. At about 5:00 UTC a cloud layer at about 1km appears
that is present during the rest of the day (Figures not shown) and that is the reason why
AERONET gives so sparse measurements for that day.
Figure 13 PollyXT lidar measurements. Range corrected signal at 1064nm (left image) and
Volume depolarization ratio at 532nm on the 29/4/2017 at 00:00-06:00 UTC (right image)
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
Additionally, to the in-situ measurements, the dust episode was also detected from remote
sensing measurements. Figure 14 shows the same dust intrusion as detected from SEVIRI
onboard the MSG satellite, that observes an AOD of about 0.2 in accordance with the AERONET
measurements.
Figure 14 Aerosol Optical Depth on the 29/04/2017 at 04:00 UTC observed from MSG-SEVIRI

On the 29/04/2017 CALIPSO had an overpass at 00:24 UTC near the Finokalia station as shown
in Figure 15 . Here it is showed the scene classification detected from the lidar on board
CALIPSO satellite. CALIPSO observes an aerosol layer extending from the surface up to 2km of
altitude in accordance with the PollyXT retrievals.
Figure 15 CALIPSO overpass over Finokalia station and corresponding Vertical Feature Mask

We can also examine the dust scene using the NASA Worldview tool. Figure 16 shows the
Aerosol Optical Depth observed by MODIS over Terra satellite. Terra passes at 09:45 UTC above
Finokalia, when clouds are dominating the scene as observed from the PollyXT lidar. For that
reason, MODIS does not describe the dust layer. In the nearby grid boxes however, above Crete
the AOD is estimated at about 0.2 in accordance with in-situ AERONET retrievals.
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Figure 16 Aerosol Optical Depth above Crete at the 29/04/2017 observed from MODIS over
Terra satellite

Apart from the observational dataset used above to describe the dust scene in detail, often
forecasting models can provide important information about the scene under study. Figure 17
presents the dust forecast simulated by NMME-DREAM. The model predicts correctly a dust
load over Crete of about 1g/m2. Also, the simulated backward trajectories provide important
information on the origin of the air masses. This figure shows the backward trajectories of 36
hours ending at Finokalia as simulated by the NOA FLEXWRF model. The model shows that the
air masses at this day are coming from the Northwest, from the region of Libya, that
complements the observational evidence of the presence of dust transported from the Sahara
Desert.
Figure 17 Dust forecast from NMME-DREAM for the dust load in (g/m2) and wind in 2000m (left
image), and back trajectories forecast for Finokalia (+36h) simulated by FLEXPART model (right
image)
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This case study shows the added value of each instrument. The different data agree with one another
and provide with additional information complementing the characterization of the scene of interest.
The sun-photometers (CIMEL) detect a dust layer with AOD values of about 0.2. The same characteristics
of the scene are observed with the passive sensors (MODIS, MSG-SEVIRI) even if the satellites’
overpasses are not referring to exactly the same place and time with in-situ measurements. The PollyXT
lidar also detects a dust layer, being able to accurately describe its vertical distribution extending from
the surface up to 2km, in accordance with the CALIPSO detection over the area. Additionally, the use of
backward trajectories coming from forecasting models describe the origin of the observed dust layer.
The use of different data (coming from lidar and sunphotometers, in situ and satellite sensors,
observations and models) on the one hand give a complete picture of the scene under study and on the
other hand they strengthen our confidence on the retrieved characteristics of the scene.
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