AMTAtmospheric Measurement TechniquesAMTAtmos. Meas. Tech.1867-8548Copernicus PublicationsGöttingen, Germany10.5194/amt-10-3175-2017Target categorization of aerosol and clouds by continuous multiwavelength-polarization lidar measurementsBaarsHolgerbaars@tropos.dehttps://orcid.org/0000-0002-2316-8960SeifertPatrichttps://orcid.org/0000-0002-5626-3761EngelmannRonnyWandingerUllaLeibniz Institute for Tropospheric Research (TROPOS), Permoser Str. 15, 04318 Leipzig, GermanyHolger Baars (baars@tropos.de)1September20171093175320120December201610February201729May20178June2017This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/This article is available from https://amt.copernicus.org/articles/10/3175/2017/amt-10-3175-2017.htmlThe full text article is available as a PDF file from https://amt.copernicus.org/articles/10/3175/2017/amt-10-3175-2017.pdf
Absolute calibrated signals at 532 and 1064 nm and the
depolarization ratio from a multiwavelength lidar are used to
categorize primary aerosol but also clouds in high temporal and
spatial resolution. Automatically derived particle backscatter
coefficient profiles in low temporal resolution (30 min) are
applied to calibrate the lidar signals. From these calibrated lidar
signals, new atmospheric parameters in temporally high resolution
(quasi-particle-backscatter coefficients) are derived. By using
thresholds obtained from multiyear, multisite EARLINET (European Aerosol Research Lidar Network) measurements,
four aerosol classes (small; large, spherical; large, non-spherical;
mixed, partly non-spherical) and several cloud classes (liquid, ice)
are defined. Thus, particles are classified by their physical features
(shape and size) instead of by source.
The methodology is applied to 2 months of continuous
observations (24 h a day, 7 days a week) with the
multiwavelength-Raman-polarization lidar PollyXT during
the High-Definition Clouds and Precipitation for
advancing Climate Prediction (HD(CP)2) Observational Prototype
Experiment (HOPE) in spring 2013. Cloudnet equipment was operated
continuously directly next to the lidar and is used for comparison.
By discussing three 24 h case studies, it is shown that the
aerosol discrimination is very feasible and informative and gives
a good complement to the Cloudnet target categorization. Performing
the categorization for the 2-month data set of the entire HOPE
campaign, almost 1 million pixel (5 min× 30 m)
could be analysed with the newly developed tool. We find that the
majority of the aerosol trapped in the planetary boundary
layer (PBL) was composed of small particles as expected for a heavily populated
and industrialized area. Large, spherical aerosol was observed mostly
at the top of the PBL and close to the identified cloud bases,
indicating the importance of hygroscopic growth of the particles at
high relative humidity. Interestingly, it is found that on several
days non-spherical particles were dispersed from the ground into the atmosphere.
Introduction
Aerosol and clouds are important atmospheric players influencing
weather and climate. In contrast to long-lived gaseous components in
the atmosphere, these components are short lived and feature a strong
spatiotemporal variability. Aerosols act as cloud condensation nuclei
and ice nucleating particles and are thus one major driver for cloud
optical and microphysical properties and precipitation
initiation. Because aerosol is emitted from various sources and is
short-living, several aerosol types with different optical and
microphysical properties exist in different heights of the atmosphere,
influencing solar radiation and clouds in different ways. Therefore,
the climate effects of aerosol directly and of aerosols on clouds
(indirectly) are still very uncertain .
In order to better quantify the spatiotemporal distribution of aerosol
and clouds as well as to improve the determination of their
interaction, it is essential to observe aerosol and clouds, preferably in
4-D, but realistically round the clock and vertically resolved.
Active satellite-based sensors such as CALIPSO Cloud-Aerosol
Lidar and Infrared Pathfinder Satellite Observation;
, CloudSat ,
CATS Cloud-Aerosol Transport System; , and, planned
for future space missions, Atmospheric Dynamics
Mission Aeolus and
EarthCARE Earth Clouds, Aerosols and Radiation Explorer;
cover the globe but with low temporal and spatial
resolution. Thus, high-performance ground-based observations are
also needed. Scientific networks such as Cloudnet
or the ARM (Atmospheric Radiation Measurement) Climate Research
Facilities use different ground-based instruments at the
same location (supersite) to gather as much information as possible in
high temporal and spatial (i.e. vertical) resolution but at specific
locations only. Cloudnet, for example, uses as minimum instrumentation
a cloud radar, a ceilometer (a simple backscatter lidar), and
a microwave radiometer (MWR) to characterize the atmosphere above the
supersite. Cloudnet delivers several products, ranging from calibrated
measurements to microphysical cloud properties. Very well known and
widely used is the Cloudnet target categorization ,
which classifies a series of different particle types in the observed
atmospheric column (e.g. liquid droplets, ice crystals, aerosols).
However, Cloudnet is not able to
distinguish different aerosol types in its current state, which is a prerequisite to
constrain aerosol–cloud-interaction studies and to improve the
estimation of the radiative properties of aerosol.
Active remote sensing with lidar is a key technique for characterizing
aerosol and allows capturing the atmospheric state on a vertically
resolved basis usually covering the whole troposphere. For an intense
characterization of aerosol type and properties, so-called
multiwavelength lidars (MWLs) are applied using the synergistic
information from different wavelengths, scattering mechanisms, and
polarization states of the received light e.g. .
Optical aerosol properties have been widely investigated by using
lidar profiles in low temporal resolution, applying the traditional
Raman method , the Klett–Fernald
method , and the depolarization
method e.g. to determine the intensive
properties (Ångström exponent, extinction-to-backscatter
(lidar) ratio, particle depolarization ratio) of different aerosol
types and their
mixtures .
Based on such measurements, classification schemes for aerosol from
high-resolution lidar measurements have been developed for space-borne
lidars (CALIPSO, ; EarthCARE,
), airborne High Spectral
Resolution Lidar (HSRL) measurements , some
specific ground-based instruments (ARM, Darwin, Australia,
), and lidar networks focusing on the
determination of mineral dust concentration in Asia (Asian Dust
Network, AD-NET; ).
Due to recent advances in hardware, small sophisticated ground-based
MWLs e.g. PollyXT lidar systems,
, which can run unattended and autonomously
24 h a day, 7 days a week (24/7), have been developed and are
deployed globally. Motivated by this technical progress, we aimed at
developing a stand-alone tool for continuously running
multiwavelength-polarization lidars for a basic categorization of the
observed particles (targets) in analogy to the Cloudnet target
categorization. With this tool, we want to obtain an estimate of the
dominant type of backscatterer (molecules, aerosol types, clouds)
which then can be used for further intensive studies and to complement
the Cloudnet target categorization. For this approach we focus on the
derivation of certain key parameters, which are not needed with high
accuracy but are sufficient to perform a first estimate of certain
particle types in the atmosphere. The basic lidar quantities used are
the attenuated backscatter coefficients at 532 and 1064 nm
(calibrated range-corrected lidar signal) and the calibrated volume
linear depolarization ratio at 532 nm. These key parameters
are highly useful as they are available for many continuously
measuring lidar systems worldwide, e.g. the lidars within
PollyNET , AD-NET, and the space-borne lidar CALIPSO.
From these lidar parameters, further products have been developed to
allow a first-guess particle typing.
To develop this tool and demonstrate the feasibility, potential, and
limitations of this approach, we have used the unique data set
obtained during the High-Definition Clouds and Precipitation for
advancing Climate Prediction (HD(CP)2) Observational Prototype
Experiment (HOPE; ) in western Germany. The MWL
PollyXT and the Cloudnet instruments
(cloud radar, ceilometer, MWR) were deployed in the frame of the
Leipzig Aerosol and Cloud Remote Observations System LACROS,
next to each other at Krauthausen, Germany, continuously
for 2 months in Spring 2013. PollyXT is a sophisticated,
compact, scientific multiwavelength lidar to which the
quality-assurance procedures proposed by EARLINET European
Aerosol Research Lidar Network; are
applied. Without such high-quality measurements, a proper aerosol
characterization as described in the following is not possible. The
collocation of the instruments makes the derived data set a perfect
environment for developing an aerosol classification from MWL while
the Cloudnet categorization can be performed in parallel.
