ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-17-6839-2017A new methodology for PBL height estimations based on lidar depolarization
measurements: analysis and comparison against MWR and WRF model-based
resultsBravo-ArandaJuan Antoniojabravo@ugr.eshttps://orcid.org/0000-0002-2236-5241de Arruda MoreiraGregoriNavas-GuzmánFranciscohttps://orcid.org/0000-0002-0905-4385Granados-MuñozMaría JoséGuerrero-RascadoJuan LuisPozo-VázquezDavidArbizu-BarrenaClaraOlmo ReyesFrancisco JoséMalletMarcAlados ArboledasLucashttps://orcid.org/0000-0003-3576-7167Andalusian Institute for Earth System Research (IISTA-CEAMA), Granada, SpainDepartment of Applied Physics, University of Granada, Granada, SpainInstitute of Energetic and Nuclear Research (IPEN), São Paulo, BrazilInstitute of Applied Physics (IAP), University of Bern, Bern, SwitzerlandDepartment of Physics, University of Jaén, Jaén, SpainCentre National de Recherches Météorologiques, UMR 3589, Météo-France/CNRS, Toulouse, Francenow at: Institut Pierre-Simon Laplace, CNRS–Ecole Polytechnique, Paris, Francecurrently at: Table Mountain Facility, NASA/Jet Propulsion Laboratory, California Institute of Technology, Wrightwood, California, USAJuan Antonio Bravo-Aranda (jabravo@ugr.es)12June20171711683968519August20162November201627March20173April2017This 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://acp.copernicus.org/articles/17/6839/2017/acp-17-6839-2017.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/17/6839/2017/acp-17-6839-2017.pdf
The automatic and non-supervised detection of the planetary boundary layer
height (zPBL) by means of lidar measurements was widely investigated
during the last several years.
Despite considerable advances, the experimental
detection still presents difficulties such as advected aerosol layers
coupled to the planetary boundary layer (PBL) which usually produces an overestimation of the zPBL.
To improve the detection of the zPBL in these complex atmospheric
situations, we present a new algorithm, called POLARIS (PBL height
estimation based on lidar depolarisation). POLARIS applies the wavelet
covariance transform (WCT) to the range-corrected signal (RCS) and to the
perpendicular-to-parallel signal ratio (δ) profiles. Different
candidates for zPBL are chosen and the selection is done based on the
WCT applied to the RCS and δ. We use two ChArMEx (Chemistry-Aerosol Mediterranean Experiment)
campaigns with
lidar and microwave radiometer (MWR) measurements, conducted in 2012 and
2013, for the POLARIS' adjustment and validation. POLARIS improves the
zPBL detection compared to previous methods based on lidar
measurements, especially when an aerosol layer is coupled to the PBL. We also
compare the zPBL provided by the Weather Research and Forecasting (WRF)
numerical weather prediction (NWP) model with respect to the zPBL determined
with POLARIS and the MWR under Saharan dust events. WRF underestimates the
zPBL during daytime but agrees with the MWR during night-time. The
zPBL provided by WRF shows a better temporal evolution compared
to
the MWR during daytime than during night-time.
Introduction
The planetary boundary layer (PBL) is the region of the troposphere directly
influenced by the processes at the Earth's surface. This region typically
responds to surface forcing mechanisms with a timescale of about 1 h or
less (Stull, 1988). The PBL height (zPBL) is a relevant
meteorological variable with a strong effect on air pollution as it defines
the atmospheric volume that can be used for pollutant dispersion. Over
time, different approaches based on the use of elastic lidar data have been
proposed for detecting the zPBL (e.g. Morille et al., 2007;
Granados-Muñoz et al., 2012; Wang et al., 2012; Pal et al.,
2010; Collaud-Coen et al., 2014; Banks et al., 2015).
Among them, some methods like the wavelet covariance transform (WCT) have
already been demonstrated to be a good tool for an automatic and unsupervised
detection of the zPBL (Morille et al., 2007; Baars et al., 2008;
Pal et al., 2010; Granados-Muñoz et al., 2012; Wang et al., 2012). This
method can be considered the combination of applying the so-called gradient
method to a range-corrected profile after smoothing by a low-pass filter
(Comerón et al., 2013). In these methods, the top of the PBL is
associated to the height where there is a sharp decrease of the range-corrected signal (RCS) and thus of the aerosol load. Lidars provide an
interesting tool for the retrieval of the PBL height, due to their vertical
and temporal resolution that allows for a continuous monitoring of the PBL. In
addition, the number of active ceilometers in Europe has considerably
increased due to the low cost and the easy maintenance, allowing us to
improve the spatial and temporal monitoring of the PBL. Both lidars and
ceilometers use aerosol as a tracer for the identification of the PBL height.
This represents a challenge due to the PBL evolution and complex internal
structure. The diurnal period is characterized by a mixing layer (statically
unstable) where turbulent mixing controls the vertical dispersion up to the
top of the convective cells (Seibert et al., 2000). The mixing boundary layer becomes a mixed layer, when the
homogenization is complete (neutral stability), something that happens when
turbulence is really vigorous and there is an intense convection. During
night-time, the stable boundary layer (also known as the nocturnal boundary
layer) is in direct contact with the surface, and the residual layer is
located above the stable layer, loaded with the aerosol that reached high
elevation in the previous day (Stull, 1988). The PBL structure is especially
complex during the sunrise and sunset when the mixing and residual layers
coexist. Furthermore, the coupling of advected aerosol layers in the free
troposphere with aerosol in the PBL or the presence of clouds leads to under-
or overestimation of the PBL height (Granados-Muñoz et al., 2012; Summa
et al., 2013).
In this work, we present a new method, called POLARIS (PBL height
estimation based on lidar depolarisation), which is an ameliorated version of the method
presented by Baars et al. (2008) and Granados-Muñoz et al. (2012).
POLARIS uses the combination of the WCT applied to the RCS and the
perpendicular-to-parallel signal ratio (δ) profiles. Using these
profiles, different candidates for the zPBL are chosen and the
optimum candidate is selected using the POLARIS algorithm. POLARIS is
particularly useful when advected aerosol layers in the free troposphere are
coupled to the PBL, because the lidar depolarization ratio profiles provide
information about the particle shape, allowing for the discrimination among
different aerosol types. Furthermore, POLARIS improves the zPBL
detection since the computation of δ (based on the ratio of two lidar
signals) partially cancels out the incomplete overlap effect, allowing for the
zPBL detection at lower heights rather than using methods based
exclusively on the RCS (affected by incomplete overlap).
Data sets of lidar and microwave radiometer measurements registered in the
ChArMEx (Chemistry-Aerosol Mediterranean Experiment;
http://charmex.lsce.ipsl.fr/) experimental campaigns during the summers
of 2012 and 2013 are used in this study for the POLARIS evaluation. ChArMEx
is an international collaborative research program to
investigate Mediterranean regional chemistry–climate interactions (Mallet et
al., 2016). One of the goals of ChArMEx is to gain a better understanding of the
atmospheric aerosol over the Mediterranean Basin (Dulac, 2014; Sicard et al., 2016; Granados-Muñoz et al.,
2016). This work contributes to the Mediterranean studies since POLARIS
improves the PBL detection under the frequent dust outbreaks affecting this
region.
Since the experimental detection of zPBL is spatially and temporally
limited due to instrumental coverage, the use of numerical weather
prediction (NWP) models for the estimation of zPBL is a feasible
alternative. In this regard, several validation studies of these model
estimations have been conducted based on lidar, surface and upper air
measurements (Dandou et al., 2009; Helmis et al., 2012), some of them in
areas close to the study region (Borge et al., 2008; Banks et al., 2015).
Results showed that NWP estimations of the zPBL (zPBLWRF)
are
feasible, but with a tendency to the underestimation of the zPBL in
most synoptic conditions. In this study, zPBLWRF is tested against
the zPBL derived from POLARIS and MWR (microwave radiometer) measurements under Saharan dust
events.
Experimental site and instrumentation
In this work we use measurements registered in the Andalusian Institute for
Earth System Research (IISTA-CEAMA). This centre is located at Granada,
in southeastern Spain (Granada; 37.16∘ N, 3.61∘ W; 680 m a.s.l.). The metropolitan Granada population is around 350 000 inhabitants:
240 000 inhabitants from the city and 110 000 inhabitants from the main
villages surround the city (http://www.ine.es). It is a non-industrialized city
surrounded by mountains (altitudes up to 3479 m a.s.l., Mulhacén peak).