For the HOPE data set, we perform a so-called absolute calibration on
the lidar observations from automatically derived particle backscatter
coefficient
profiles and derive atmospheric parameters in high
temporal resolution, which allow us to estimate size and shape and
finally type of the particles in the atmosphere. This basic typing
can then be used for detecting different aerosol layers, for further
research like on aerosol–cloud-interaction processes, as input for
calibration procedures to automatically retrieve optical properties of
the observed particles (e.g. ), and finally even for
retrieving microphysical properties which
then may lead to an advanced particle categorization e.g.
HETEAC, hybrid end-to-end aerosol classification model; .
The paper is structured as follows: first, the HOPE campaign, i.e.
location and instrumentation, is briefly introduced in
Sect. . The methodology to derive quantitative lidar
parameters with temporal high resolution is explained in
Sect. . Next, the new target categorization is introduced
and intensively discussed by means of three case study days during
HOPE in Sect. . The new methodology was applied on the
complete HOPE data set and analysed in Sect. . Finally,
conclusions are drawn in Sect. .
HOPE
During the HD(CP)2 Observational Prototype Experiment
(HOPE; ), the multiwavelength-Raman lidar
PollyIfTXT
was deployed at Krauthausen (50.879746∘ N,
6.414571∘ E, 110 ma.s.l.), near Jülich,
western Germany, in April and May 2013 as part of the LACROS
facility . A detailed description of the campaign
together with the prevailing meteorological conditions can be found in
.
PollyIfTXTsystem version labelled
“IfT”, compare is an automatic, portable
multiwavelength-polarization Raman lidar with automatic calibration
procedures of the latest standards which was operated in 24/7 mode
during HOPE. The lidar emits pulses of linearly polarized light at
355, 532, and 1064 nm at a repetition frequency of
20 Hz. The receiver has seven channels detecting the elastically
backscattered light at the three aforementioned wavelengths, the
cross-polarized light at 532 nm, and the vibrational Raman
scattered light at 387, 407, and 607 nm. With
PollyIfTXT, aerosol profiles can be obtained
with 30 m vertical and 30 s temporal resolution. The
full overlap between the laser beam and the receiver field of view is
about 1500 m; thus, an overlap correction is
applied below this height. The lidar was operated in photon-counting
mode. The system is pointed 5∘ off-zenith to prevent the
detection of specular reflection by the planar planes of horizontally
oriented ice crystals . A detailed
description of the system including a quality assessment can be found
in .
Furthermore, a cloud radar, a Doppler wind lidar, a ceilometer, and an
AERONET (Aerosol Robotic Network) sun photometer were deployed next to
the lidar as part of the LACROS facility. From these instruments,
Cloudnet products and AERONET
products are available. Because of radar scanning
experiments during HOPE in Jülich, Cloudnet products which require
vertically pointing measurements are sporadically unavailable for
this campaign.
Methodology
In modern multiwavelength lidars, a number of different receiving
channels are installed to make use of as much information from the
atmosphere as possible (elastic and Raman [inelastic] scattering,
change in polarization state of emitted light due to scattering,
etc.). In this way, high-quality aerosol products are obtained on
a vertically resolved basis. However, because of the high background
noise, Raman lidar observations during daytime are
challenging. Therefore, for continuous (24/7) measurements, we
concentrate on the use of channels for elastic backscattering,
including depolarization. The key challenge to succeed with automated
aerosol retrievals is the calibration of the lidar signals. There are
two main tasks necessary before an automated aerosol target categorization can
be performed: the calibration of the backscatter profiles and the
calibration of the depolarization products.
Calibration of backscatter
The backscatter signal strength P (number of counted photons per 30 s and 30 m in the case of the lidar system used here) for a certain range R at the
wavelength λ can be described for each channel by
PλR=CλOλ(R)R2βparλR+βmolλR×exp-2∫0Rαparλr+αmolλrdr,
with the wavelength-dependent lidar system parameter Cλ
containing all instrument-relevant quantities, the overlap function
Oλ(R), the molecular (subscript mol) and
particle (subscript par) backscatter coefficient β,
and the atmospheric transmissivity described by the molecular and
particle extinction coefficient α. The molecular backscatter
and extinction coefficients can easily be calculated from pressure and
temperature profiles obtained from radio soundings or model output
with well-known scattering formulas . For usual
lidar applications, the particle backscatter coefficient is obtained
by applying the Raman or Klett–Fernald
method to the received signals. With
these methods, the lidar signal is calibrated in a certain height
range of the atmosphere for which only molecular scattering is
assumed. However, these methods require appropriate weather conditions
(e.g. no low-level clouds) and temporal averaging over typically at
least 30 min to increase the signal-to-noise ratio (SNR) in
the calibration height region. Thus, for temporally high-resolved
24/7 aerosol analysis, these methods are not applicable. Therefore,
we perform an absolute lidar calibration by deriving the lidar system
parameter Cλ to obtain foremost the attenuated backscatter
coefficient.
For the measurements performed during HOPE, Cλ was derived
from particle backscatter coefficient profiles, which were
automatically computed with the Raman or Klett–Fernald methods at
30 min resolution as described in . From
these profiles, Cλ(R) can be calculated by rearranging
Eq. () to
Cλ(R)=PλRR2βparλR+βmolλROλ(R)×exp2∫0Rαparλr+αmolλrdr.
The final Cλ is computed as the mean value of a height
window of 1000 m above the full overlap height (i.e.
1500 m in the case of PollyIfTXT) and is
considered to be height independent. Cλ is valid for the recorded raw resolution of 30 m, 30 s, and repetition rate of 20 Hz of the lidar system used here. For the automatically retrieved
particle backscatter profiles from the Polly systems, all known
instrumental issues (e.g. background subtraction) which could cause
height-dependent effects were corrected, except for the partial overlap
in the lowermost part of the lidar profile described by
Oλ(R), which is a substantial feature of each lidar system.
For that reason and because the particle extinction coefficient
derived with the Raman method is only available during nighttime, we
introduced a two-step approach to estimate the particulate transmission
needed to solve Eq. (). First, we calculate the particle
extinction coefficient profile derived from the particle backscatter
coefficient profile (Raman or Klett – depending on time of day)
multiplied with a constant lidar ratio of 55 sr as a good
compromise of the lidar ratio values observed during HOPE and at other
European continental sites clean and polluted continental
aerosol, desert dust, and smoke;
. Second, we
assume height-independent extinction below 500 m to account
for both the incomplete overlap within the lidar profile and
atmospheric variability in the lowermost troposphere. At
500 m, already more than 80 % of the overlap between
the laser and the telescope field of view is reached and the applied
overlap correction profile can correct the signal with high accuracy up to
the full overlap height. Finally, an extinction profile from the
surface up to the height of interest is available to calculate the
particulate transmission in Eq. ().
Figure shows the daily mean lidar system
parameters calculated as described above for the HOPE campaign. For
some days, no calculation was possible due to unfavourable weather
conditions and thus the unavailability of automatically retrieved
backscatter coefficient profiles for calibration. Vertical dashed lines indicate
set-up changes in the lidar. Even though we tried to minimize set-up
changes (neutral-density filters, overlap adjustment, laser energy,
emission-window cleaning), several changes were necessary but not
always influencing the derived lidar system parameter.
Lidar system parameter Cλ for 355, 532, and 1064 nm. Cλ is given
for the photon counts of the recorded raw resolution of 30 m, 30 s, and repetition rate of 20 Hz corrected for the range dependency (R2) in metres. Vertical lines indicate lidar set-up changes.
One can see that the lidar system parameter is relatively stable and
only some of the set-up changes have caused a significant change in
Cλ. However, there are also periods were there was
a significant change of Cλ even without changes in the
set-up, e.g. between 21 April 2013 and 1 May 2013. It was found that
changes in the indoor temperature of the cabinet due to air
conditioning malfunctioning had led to a change of the alignment
(e.g. the overlap between the receiver field of view and the laser
beam) and thus a change in Cλ during this period. On
2 days (25 April and 10 May), the corresponding data were therefore
partly not considered in the analysis. The daily mean lidar system
parameter can finally be obtained with an SD
(standard deviation) of less than
20 %. The relative change of the lidar system parameter is
similar for all three wavelengths, even though it looks different in
Fig. due to the scaling applied. On 3 days
(18, 25 April, and 10 May), for which multiple system set-up
changes were performed, more than one lidar system parameter was used
to account for these set-up changes. In all other cases, the daily mean
system parameter was used when available, otherwise the closest lidar
system parameter from the days before or after was applied, to calculate
the calibrated attenuated backscatter coefficient derived by dividing
the range-corrected signal with the lidar system parameter:
βattλ(R)=PλRR2Cλ=βparλR+βmolλR×exp-2∫0Rαparλr+αmolλrdr.
Calibration of depolarization ratio
Daily depolarization calibration factor V* as derived during HOPE.
The calibration of the depolarization measurements of
PollyXT systems is done with the so-called
Δ90∘-method in agreement with
EARLINET standards. For this purpose, a motorized filter wheel is
implemented in the receiver unit of PollyXT to perform the
Δ90∘ calibration automatically three times a day. The
procedure delivers the calibration constant V*, which was found
to be very stable for HOPE as shown in Fig. . It relies on
the ratio of two signals and thus is invariant against most changes in
the lidar set-up (e.g. laser power, overlap). For days at which
inappropriate weather conditions did not allow the determination of
V*, a standard value (mean of HOPE) is used. Only changes in
the neutral-density filter set-up of the polarization channels will
affect the depolarization calibration constant, which was not the case
during HOPE. Thus, we consider the calibration as very accurate with
an SD of less than 8 % as seen in Fig. . By
knowing the lidar-system-dependent transmission ratios
Dc and Dtotsee of
the cross and total channel, respectively, the volume linear
depolarization ratio is derived without any further assumptions by
δvolλ(R)=V*-δλ(R)δλ(R)Dtot-V*Dc
with
δλ(R)=Pcλ(R)Ptotλ(R),
where Pcλ and λPtot are the cross-polarized and total lidar signals, respectively. In the case of HOPE, depolarization measurements are available at λ=532nm.
Aerosol characterization
The methodology to derive the lidar system parameters was based on
30 min averaged profiles of the particle backscatter
coefficient, which are only available for specific atmospheric
conditions. For the target characterization aimed at in this paper,
24 h measurements with 5 min resolution are analysed
to characterize aerosols and clouds. The received signals of the
backscattered light at 532 and 1064 nm and the depolarization
ratio at 532 nm are used for this purpose. In the following,
the methodology is introduced and then explained in detail in terms of
a case study from HOPE.
Obtaining aerosol products – extensive properties
Since the molecular backscatter and extinction coefficients can be
calculated from temperature and pressure profiles and the lidar system
parameter can be estimated as described above, only the particle
extinction coefficient, i.e. the transmission through the atmosphere,
is left as an unknown in Eq. () to retrieve the particle
backscatter coefficient. As a first guess for the particle
backscatter coefficient, the particulate attenuation in the atmosphere
is neglected, which reduces Eq. () to
quasi*βparλR=βattλRexp2∫0Rαmolλrdr-βmolλR.
To account for the incomplete overlap of the lidar system in lower
heights, an overlap correction function is applied and
height-independent backscattering below 500 m is assumed in
analogy to the calculation of the lidar system parameter
Cλ. The particle extinction coefficient is now estimated
in analogy to the procedure during the calculation of Cλ by
multiplying quasi*βparλR with a constant lidar ratio of
Spar=55sr:
quasiαparλr=quasi*βparλRSpar.
As explained already in Sect. , the lidar ratio value
used serves as a good compromise for lidar ratio values observed
during HOPE and at other European continental sites. Finally,
temporally high-resolved profiles of the so-called quasi-particle-backscatter coefficient defined as
quasiβparλR=βattλR×exp2∫0Rαmolλr+quasiαparλrdr-βmolλR≈βparλR
can be calculated, which serve as best estimate for the particle
backscatter coefficient βparλR
determined with the Raman or Klett method as demonstrated in
Sect. . The quasi-particle-backscatter coefficient at 532
and 1064 nm is then used as the input for the particle
characterization described below. An iterative approach for the
determination of the particle extinction coefficient using the
formulas above is not possible, because the solutions do not converge
if the input lidar ratio is not exactly identical to the lidar ratio
valid for the observed scatterers. If the input lidar ratio is higher
than the atmospheric one, the extinction coefficient and thus also the
backscatter coefficient is, in general, overestimated, and the procedure
quickly approaches unstable solutions. On the other hand, if the lidar
ratio input is too low, too small values that do not increase during the
procedure are obtained. This behaviour is similar to the so-called
Klett–Fernald forward iteration , which
also relies on a priori information of the lidar ratio and can be
numerically unstable.
Obtaining aerosol products – intensive properties
With the calibration methods described above, a rough but temporally
high-resolved aerosol characterization can be done by using the
quasi-particle-backscatter coefficients and the volume depolarization ratio
to obtain intensive aerosol-type specific quantities. From the
quasi-particle-backscatter coefficients, the quasi-particle Ångström exponent
quasiåparλ1/λ2=-lnquasiβparλ1quasiβparλ2lnλ1λ2
is calculated for the wavelength pair λ1 and
λ2, e.g. 532 and 1064 nm, to obtain information
on particle size.
The quasi-particle depolarization ratio defined as
quasiδparλR=δvolλR+1×βmolλRδmolλ-δvolλRquasiβparλR1+δmolλ+1-1-1
is also an intensive property and used to obtain information about the
particle shape. The molecular depolarization ratio δmolλ is calculated theoretically from the
bandwidth of the interference filters (e.g. see )
and is 0.0053 at 532 nm in the case of
PollyXT.
Example observation: 22 April 2013
To demonstrate the introduced quantities, the time-height cross
sections of the four possible extensive (Fig. )
and four possible intensive (Fig. ) particle
quantities of PollyIfTXT are shown for 1 day
of HOPE – the 22 April 2013.
Polly observations at Krauthausen on 22 April 2013. Extensive properties from top to bottom: quasi-particle-backscatter coefficient at 355, 532, and 1064 nm, and volume depolarization ratio at 532 nm.
Polly observations at Krauthausen on 22 April 2013. Intensive properties from top to bottom: quasi-particle Ångström exponent for the wavelength pairs as indicated and quasi-particle depolarization ratio.
The daily mean aerosol optical depth (AOD) was 0.34 at 500 nm
wavelength on this day and thus was comparably high (monthly mean was
0.19). The atmospheric features are very well seen at 1064 and
532 nm while at 355 nm the atmospheric conditions are
obviously not well represented, which will be explained later in
detail. The 22 April 2013 started with a stratiform cloud with its
base between 1.5 and 2.5 km which prevailed until
04:00 UTC. Below the cloud, inhomogeneous aerosol structures
can be seen. The cloud is characterized by a high quasi-particle-backscatter
coefficient at all wavelengths and a high volume depolarization ratio.