Granada's meteorological conditions are characterized by a large seasonal
temperature range (cool winters and hot summers) and by a rainy period
between late autumn and early spring, with scarce rain the rest of the year.
The main local sources of aerosol particles are the road traffic, the soil
re-suspension (during the warm–dry season) and the domestic heating based on
fuel oil combustion (during winter; Titos et al., 2012). Additionally, due
to its proximity to the African continent, Granada's region is frequently
affected by outbreaks of Saharan air masses, becoming an exceptional place to
characterize Saharan dust. Additionally, Lyamani et al. (2010) and
Valenzuela et al. (2012) point to the Mediterranean Basin as an additional
source of aerosol particles in the region.
MULHACEN is a multiwavelength lidar system with a pulsed Nd:YAG laser,
frequency doubled and tripled by potassium dideuterium phosphate crystals.
MULHACEN emits at 355, 532 and 1064 nm (output energies per pulse of 60, 65
and 110 mJ, respectively) and registers elastic channels at 355, 532 and
1064 nm as well as Raman-shifted channels at 387 (from N2), 408 (from H2O) and 607 (from N2) nm. The depolarization measurements
are performed by splitting the 532 nm signal by means of a polarizing
beam-splitter cube (PBC), being the parallel signal with respect to the
polarizing plane of the outgoing laser beam measured in the reflected part of
the PBC. The depolarization calibration is performed by means of the
±45∘ calibration method (Freudenthaler, 2016). This calibration
procedure performed with MULHACEN is described in detail by Bravo-Aranda et
al. (2013), and its systematic errors are analysed
by Bravo-Aranda et al. (2016).
The optical path of the parallel and perpendicular channels at 532 nm are
designed to be identical up to the PBC where the 532 nm signal is split into
parallel and perpendicular before reaching the PMT. This setup allows us to
assume almost the same overlap for both polarizing components. Thus, the
depolarization profile is practically not influenced by the incomplete
overlap since it is cancelled out by the ratio of the perpendicular and
parallel channels. Only the thermal dilation and contraction of the lidar
optics after the PBC might independently change the overlap function of each
channel. Since MULHACEN is deployed inside an air-conditioned building, the
temperature fluctuation is small, and thus the overlap difference between the
channels might be low. Therefore, we assume significant differences only for
small values of the overlap function. Navas-Guzman et al. (2011) and Rogelj
et al. (2014) retrieve the overlap function of the total signal at 532 nm
(sum of parallel and perpendicular channels) by means of the method presented
by Wandinger and Ansmann (2002). This study shows that the full-overlap
height of MULHACEN is around 0.72 km a.g.l. Assuming that the artefacts due
to thermal fluctuations are negligible for overlap-function values above
70 %, depolarization profiles can be exploited in terms of
PBL height detection above ∼ 0.25 km a.g.l. Further details about the
technical specifications of MULHACEN are provided by Guerrero-Rascado et
al. (2008, 2009).
A ground-based passive microwave radiometer (RPG-HATPRO, Radiometer Physics
GmbH) continuously measured tropospheric temperature and humidity profiles
during the studied period. The microwave radiometer uses direct detection
receivers within two bands: 22–31 GHz (providing information about the
tropospheric water vapour profile) and 51–58 GHz (related to the
temperature profile). Temperature profiles are retrieved from surface
meteorology and the brightness temperature measured at the V-band frequencies
with a radiometric resolution between 0.3 and 0.4 K, with a root mean square error at 1 s
integration time. The frequencies 51.26, 52.28 and 53.86 GHz are used only
in zenith pointing, and the frequencies 54.94, 56.66, 57.3 and 58 GHz are
considered for all the elevation angles (Meunier et al., 2013). The inversion
algorithm is based on neural networks (Rose et al., 2005) trained using the
radiosonde database of the Murcia WMO station no. 08430 located 250 km from
Granada. The accuracy of the temperature profiles is 0.8 K within the first
2 km and 1.5 K between 2 and 4 km. The altitude grid of the inversion
increases with height: 30 m below 300 m a.g.l., 50 m between 300 and
1200 m a.g.l., 200 m between 1200 and 5000 m a.g.l., and 400 m above 5000 m a.g.l. (Navas-Guzmán,
2014). The MWR temperature profile is used to locate the zPBL(zPBLMWR) by two algorithms. Under convective
conditions, fuelled by solar irradiance absorption at the surface and the
associated heating, the parcel method is used to determine the mixing layer
height (zMLMWR; Holzworth, 1964).