After 04:00 UTC, a cloud-free nocturnal residual layer was
observed. Note the layer structure, which indicates a slow descent of
the lofted aerosol layer. At around 10:00 UTC (12:00 LT),
the growth of the convective PBL could finally be observed. The PBL
reached up to 2–2.5 km on this day. At 20:00 UTC, the
nocturnal PBL began to form as can be seen below 1 km height
in Fig. . No low-level or mid-level clouds
were observed after 04:00 UTC, but cirrus at altitudes above
6 km (not shown) appeared after 13:00 UTC. From the
temporal development of the volume depolarization ratio
(Fig. , bottom), one can see that particles producing
enhanced depolarization were mixed into the PBL from shortly after
12:00 UTC. A maximum of the quasi-particle depolarization
ratio was observed at 16:30 UTC below 1.5 km height
(Fig. , bottom). Obviously, non-spherical
particles were mixed from the surface into the PBL as
will be discussed further below.
The quasi-particle depolarization ratio is also enhanced at the lower cloud
boundaries due to multiple scattering and/or because of falling ice
crystals. The three quasi-particle Ångström exponents
(Fig. ) exhibit a very different behaviour showing
that the Ångström exponents incorporating the quasi-particle-backscatter coefficient at 355 nm are not representative. This
is due to the corrections and assumptions made to estimate the
particulate extinction and finally the quasi-particle-backscatter
coefficient. As at 355 nm molecular backscattering is 80 (5)
times higher than at 1064 (532) nm, large uncertainties are
introduced into the attenuation correction presented in
Sect. when 355 nm signals are considered, even
though the lidar system parameter is known with good accuracy. The
partial negligence of particulate extinction in the first-guess
profile (Eq. ) and the subtraction of the molecular
scattering contribution leads often to very large errors (as molecular
backscattering is usually much stronger than particle backscattering
at this wavelength) with even negative quasi-particle-backscatter
coefficients. These effects are illustrated in
Fig. for a 30 min period of 22 April
2013. The particle backscatter coefficients determined with the Klett
method, the attenuated backscatter coefficients, and the quasi-particle-backscatter coefficients are shown for the different
wavelengths.
Comparison of the particle backscatter coefficient
determined with the Klett method, the attenuated backscatter
coefficient, and quasi-particle-backscatter coefficient for
the three laser emission wavelengths on 22 April 2013,
14:20–14:50 UTC. Additionally, the quasi-particle-backscatter coefficient with a different approach for
attenuation correction (extinction coefficient derived from
the 1064 nm extinction coefficient with the
Ångström relation) is plotted for Ångström
exponents of Å=1.0, 1.4, and 2.0
We have also considered other approaches to estimate the extinction at
355 nm for the calculation of the quasi-particle-backscatter
coefficient (cp. Eq. ), for example, by using the
Ångström relationship to
convert the 1064 nm extinction with an assumed a priori
extinction-related Ångström exponent to the extinction
coefficient profile at 355 nm similar to Eq. ().
Three different Ångström exponents were chosen which are
representative for the HOPE campaign, i.e. 1.0, 1.4, and 2.0, to
obtain the extinction at lower wavelengths from the extinction at
1064 nm. This procedure is illustrated also in
Fig. , where additionally the three backscatter
coefficient profiles derived with this methodology are plotted. However, with that approach it was also found
that the a priori choice of the
extinction-related Ångström exponent is so crucial at
355 nm that it cannot be applied in an automatic retrieval
(e.g. see profile derived with an Ångström exponent of 2.0
at 355 nm). Closest to the particle backscatter coefficient
at all wavelengths is the quasi-particle-backscatter coefficient
derived with the methodology described in
Sect. (without the Ångström exponent assumption
for extinction estimation). Taking into account the satisfying
results at 1064 and 532 nm with this approach, one can
conclude that the quasi-particle-backscatter coefficient is a better
estimate than the attenuated backscatter coefficient for particle
backscattering in the atmosphere.
Comparison of Ångström exponents derived from the particle backscatter coefficients determined with the Klett method, the attenuated backscatter coefficients,
and the quasi-particle-backscatter coefficients for 22 April 2013, 14:20–14:50 UTC. A five-bin vertical smoothing was applied.
This finding is also proved when comparing the different
Ångström exponents as done in
Fig. . Here, the Ångström exponents
derived from the quasi-particle-backscatter coefficients at 532 and
1064 nm (deep yellow with stars) are very similar to the ones
obtained from the particle backscatter coefficients derived with the
Klett method (black, blue, and red, all close to 1.4 and
height independent for the aerosol layer up to 2 km). However,
the Ångström exponents using the 355 nm
quasi-particle-backscatter coefficients show already significant deviations (avocado
green and purple) from the aforementioned value of 1.4. Even worse
are the results when the attenuated backscatter coefficients are used
(dark brown, orange, and magenta with circles), which shows again that
this quantity cannot be applied for particle typing by using multiple
wavelengths.
Consequently, we apply the quasi-particle-backscatter coefficients at 532 and
1064 nm, which are straightforward to determine and which are close to
the atmospheric truth; the corresponding quasi-particle Ångström
exponent; and the quasi-particle depolarization ratio at 532 nm
for the temporally high-resolution target categorization.
Typing
For the typing of atmospheric features, i.e. the optical dominant
scatterer type, three extensive (quasi-particle-backscatter coefficient at 532
and 1064 nm and volume depolarization ratio) and two
intensive properties (quasi-particle Ångström exponent and
quasi-particle depolarization ratio) are available to detect aerosol and
cloud layers and to distinguish between those two and classify
subtypes. The lidar-only attempt is made to categorize aerosols and
clouds concerning different types in analogy to the Cloudnet
classification. In the following, the methodology is described
followed by an intensive discussion concerning the applicability by
means of example cases of HOPE.
Typing methodology
The complete typing procedure based on the quasi-particle-backscatter
coefficients, depolarization ratios, and Ångström exponent
profiles is illustrated in Fig. and listed in
Table .
Schematic illustration of the typing procedure. Details in text and Table .
Overview of particle typing. Criteria for the feature classes are given. Quasi-particle-backscatter coefficient values are given in m-1 sr-1.
The lidar-only classification consists of the following main classes:
clean atmosphere, non-typed particles/low concentration, non-typed
clouds, small spherical particles, large spherical particles, aerosol
mixture, non-spherical particles, ice clouds, and liquid clouds. The
“clean atmosphere” class represents a Rayleigh atmosphere where pure
molecular scattering can be assumed. As the a priori
information (i.e. the lidar ratio assumed) used to derive the
quasi-particle-backscatter coefficient is valid for aerosol particles only; we do
not aim to make a complete cloud classification. However, the
quantities available for typing are mostly representative to identify
the bases of ice clouds and liquid clouds. Attenuation correction at
the base is not crucial, so the assumption of a wrong lidar ratio
does not play a major role. However, we do not attempt to identify any
particle classes above a liquid cloud as the attenuation correction
will be corrupted.