Granados-Muñoz et al. (2012) already validated this methodology,
obtaining a good agreement with radiosonde measurements. Since the parcel
method is strongly sensitive to the surface temperature (Collaud-Coen et al.,
2014), surface temperature data provided by the MWR are replaced by more
accurate temperature data from a collocated meteorological station, in order
to minimize the uncertainties in zMLMWR
estimation. Conversely, under stable situations, the stable layer height
(zSLMWR) is obtained from the first point where
the gradient of potential temperature (θ) equals zero. Collaud-Coen
et al. (2014) determine the uncertainties of the PBL height for both methods
by varying the surface temperature by ±0.5∘. The uncertainties
are on the order of ±50 to ±150 m for the PBL maximum height reached
in the early afternoon, although uncertainties up to ±500 m can be found
just before sunset. Further details about both methods are given by
Collaud-Coen et al. (2014).
The POLARIS methodWavelet covariance transform
The wavelet covariance transform WF(a,b) applied to a
generic function of height, F(z), (e.g. RCS or δ) is
defined as follows:
WF(a,b)=1a∫zbztF(z)h(z-b)adz,
where z is the height, zb and zt are the integral limits, and
h((z-b)/a) is
the Haar function defined by the dilation, a, and the translation, b
(Fig. 1).
Figure 2 shows an example of the WCT applied to the RCS (WRCS). WRCS
presents a maximum in coincidence with the sharpest decrease of the RCS, and
thus the WRCS maximum is associated to a sharp decrease of the aerosol
load which could be related to the top of the PBL. In this sense, Baars et
al. (2008) proposed the use of the first maximum in the WRCS profile
from a surface larger than a threshold value to detect the zPBL.
Granados-Muñoz et al. (2012) improved this method using an iterative
procedure over the dilation parameter starting at 0.05 km and decreasing
with steps of 0.005 km. These studies show that the automatic application of
this method provides reliable results of the PBL height in most cases.
However, Granados-Muñoz et al. (2012) state that the method tends to
fail under more complex scenarios such as the aerosol stratification within the
PBL or the coupling of aerosol layers with the PBL. To improve the PBL
height retrieval for these more complex situations, we introduce the use of
the depolarization measurements by means of the POLARIS algorithm described
in the next section.
Haar's function defined by the dilation (a) and the
translation (b).
Description of POLARIS
POLARIS is based on the detection of the sharp decrease of the aerosol load
with height using the range-corrected signal and on the relative changes in
the aerosol particle shape with height using the perpendicular-to-parallel
signal ratio (δ): low δ values might be related to spherical
particle shape and vice versa (Gross et al., 2011). Since POLARIS is based
on vertical relative changes, the depolarization calibration is not required
to facilitate the procedure. POLARIS uses a 10 min averaged range-corrected
signal and perpendicular-to-parallel signal ratio (δ) and
carries out the following steps:
Example of a normalized RCS and its wavelet covariance transform.
The red cross indicates the possible location of the PBL
height.
Flux diagram of the algorithm used by POLARIS to determine the
zPBL. Cmin, Cmax and CRCS are the
candidates. The blue arrow indicates the start. Conditions are marked in
ellipses and the final attribution of the zPBL in rectangles.
The green and red arrows indicate the compliance and noncompliance of the
conditions, respectively. The rest of the symbols are explained in the text.
Examples of the cases mentioned in Fig. 3 occurred during ChArMEx
2012 and 2013. Normalized RCS (violet line) and δ (grey line) are
shown on the left, and WCT of RCS (yellow line) and δ (light blue
line) are shown on the right. Cmin (blue dot), Cmax (green dot),
CRCS (black dot) and the final attribution
zPBLPOL (red start) are shown in both axis.
The WCT is applied to the RCS and to δ (WRCS and Wδ,
respectively). Then, the WRCS (Wδ) signal is normalized to the
maximum value of RCS (δ) in the first 1 (2) kilometre(s) above
the surface.
Three zPBL candidates are determined:
CRCS: the height of the WRCS maximum closest to the surface
exceeding a certain threshold ηRCS (dimensionless). This
threshold is iteratively decreased, starting at 0.05, until
CRCS is found (Granados-Muñoz et al., 2012). A dilation
value (aRCS) of 0.03 km is used according to
Granados-Muñoz et al. (2012).
Cmin: the height of the Wδ minimum closest to the surface
exceeding the threshold ηmin (dimensionless). This
threshold is iteratively increased, starting at -0.05, until
Cmin is found. Cmin indicates the height of the strongest
increase of δ.
Cmax: the height of the Wδ maximum closest to the surface
exceeding the threshold ηmax (dimensionless). This
threshold is iteratively decreased, starting at 0.05, until
Cmin is found. Cmax indicates the height of the strongest
decrease of δ.
The zPBL attribution is performed comparing the relative location of
the candidates, since we have experimentally found that each distribution in
height of the candidates (e.g, Cmax > Cmin > CRCS; Cmin > Cmax > CRCS) can be
linked with an atmospheric situation as schematized in the flow chart (Fig. 3) and explained
below.
Only one candidate is found – the zPBL corresponds to the found
candidate.
Only two candidates are found – the zPBL corresponds to the minimum of
the found candidates (Fig. 3 case A). An example is shown in Fig. 4 case A.
The three candidates are found – in this case, the attribution of the
zPBL has two well-differentiated ways:
Two matching candidates (CRCS=Cmax or
CCRS=Cmin): it is considered that
CCRS matches Cmax or Cmin when the
distance between them is less than 150 m. In these cases, the highest (in
altitude) of the matching candidates is discarded, leaving only two
candidates. Then, we define two layers: from 120 m a.g.l. up to the lowest
candidate and the layer between the lowest and the highest candidate. Then,
we retrieve the averages (δ‾CRCS and δ‾δ in Fig. 3) and the variances of δ of both layers.
When the absolute difference between the average value of δ is lower
than a threshold δt and the variances differ less than 30 %,
the aerosol types in both layers are considered equal, indicating that mixing
processes evolve up to the highest candidate. Thus, the zPBL is
attributed to the maximum of the two candidates (Figs. 3 and 4 case B or D).
Conversely, the aerosol types in both layers are considered different,
indicating that there is not mixing between the layers, and thus the lowest
candidate is the zPBL (Figs. 3 and 4 case C or E).
No match among the candidates: this situation indicates that the sharpest
decrease of the RCS does not coincide with the sharpest
decrease or increase of δ.
Cmax > Cmin > CRCS: this situation is
experimentally linked to either an aerosol layer coupled to the PBL (both
layers are in contact) or a lofted aerosol layer (aerosol layer above the
PBL) within the free troposphere. In the case of an aerosol layer coupled to
the PBL, Cmax is the top of the coupled layer (i.e. Cmax is not
the zPBL), Cmin is the limit between the PBL and the coupled
layer and CRCS is an edge of an internal structure within the PBL. In
the case of lofted aerosol layer, Cmax and Cmin are the top and
the base of a lofted layer, respectively, whereas CRCS is the zPBL.
To differentiate the two situations, we search for a local minimum of the
WRCS around Cmin (i.e. min(WRCS(Cmin±50m)) larger than ηRCSmin, dimensionless) since the
bottom of a lofted layer would also show an increase of the RCS at the same
altitude that δ increases (Cmin). If found, it is confirmed
that Cmin is the bottom of a lofted layer, and thus the zPBL
corresponds to CRCS (Figs. 3 and 4 case F). Otherwise, Cmin detects
the zPBL (Figs. 3 and 4 case G).