Optical thick clouds are identified using the Cloudnet scheme for
droplet finding . As the lidar
cannot penetrate liquid clouds, we cannot detect the cloud top, in
contrast to Cloudnet, which uses the cloud radar information to gather
this value. Therefore, the lidar target categorization will detect the
cloud base and hydrometeors some tens of metres above the base. In
principle within this scheme, clouds are detected if the quasi-particle-backscatter
coefficient at 1064 nm is higher than 2×10-5m-1sr-1 and the signal decreases by a factor
of 10 within 250 m above the maximum backscatter value. This
algorithm is applied profile by profile, and the corresponding pixels
above the threshold are flagged as non-typed cloud. Additionally, if
the quasi-particle depolarization ratio is below 0.05, the pixels are
flagged as most-likely droplets, while, if the Ångström
exponent is also less than 0.5, the pixels are flagged as droplets. The
quasi-particle-backscatter coefficient threshold for clouds of 2×10-5m-1sr-1 accounts for an extinction coefficient
of about 3.6×10-4m-1 at all wavelengths (lidar
ratio of 18 sr for water droplets, Ångström exponent
of 0 for large particles). According to the OPAC
database , an extinction coefficient value of
3.6×10-4m-1
is higher than the
values at 550 nm given for all aerosol types except for strong
pollution. According to , a threshold of 1×10-5m-1sr-1 at 1064 nm is well suited for
the discrimination between cloud and aerosol because the largest
overlap between the two types is between 4×10-6 and 1×10-5m-1sr-1. The automatically retrieved
particle backscatter coefficient profiles as presented in
showed that during HOPE aerosol particle backscatter
coefficients did not exceed 1×10-5m-1sr-1
(95 % percentile maximum at 3×10-6). Thus, we
consider the chosen threshold as valid for the conditions during HOPE
without overlapping of the categories. Visual inspection showed no
misclassification of liquid clouds, which convinces us that the
approach is valid for the detection of cloud bases. As soon as a liquid
or non-typed cloud is classified, no other classes above are evaluated
because of the risk of strong attenuation, multiple scattering, etc.,
which disturb the signals significantly as the lidar applied is
designed for aerosol and not for cloud detection.
We classify the atmosphere as clean if the quasi-particle-backscatter
coefficient at 1064 nm is less than 1×10-8m-1sr-1 and a valid signal of the
355 nm quasi-particle-backscatter coefficient (SNR > 0.5 at raw
resolution of 30 s) is present. This threshold yields a ratio
of molecular to particle backscattering at 532 (355) nm higher
than 60 (180) at sea level and thus is valid for a Rayleigh
calibration by means of the Raman or Klett–Fernald lidar method. One
future application of the target categorization presented herein might
be to find appropriate regions for Rayleigh calibration, i.e. height
regions of almost pure molecular scattering and sufficiently high SNR.
The threshold of 1×10-8m-1sr-1 is also
well below the given range for aerosols according to
for the CALIPSO classification. As the
PollyXT systems have a higher detection sensitivity than
CALIPSO, we cannot consider a higher threshold for clean atmosphere
with Rayleigh scattering only. Anything above this threshold is first
classified as non-typed particles, which could be aerosol or clouds.
Aerosol and ice clouds are typed for a quasi-particle-backscatter coefficient
at 1064 nm greater than 2×10-7m-1sr-1. Everything below remains classified
as “non-typed particles/low concentration”. The threshold is equivalent
to the one used in the CALIPSO feature mask 5×10-7m-1sr-1 for the 532 nm attenuated
backscatter coefficient, when considering an
Ångström exponent of 1.4 as measured by AERONET
on average during HOPE.
If the quasi-particle depolarization ratio is less than 0.07 and the
quasi-particle Ångström exponent ≥0.75, the scatterers are
considered to be small particles. If the Ångström exponent
is lower, it is supposed that large particles dominate the optical properties in the atmospheric volume.
A mixture of non-spherical and spherical particles is considered when
the particle depolarization ratio is between 0.07 and 0.2, while above
0.2 the particles are categorized as large and non-spherical. The
thresholds for the aerosol typing are chosen according to
and , whose analyses of
observations at several EARLINET stations show that large particles
(marine, dust) have an Ångström exponent
(532–1064 nm) less than 0.75 while smaller particle types
(smoke, polluted continental, etc.) have an Ångström
exponent (532–1064 nm) larger than 0.75. Pure Saharan dust is
supposed to have a particle depolarization ratio at 532 nm of
31 %, but lower
ratios
were also observed (e.g. around 28 %; ).
Therefore, we consider particle depolarization ratios higher than
20 % as mostly containing dust (or other non-spherical
particles) and thus classify the scatterers as large, non-spherical
particles. According to , a 20 % particle
depolarization ratio corresponds to a dust fraction in terms of
backscattering of more than two-thirds. A particle depolarization
ratio of 7 %, on the other hand, corresponds to a dust
fraction of less than 20 %.
In contrast to other classification schemes (e.g. CALIPSO,
; HSRL, ), we do not categorize by
aerosol origin (e.g. mineral dust, biomass burning smoke, etc.), but
by physical features. For example, large, non-spherical particles are
in most cases mineral dust advected to the site but could also be
volcanic ash, pollen, or local dust plumes. The interpretation is not
possible without additional information and thus will be left to the
user of the categorization. We want to focus on the physical
properties as these are the quantities we can obtain with this
lidar-only approach.
Ice crystals, as they occur in cirrus clouds or virgae, are identified
by their highly depolarizing properties independent of the cloud
identification or the aerosol typing and thus may overwrite these
classes. As cirrus may be optically very thin, the same backscatter
coefficient threshold as for aerosol is used to find ice crystals.
The class “likely ice” is identified if the volume depolarization
ratio (independent of quasi-particle-backscatter coefficient) is higher than
30 %. “Ice crystals” are identified if the particle
depolarization ratio is higher than 35 % and may overwrite
the “likely ice” class. However, the identification of ice crystals
is the most critical matter, as sometimes the depolarization
information at 532 nm is not available due to the low SNR,
whereas with the 1064 nm channel these particles can be
detected. Thus, many ice crystals remain unclassified and are
categorized as non-typed particles or clouds.
In the next section, we want to demonstrate the performance of the
newly developed target categorization by means of three example cases.
Examples for the aerosol categorization
In the following, the observation days of 22, 4, and 18 April
during HOPE are discussed by means of the lidar target
categorization. These example cases represent a wide variety of
different meteorological situations and are therefore well suited to
demonstrate the capabilities of the newly developed lidar target
categorization.
22 April 2013
Lidar particle categorization for 22 April 2013.
Figure shows the newly developed MWL
classification scheme for the example day of 22 April 2013, which was
already presented in Sect. . Several features were successfully detected: between 00:00 and
04:00 UTC, the cloud base of the liquid cloud was successfully
identified. The base was categorized as “Cloud: likely water
droplets” (light blue). Due to the required a priori assumptions for
the quasi-particle-backscatter coefficients which are aiming at aerosols, the
Ångström exponent was not below 0.5 and thus the “Cloud:
water droplets” requirements were not fulfilled. Above the cloud
base, the depolarization ratio is slightly enhanced due to multiple
scattering (see Fig. , bottom) and thus the cloud
is classified as a “non-typed cloud”. Below the cloud, at the top of
the PBL, large aerosol (orange) is identified above small aerosol
particles (yellow) due to the low Ångström exponent
(532/1064 nm, see Fig. ). The growth of
aerosol with increasing altitude within the PBL is most probably
caused by hygroscopic growth. After 04:00 UTC, the cloud deck
dissolved, and an aerosol layer with mostly small particles but large
particles at the top remained the whole day. In addition, a small cumulus cloud
was observed shortly past 12:00 UTC at the top of
the convective PBL, remaining the only cloud at daytime on this day.
The aerosol layer top and thus also the PBL top reached its maximum
with 2.2 km at around 19:00 UTC before the aerosol
layer starts to decay. We have to note that from the lidar target
categorization the identification of the PBL, i.e. the mixing layer
height, is not possible and needs additional information; therefore, we
refer with the term PBL to the main aerosol layer which might have
very often coincided with the mixing layer during daytime.
An interesting feature is the entrainment of partly non-spherical
particles (brown) between 16:00 and 19:00 UTC from the
surface. After 19:00 UTC, these non-spherical particles were
detected close to the top of the nocturnal aerosol layer. The source
of these non-spherical particles could be local dust (from open-pit
mining close by, see Fig. 2 in ) and/or pollen from
the local agricultural activity (e.g. see Fig. 1b in
). Such entrainment from ground was very often
observed in April at Krauthausen and needs to be investigated further
in the future. Above the main aerosol layer, some aerosol, but in low
concentration, is identified (dark grey), which means that these regions
are not suitable for the so-called Rayleigh fit
needed for the Raman or Klett–Fernald lidar method for which one needs
regions of molecular scattering only (light grey).