Cmin > Cmax > CRCS: this situation
indicates that RCS first decreases, then δ decreases and finally
δ increases. This situation is linked to a multi-layered PBL. In
this case, the attribution of the zPBL is performed considering the
altitude at which both RCS and δ profiles have the sharpest
decrease. To this aim, Σmax and ΣRCS are defined asΣmax=WδCmax+maxWRCSCmax±50m,ΣRCS=WRCSCRCS+maxWδCRCS±50m,
where max(WRCS(Cmax±50m)) is the maximum of
WRCS in the range Cmax±50 m and max(Wδ(CRCS±50m)) is the maximum of Wδ in the
range CRCS±50 m. Physically, the parameters Σmax
and ΣRCS are the sum of the WCT where both RCS and
δ profiles have a sharp decrease. Then, if Σmax>ΣRCS, both RCS and δ present a
stronger peak
at Cmax than at CRCS, and thus the zPBL is
attributed to Cmax (Figs. 3 and 4 case J), otherwise the
zPBL is attributed to CRCS (Figs. 3 and 4 case I).
In the rest of the height distributions of Cmin, Cmax and CRCS
not considered in c.2.1 and c.2.2, the zPBL is attributed to the
minimum of the candidates (Cmin and Cmax; e.g. Figs. 3 and 4
case H).
Finally, the temporal coherence of the zPBL is checked as
proposed by Angelini et al. (2009) and Wang et al. (2012). Once
zPBL is determined for a certain period, each zPBL is
compared with its previous and subsequent values. Those
zPBLPOL values which differ more than 300 m with
respect to their previous and subsequent values are considered unrealistic
and, thus, replaced by the average value of its three or six previous and
latter values, if available. In this way we guarantee the smoothness of the
temporal series of zPBL. According to Angelini et al. (2009),
occasional aerosol stratification may occur within the mixing layer. These
types of stratification, which are usually short in time, are not really
linked with the planetary boundary development leading to false detections of
the PBL height. A
7-bin moving median filter is used to reject the possible attributions
related to this type of aerosol stratification.
To illustrate how the distribution in height of the candidates is related to
a specific atmospheric situation, we analyse a particular case at 21:30 UTC
on 16 June 2013 (Fig. 5) corresponding to an example of the c.1 scenario. As
can be seen, CRCS and Cmax are located at 4.46
and 4.41 km a.g.l., whereas Cmin is located at 0.7 km a.g.l. Since
the difference between CRCS and Cmax is lower
than 0.15 km, we assume that both candidates point to the same edge of the
layer, and thus this situation corresponds to
CRCS=Cmax>Cmin. The
mean and variance of δ in the layer below Cmin and the
layer between Cmin and Cmax are 0.65 and
7×10-4 and 0.99 and 91×10-4, respectively.
Since the δ mean difference is larger than δt and the
variances differ more than 30 %, we determine that there are two different
layers: the PBL (low δ) and the coupled layer (high δ), where
CRCS=Cmax indicates the coupled layer top
and Cmin indicates the limit between the residual and the
coupled layer, being chosen as zPBL. In this particular case, POLARIS
improves the zPBL detection from 4.46 to 0.7 km a.g.l.
Normalized RCS and δ profiles (a). WCT of the RCS,
δ and thresholds ηmin (-0.05), ηRCS
(0.05) and ηmax (0.04) (b) at 21:30 UTC 16 June
2013.
CRCS, Cmin and
Cmax candidates and zPBLPOL are
shown in both axes.
Temporal evolution of the range-corrected signal (RCS) (a)
and the perpendicular-to-parallel signal ratio (δ)(b) in
the period 09:00 UTC 16 June–20:00 UTC 17 June 2013 (colour maps). The
scatter plots represent the candidate for zPBL:
CRCS (brown dot), Cmin (pink dot) and
Cmax (ochre dot). The zPBL determined with
POLARIS (black star), using MWR measurements (violet
star) and derived from the WRF model (red star), is shown. The measure gaps are
dark-current measurements.
POLARIS adjustment
Figure 6 shows the time series of the RCS and δ at 532 nm for the
36 h lidar measurement (10:00 UTC 16 June–19:30 UTC 17 June) of ChArMEx
2013 campaign, the CRCS, Cmax and Cmin
candidates, and zPBLPOL and
zPBLMWR. This measurement is used to optimize the
algorithm, optimizing the dilation aδ and the different
thresholds (ηRCSmin and δt). Following a
similar procedure as that explained in Granados-Muñoz et al. (2012),
different combinations of dilation and threshold values are used to compute
zPBLPOL. Low dilation values (e.g. < 0.2 km)
provide a wrong PBL detection since the WCT identifies as edge changes in the
signal that are related to the noise of the δ profile, whereas large
dilation values (e.g. > 0.5 km) detect only strong edges (e.g.
the top of the dust layer). The optimal dilation (aδ) is
established at 0.45 km. This aδ value is larger than the
dilation for the RCS profile (0.3 km) determined by Granados-Muñoz et
al. (2012), which may be due to the fact that δ is noisier than RCS.
In the case of ηRCSmin, the threshold used to distinguish
decoupled layers, a value of 0.01 is chosen considering the signal-to-noise
ratio of the RCS in the first kilometre of the atmospheric column. A δt value (used in case b.1 for distinguishing two aerosol layers)
of 0.06 is determined, since lower values separate the same aerosol layer
with slight internal variations and larger values make the differentiation
between the mixing and residual layer with similar δ values more
difficult.
During this optimization process zPBLMWR is used as reference. The
goal is to minimize the differences between zPBLMWR and zPBLPOL, even though discrepancies are still expected between both
methodologies due to the use of different tracers (temperature for the MWR
and aerosol for POLARIS) and the uncertainties associated to both methods.
The zPBLPOL determined with the optimal values of aδ,
ηRCSmin and δt is shown in Fig. 6. During night-time
(from 20:30 UTC on 16 June to 04:00 UTC 17 June), we compare the residual
layer height determined by the method which uses only the RCS
(CRCS) and by POLARIS (zRLPOL) and the stable
layer height determined with the MWR (zSLMWR). The
CRCS candidates are mainly pointing to either the top
of the dust layer or the internal substructures within the dust layer (Fig. 6).
However, POLARIS distinguishes the transition between the residual aerosol
layer and the dust layer. In addition, CRCS shows no
or little temporal coherency and large discrepancies with zSLMWR as
it is evidenced by the means and standard deviations of
CRCS (2.42 ± 1.6 km a.g.l.) and of zSLMWR
(0.22 ± 0.01 km a.g.l.). On the contrary, zRLPOL (0.82 ± 0.3 km a.g.l.) is more stable with time than CRCS, with
closer values to zSLMWR, providing more reliable results. The offset
of 600 m observed between zSLMWR and zRLPOL during the night
is mostly due to the fact that zRLPOL corresponds to the residual
layer and zRLMWR marks the top of the nocturnal stable layer.
On 16 June 2013, the mean and standard deviation of zMLPOL,
zMLMWR and CRCS during daytime are 2.0 ± 0.3, 2.7 ± 0.4 and 1.5 ± 1.1 km a.g.l., respectively.
The CRCS mean is more than 1 km lower than zMLMWR
because CRCS is most frequently detecting internal
structures rather than the top of the PBL. The large standard deviation of
the CRCS (1.1 km) is caused by the detections of
either the structures within the PBL at around 1.12 km a.g.l. or the top of the
dust layer at around 3.8 km a.g.l. (Fig. 6). On the contrary, the zMLPOL
mean provides a more comparable value with a similar standard deviation. These
results evidence that the method which uses only the RCS fails when a dust
layer is overlaying the PBL. Besides, zMLPOL fits the trend
of zMLMWR better.
The main differences between zMLPOL and zMLMWR are caused by
the different basis of each methodology: zMLMWR is determined using
the temperature as the tracer whereas POLARIS uses the aerosol. For example, on
16 June 2013, zMLMWR increases from 0.8 to 2.02 km a.g.l.
between 10:15 and 11:30 UTC, whereas zMLPOL increases abruptly from
0.52 to 1.82 km a.g.l. between 11:20 and 11:30 UTC (i.e. almost 1 h
later). This is because zMLMWR grows due to the increase of the
temperature at surface level during the morning whereas zMLPOL
increases later, once the convection processes are strong enough to
dissipate the boundary between the mixing and the residual layer. Another
example of the influence of the tracer is the 1 km bias between
zMLPOL and zMLMWR between 18:00 and 21:00 UTC on 16 June
2013. During the late afternoon and early night, the temperature at surface
level quickly decreases and the atmospheric stability suddenly changes from
instable to stable. This pattern is registered by the zMLMWR
decreasing from 1.82 to 0.055 km a.g.l. between 18:00 and 18:30 UTC. The
increasing atmospheric stability during the late afternoon and early night
stops the convection processes, and then the mixing layer becomes the
residual layer. This change from mixing to residual layer is tracked by the
temporal evolution of zRLPOL decreasing from 1.92 to 0.52 km a.g.l.
between 18:00 and 24:00 UTC. Therefore, there are differences between
zPBLPOL and zPBLMWR explained in terms of the tracer used
for each method that are not related to a wrong attribution of POLARIS.