Cloudnet target categorization for 22 April 2013.
Lidar particle categorization (a) and Cloudnet target categorization (b and c) for 4 April 2013 at Krauthausen and Jülich.
For comparison, Fig. shows the standard Cloudnet
classification which is derived from cloud
radar, microwave radiometer, and ceilometer observations. This classification allows us to distinguish between the different
cloud types and to detect aerosol. However, no discrimination between
aerosol types is possible. At around 2 km between 00:00 and
03:00 UTC, a supercooled liquid layer was clearly observed
(slightly above the lidar-detected cloud base). Below, ice crystals
were identified, which turned into liquid at about
1.2 km. According to temperature profiles retrieved from
GDAS1
Global Data Assimilation System,
https://www.ready.noaa.gov/gdas1.php
for the lidar location,
the 0 ∘C altitude was 1.4 km, confirming the
findings. The identification of the liquid droplet layer by Cloudnet
shows that the detected cloud features by lidar are certainly mostly
liquid droplets and thus confirm the correct classification by the
lidar categorization. The lidar, however, did not identify drops or ice
below the cloud, most probably due to the low concentration of these
hydrometeors for which the lidar is not sensitive. After
04:00 UTC, Cloudnet classifies aerosol only. The small cloud
layer as observed with the MWL is also detected shortly past
12:00 UTC.
Finally, we can conclude the lidar-only target categorization works
well and is in agreement with Cloudnet even though the different
instrumentations allow the detection of different atmospheric features
as intensively discussed in the next case study.
4 April 2013
A second example case to be discussed is 4 April 2013 at
Krauthausen. The MWL target classification (top) and the Cloudnet ones
(centre and bottom: LACROS and JOYCE) are shown in
Fig. . JOYCE (Jülich ObservatorY for Cloud
Evolution; 3 km away) data are shown because no data from
Cloudnet are available for LACROS past 17:00 UTC due to
maintenance work on the cloud radar. Nevertheless, the most
interesting feature on this day is the overcast cloud condition
between 03:00 and 10:00 UTC. During this time, the MWL
classification detects very well the cloud base (cloud or likely
cloud) and large aerosol below. The Cloudnet classification, however,
detects the liquid cloud base as well, but it classifies below ice and
super cooled droplets and/or ice not touching the ground. According to
the temperature profile derived from the GDAS1 data set, a strong
inversion was present between 1.8 and 2.2 km and temperatures
were below 0 ∘C throughout the troposphere. Thus, both
classifications are reasonable, and one could suppose that the ice and
drizzle detected by the radar led to evaporation which increased the
relative humidity (RH) in the aerosol layer and led to hygroscopic
growth and finally, as detected, to large, spherical aerosol
particles. As at the cloud base 100 % RH can be considered,
the particles just below the cloud experienced high RH, and thus
a strong particle growth has most likely led to increased
scattering e.g. .
This example shows the different sensitivity concerning particle size
and thus the potential synergy between the lidar- and radar-based
classifications. While the lidar is more sensitive to the numerous but
comparably small aerosol particles, the radar is most sensitive to the
few but large precipitation particles. If we assume a Marshall–Palmer
rain droplet number size distribution , we can estimate
the light extinction of the drizzle in dependence of the rain rate as
shown in Fig. . For low rain rates, which have
occurred in the case of 4 April 2013 because no precipitation reached
the ground, extinction coefficients well below typical aerosol values
are calculated. Aerosol extinction in the PBL was about 150 to
200 Mm-1 throughout the observation time in the case
presented here. At a height of 1.5 km, which is 250 m
below the cloud base, extinction coefficients of about
100 Mm-1 were observed at 04:00 UTC. When no clouds
were present at 01:00 UTC, they were 35 to 50 Mm-1
at this height. Thus, if one considers hygroscopic growth, one can
conclude that the lidar signal was dominated by aerosol instead of the
few drizzle droplets even though they also contributed to the lidar
return. On the other hand, as the radar is sensitive to the sixth
power of the diameter of the scatterers (while the lidar is to the
power of 2), it is sensitive to the few but large precipitation
droplets. Therefore, the Cloudnet classification defines the region of
interest to contain ice and supercooled drops and ice only – putting
the priority on the cloud-sensitive radar observations. Given the added value of the multiwavelength lidar aerosol
classification, we can however conclude that between 03:00 and
10:00 UTC all detected features, i.e. large, spherical
aerosol particles and ice and supercooled drops, were present
simultaneously, even though the full instrument synergy of the
instruments presented here is still a current research topic.
Simulated light extinction coefficient for drizzle in dependence of rain rate.
Past 11:00 UTC, another cloud with its base at around
1 km was detected at the top of the growing PBL. Again, the
cloud base is identified with lidar at the height at which Cloudnet
identifies cloud droplets only. Above and below the cloud base,
Cloudnet classifies ice crystals, which cannot be verified with the
MWL target categorization. There, mostly small, but also some large,
spherical particles close to the cloud base are identified. Above the
cloud base, no valid lidar signal is available.
Past 16:00 UTC, the lidar detected ice clouds from 2.5 to
6 km height, which was observed with Cloudnet instrumentation too.
Cloudnet is able to detect the ice clouds already before at
altitudes up to 9 km, which is not possible with the MWL during
the low-level-cloud-deck period. Interestingly, during the period past
16:00 UTC, a lofted aerosol layer was found below the ice
cloud between 2 and 3 km classified mostly as spherical
particles. Below, in the transition zone to the PBL, non-spherical
particles were identified because of an increased depolarization
ratio. In the PBL itself, small, spherical particles were
observed. The Cloudnet observations from JOYCE only 3 km away,
however, gave no indication of ice crystals at this altitude, so
we can conclude that the non-spherical particles were advected towards
the site.
Interestingly, at around 17:00 UTC, large, spherical particles
are directly classified below/within the ice cloud at around
3.5 km because of low depolarization values. We can only
speculate that due to evaporation of ice crystals, residual aerosol
might have grown. Unfortunately, the radar at LACROS was not in
operation to investigate this feature in more detail.
As can be seen as well in Fig. a, ice crystals are often classified correctly but sometimes remain unclassified or are even falsely classified as aerosol. The reason for the non-classification of ice crystals is mostly the lack of depolarization information at 532 nm while the 1064 nm channel is able to detect particles especially at high altitudes at which the SNR of the 532 nm channels is too low. This occurs, e.g. for the thin ice cloud at about 10 km past 21:30 UTC.
The frequency of occurrence of misclassification of ice crystals as
aerosol is increasing with increasing penetration depth of the ice
clouds as can be seen in Fig. a past 16:00 UTC
in the height range of 4–7 km. The reason for that false
classification is the used a priori information aiming on aerosol
(i.e. the lidar ratio and Ångström exponent). This leads to
a wrong attenuation correction and thus to wrong quasi-particle-backscatter coefficient and quasi-particle depolarization ratio values
above the cloud base. Furthermore, multiple scattering at the large
cloud hydrometeors leads to an additional underestimation of the light
attenuation (see , or
). For that reason, the current lidar stand-alone
approach is trustworthy only at cloud bases and a few tens of metres
above, depending on the cloud optical thickness. Nevertheless, in the case of
ice clouds, the classification is also performed above the cloud base
as the cloud optical thickness is usually low and thus
false classification is comparably rare, as seen in Fig. a.
However, we think the cloud classification can be
significantly improved, when the lidar-only categorization is combined with the Cloudnet one, as explained in the outlook, because
the use of cloud radar information will allow setting
different a priori information for the clouds.
This case study also shows that, under conditions of low-level clouds,
atmospheric features can be identified by MWL with the newly developed
methodology, which is not easily possible with the traditional Raman or
Klett–Fernald lidar methods.
18 April 2013
The third example day, 18 April 2013, is shown in Fig. .
Lidar particle categorization (top) and Cloudnet target categorization (bottom) for 18 April 2013.
This day is characterized by strong westerly winds with wind gusts up
to 16 ms-1 as it was found from Doppler lidar
observations. On this day, a mixture with non-spherical aerosol in the
lowermost boundary layer was observed almost continuously, except for
the period of cloud occurrence between 05:00 and
07:00 UTC. This liquid cloud is identified with MWL and
Cloudnet in good agreement. The MWL classification detects an
optically thin lofted aerosol layer between 2 and 3.5 km
height after the low cloud layer disappeared at around
07:00 UTC. Cloudnet did not detect this aerosol layer. At the
top of this layer, a cloud formed shortly past 08:00 UTC. Both
clouds are identified to be pure liquid by both algorithms. Shallow
boundary layer clouds were observed occasionally past
12:00 UTC.
Due to the strong westerly winds, we conclude that the observed
non-spherical particles in the PBL originate from the open-pit mine of
Inden (see Fig. 2 in ) west of our
measurement location. Most of these particles remained below
1 km at the lidar site (except during the growing phase of the
PBL from 10:00 to 12:00 UTC). This is an indication that the
particles were just entrained into the PBL and had not had the time
to be transported to the top of the PBL yet. Another reason could be
that the particles were of much larger size than typical aeolian dust
and thus sediment much more rapidly after their emission than other
particle types. Visual inspection of the pit mine of Inden,
1.5 km west of the LACROS site, proved strong dust emissions
as shown in Fig. .
Photograph of the easterly border of the open-pit mine of Inden on 18 April 2013. Strong dust emissions were observed. The LACROS site was located 1.5 km east (i.e. downwind) of the pit.
After 23:00 UTC, a shallow convective cloud system whose precipitation (first ice than drizzle) did not touch
the ground at the LACROS site (see Cloudnet categorization in
Fig. ) was
observed. The MWL target categorization also detects the
cloud but, as already discussed in the previous example case, does
not obviously resolve the drizzle and ice but identifies large aerosol
particles, which might again have been influenced by hygroscopic
growth due to precipitation evaporation.
HOPE
In this section, an overview of the aerosol conditions during entire
HOPE is provided. The MWL PollyIfTXT was
routinely operating at Krauthausen from 2 April 2013 to 31 May
2013. Thus, 2 full months of a spring season could be covered. An
overview of the observations of the full campaign is given in the
Appendix in Fig. (April) and
Fig. (May) in terms of the quasi-particle-backscatter coefficients at 532 and 1064 nm (extensive
properties), the quasi-particle Ångström exponent
(532–1064 nm), and the quasi-particle depolarization ratio
(intensive properties) as used for the categorization. As described
in , the weather conditions during HOPE varied from
periods with several warm and cold front passages interrupted by a few
high-pressure systems with high-level cirrus clouds at the beginning
of the campaign to more low-level convective cloud conditions later
on.
Continuous MWL observations were available during the entire period
with the exceptions of some short interruptions due to
maintenance. During days of almost only precipitation (e.g. 16 May
2013), lidar observations are only sporadically available as the
system stops measurements during precipitation events. Thus,
calibrated lidar signals and the corresponding Ångström
exponents and depolarization ratios are available for most of the time
of favourable weather conditions and allow the typing of the particles
according to the scheme described above.
The corresponding lidar target categorization for the entire HOPE campaign aiming
on aerosol discrimination is shown in Fig.
together with the respective Cloudnet classification. The lidar target categorization reveals that aerosol was usually
located from the ground up to 2 km height. Non-typed particles
and low aerosol concentration were typically detected up to higher
altitudes (4–5 km) showing that these regions are not
appropriate for the Rayleigh fit procedure as already described
above. Furthermore, it can be seen that the spring of 2013 at Krauthausen
was dominated by low-level clouds and cirrus. Only on a few days
clear sky conditions were observed. Comparing to the Cloudnet target
categorization, it is confirmed that April and May was often dominated
by deep clouds covering almost the whole troposphere. The lidar target
categorization by definition only identifies the cloud bottoms in
these cases, but this in good agreement with Cloudnet.
Lidar particle categorization and Cloudnet categorization for April (top) and May (bottom) 2013.
Interestingly, the intrusion of non-spherical particles was observed
several times in the lowest 2 km until beginning of May (see
lidar target categorization in Fig. ). We can
only speculate that this might be local dust from open-pit mining, as
intensively discussed for the 18 April case study, or pollen. After
10 May 2013, low-level clouds together with precipitation prevailed
(see also Cloudnet target categorization), and thus it is reasonable
that the local dust was too wet to be entrained into the air and/or
the pollen season was over. These observations might be an interesting
topic for future studies focusing on local aerosol emissions.
Furthermore, one sees that during HOPE the majority of the aerosol in
the PBL was classified as small aerosol, as we would expect for an
industrial and highly populated area. However, large aerosol was also
observed occasionally, but mostly at the top of the PBL, indicating the
importance of hygroscopic growth. Comparing again to Cloudnet, one
sees that drizzle is often observed with radar while the lidar still
detects aerosol. This interesting feature, discussed already for the
presented case studies, was observed frequently and demonstrates the
different sensitivity of the different instruments. Furthermore, it is
found that Cloudnet does not detect as much aerosol with low
concentrations due to the use of the ceilometer, which is not as
powerful as the MWL.
To give an overview of the aerosol and also partly the cloud
conditions during HOPE, a statistic of the classified scatterers for
the entire troposphere for HOPE is shown in
Fig. . Concerning typed aerosol (Fig. , top, left), the
majority of the particles were classified as small aerosol (two-thirds). Large, spherical particles were observed 20 % of
the time, while a mixture of non-spherical and spherical particles was
observed in 9 % and large, non-spherical particles only in
3 % of the analysed pixels. As already discussed, these particles were mostly mixed from the ground into the
atmosphere. Only on a few of the days, thin, lofted layers of Saharan dust were
observed.
Statistics on particle categorization for the entire HOPE campaign: (a) for all typed aerosol particles, (b) typed aerosol and non-typed particles, (c) cloud particles, and (d) all typed pixels.
If one also takes into account the “non-typed particles/low
concentration” class (Fig. , left, bottom) one sees
that surprisingly 42 % of the particles are non-typed or of
low concentration. But one has to take into account that this
particle class can inherit every scatterer type (i.e., all types of clouds and aerosol)
and that very low aerosol concentrations were almost always
present above the PBL. Due to the conservative approach chosen,
particles are only typed if enough information is available. Thus,
often the 1064 nm channel, which is least sensitive for
molecular scattering, detects particles while the other channels have
a too-low SNR to be used for particle typing, which leads to a large
number of pixels being classified as non-typed aerosol particles. However, the
information given by this category is still very useful, as it makes
clear that molecules are not the only ones that contribute to the light scattering,
which is important when the target classification will be used
for the determination of suitable calibration periods and regions with
negligible aerosol scattering.
For the clouds identified during HOPE, a different picture was
obtained (Fig. , top, right). Here, the “likely ice
cloud” class is the dominant type, with 46 %. Due to the
assumption made above aiming on aerosols (lidar ratio), the
quasi-particle depolarization ratio is underestimated in cirrus and thus
does not often exceed 35 %. Therefore, the clearly identified
ice clouds make only a fraction of 6 %. However, we have to
repeat that we do not aim at classifying clouds as we focus on aerosol,
and the cloud information might be used only as a hint for the type of
clouds for which further investigations are necessary. Water droplets
are typed in 21 % of all cases and likely liquid clouds only
three percent of the time. Non-typed clouds amount to
26 % of all cloud classes. We have to repeat that this cloud
statistic is biased as the lidar can penetrate liquid clouds only by
a few tens of metres. Above a detected liquid cloud, no typing is
performed. In turn, the lidar can often penetrate cirrus clouds, and
thus, in contrast to liquid clouds, ice crystals can also be detected
well above the cloud base.
Altogether during the HOPE campaign, more than 1 million pixels in
the troposphere of 30 m vertical and 5 min temporal
resolution could be analysed. From these pixels, clean
(i.e. molecular scattering dominating) atmosphere was observed in
29 %, clouds in only 7 %, aerosol in about
37 %, and “non-typed particles/low concentration”
in 27 % of the analysed and feature-classified pixels
(Fig. , bottom, right).
Conclusions
In this work, we have used absolutely calibrated lidar signals to
categorize primary aerosol but also clouds in high temporal and
spatial resolution. Two months of 24/7
observations from the multiwavelength-Raman-polarization lidar
PollyIfTXT during the HOPE campaign have been
analysed for that purpose. We have used the well-established Cloudnet
framework to develop a lidar stand-alone classification. The Cloudnet
equipment was operated continuously directly next to the lidar and
has been used for comparison.
Automatically derived particle backscatter coefficient
profiles in low temporal resolution (30 min)
have been used to calibrate the lidar signals. A daily mean lidar
calibration parameter was derived with an accuracy better than
20 %. From these calibrated lidar signals, new atmospheric
parameters in temporally high resolution (quasi-particle-backscatter
coefficient) which require a priori information
(assumptions) for attenuation correction have been developed. It was found that the newly
developed procedure works well at 532 and 1064 nm, but
deviations from the particle backscatter coefficients can be strong at
355 nm when the a priori information is not perfect. As
a consequence for the particle typing, the quasi-particle coefficients
at 532 and 1064 nm, its corresponding Ångström
exponent, and the linear depolarization ratio at 532 nm are
used for the classification.
By using thresholds obtained from multiyear, multisite EARLINET
measurements, four aerosol classes (small; large, spherical; large,
non-spherical; mixed, partly non-spherical) are defined. Thus,
particles were classified by their physical features (shape and size)
instead of by source as, for example, the well-known CALIPSO
typing does. For source definition, additional information is needed,
which has been out of the scope of this development, which has
focused on a lidar stand-alone tool.
The bases of optical thick clouds (liquid droplets) can be identified
using the Cloudnet approach applied to the MWL. Cirrus clouds/ice are identified by its
highly depolarizing features. Furthermore, regions dominated by
molecular scattering and regions of non-typed particles/low aerosol
concentration are identified with the target categorization. The
detection of molecular regions can be very useful for lidar
calibration in the atmosphere.
By discussing three 24 h case studies, it was shown that the
aerosol discrimination is very feasible and informative and gives
a good complement to the Cloudnet target categorization. By analysing
the entire HOPE campaign, almost 1 million pixel (5 min,
30 m) could be successfully classified with the newly
developed tool from the 2-month data set. We found that the
majority of the aerosol trapped in the PBL were small particles as
expected for a heavily populated and industrialized area. Large,
spherical aerosol was found mostly at the top of the PBL and close to
cloud bases, indicating the importance of hygroscopic growth of the
particles at high relative humidity. Interestingly, it was found that
on several days non-spherical particles were mixed from the ground
into the atmosphere. The origin of these particles remains unclear and
needs further research. Lofted layers of Saharan dust as it is typical for
spring in Germany were observed only sporadically and with low AOD
during the investigated time frame of the HOPE campaign in spring
2013. Non-typed aerosol with low concentrations was found often above
the PBL up to heights of about 4 km. Cloudnet was not able to
identify these optically thin particle layers due to the lower
sensitivity of the used ceilometer. The capability to detect cloud
bases was compared to the Cloudnet feature mask, and the good agreement
gives evidence that this feature could be used to apply robust cloud
screening, which is often needed for lidar data retrievals, for example, for other
automatic approaches such as the EARLINET Single Calculus
Chain . Ice crystals were also often classified
correctly but sometimes remained unclassified or were even falsely
classified as aerosol as a consequence of multiple reasons (a priori
information aiming at aerosol, low depolarizing characteristics in
certain temperature ranges, etc.). This behaviour might be overcome
when combining the lidar stand-alone target categorization with the
Cloudnet target categorization as planned in ACTRIS-2
ACTRIS
is the European Research Infrastructure for the observation of
Aerosol, Clouds, and Trace
gases: http://www.actris.eu/.
.
Then, the 10 lidar-based target types are available in addition to the
already existing Cloudnet quantities for an advanced categorization of
both aerosol and clouds. In this way, errors, i.e. misclassifications,
could be minimized in both schemes and a detailed data set could be
provided for European and other supersites hosting both Cloudnet
standard equipment and reliable, automatic, high-quality lidars based
on EARLINET standards.
However, it is important to have a lidar stand-alone tool, as at the
moment Cloudnet and automatic
continuously running MWLs are operated only at three European stations, while stand-alone lidar systems
are available at more than 25 EARLINET stations. We also consider the
presented MWL approach for the classification of aerosol types as
a prerequisite for the development of schemes for the identification
of aerosol layers. Current retrievals, such as the STRAT
algorithm , aim for providing aerosol layering
information from lidar observations at one wavelength and can thus
only identify a single layer even though it would actually consist of
several layers of different types, such as smoke or dust. With this
development, the integration of EARLINET and Cloudnet is ongoing and
offers a high potential for future synergistic profiling of aerosols,
clouds, and their interaction by combining modern state-of-the-art
atmospheric instruments.
The calibrated lidar signals and the lidar and cloudnet
target categorization are available via the SAMD data archive:
https://icdc.cen.uni-hamburg.de/index.php?id=samd (see Stamnas et al.,
2016 for more information). Lidar quicklooks can be found at polly.tropos.de.
AERONET data are available via
https://aeronet.gsfc.nasa.gov/cgi-bin/type_one_station_opera_v2_new?site=HOPE-Krauthausen&nachal=0&year=21&aero_water=0&if_day=0&year_or_month=1&level=3&place_code=4.
GDAS1 data are available via
https://www.ready.noaa.gov/gdas1.php.
Measurement overview
Overview of MWL PollyXT observations in April 2013. Top
to bottom: quasi-particle-backscatter coefficient at 532 and 1064 nm,
corresponding Ångström exponent, and quasi-particle depolarization
ratio at 532 nm.
Overview of MWL PollyXT observations in May 2013. Top to bottom: quasi-particle-backscatter coefficient at
532 and 1064 nm, corresponding Ångström exponent,
and quasi-particle depolarization ratio at 532 nm.
The authors declare that they have no conflict of
interest.
This article is part of the special issue “HD(CP)2
Observational Prototype Experiment (ACP/AMT inter-journal SI)”. It is not
associated with a conference.
Acknowledgements
The authors acknowledge support through the High-Definition Clouds and Precipitation for
advancing Climate Prediction research program (HD(CP)2; FKZ: 01LK1209C and 01LK1212C) funded by Federal Ministry of
Education and Research in Germany (BMBF), ACTRIS under grant agreement
no. 262254 and ITaRS under grant agreement no. 289923 of the European
Union Seventh Framework Programme (FP7/2007-2013), and ACTRIS-2 under
grant agreement no. 654109 from the European Union's Horizon 2020
research and innovation programme. Many improvements, both in terms
of hardware and software, were triggered by the fruitful discussions and
network activities within EARLINET. The software framework of Cloudnet
was used for this development for which the authors are grateful. We
also acknowledge the use of JOYCE data which are provided via the
HD(CP)2 data portal. Edited by: Stefan Buehler Reviewed by: two
anonymous referees
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