Temporal evolution of the range-corrected signal (RCS) (a)
and the perpendicular-to-parallel signal ratio (δ)(b) in
the period 12:00 UTC 9 July–06:00 UTC 12 July 2012 (colour maps). The
scatter plots represent the candidate for zPBL:
CRCS (brown dot), Cmin (pink dot) and Cmax
(ochre dot). The zPBL determined with POLARIS (black star),
using MWR measurements (violet star) and derived from
the WRF model (red star), is shown. The measure gap
is a dark-current
measurement.
Validation of POLARIS
After the optimization process, POLARIS is applied in an automatic and
unsupervised way to the 72 h lidar measurement performed during the ChArMEx
2012 campaign (between 9 and 12 July 2012). POLARIS is evaluated by comparing
zPBLPOL with zPBLMWR and
CRCS. During this campaign, a Saharan dust outbreak occurred over the
southern Iberian Peninsula. As it can be seen in Fig. 7, δ values are
lower close to the surface (mainly local anthropogenic aerosols) in
comparison with the lofted aerosol layers (dust aerosol plumes).
The detection of the zPBL by means of the method applied by
Granados-Muñoz et al. (2012) (CRCS) shows an erratic
trend during the analysed period when the dust layer is coupled to the PBL
(Fig. 7). As it can be seen, CRCS sometimes detects either
the top of the dust layer, as in the periods 19:30–22:00 UTC on 9 July and
15:40–16:10 UTC on 11 July, reaching values above 5 km a.g.l. or an
internal structure within the dust layer (e.g. between 11:50 and 12:20 UTC
on 11 July). These estimations are really far from the
zMLMWR, and thus they are not linked with the top of
the mixing layer. For example, in the period 15:40–16:10 UTC on 11 July,
the difference between CRCS and zMLMWR
is around 3 km whereas the difference between zMLPOL
and zMLMWR is around 0.5 km, and thus we can conclude
that the estimation performed using POLARIS significantly improves the
detection of the zPBL when an aerosol layer is coupled to the
PBL. POLARIS and the method applied by Granados-Muñoz et al. (2012)
(CRCS) agree with discrepancies lower than 250 m when the
dust layer is decoupled from the PBL (e.g. 00:00–08:00 UTC 10 July,
00:00–09:00 UTC 11 July and 18:00 UTC 11 July–04:45 UTC 12 July),
evidencing that the use of POLARIS is also appropriate when no coupled layers
are present.
The comparison between zMLPOL and
zMLMWR shows a good agreement when the mixing layer is
well developed (13:00–16:00 UTC on each day). However, some discrepancies
are found (e.g. 14:46 UTC 10 July 2012 and 15:51 UTC 11 July 2012). These
differences can be easily explained considering the different uncertainties
and tracers of both methods, which have different responses during the
changing conditions, e.g. those observed during sunset or sunrise. During
night-time (e.g. 20:00 UTC 9 July), the offset between the residual and
stable layer can be easily tracked with zRLPOL and
zSLMWR. POLARIS detects the residual layer instead of
the stable layer because the WCT can be applied only from aδ/2 m above the first valid value of the profile
(∼ 0.25 km a.g.l.), i.e. around ∼ 450 m, whereas the
zSLMWR is between 100 and 300 m a.g.l.
WRF validation using POLARIS and MWR
Recent studies use the zPBL determined using lidar data to validate the
zPBL obtained from the WRF (Weather Research and Forecasting) model (zPBLWRF; Xie et al., 2012;
Pichelli et al., 2014 and Banks et al., 2015). In this section, we take the
advantage of the zPBL determined by POLARIS (zPBLPOL) together
with the microwave radiometer zPBLMWR during CHArMEx 2012 and 2013
to validate the zPBLWRF.
WRF model setup
The model configuration consists of four nested domains with 27, 9, 3 and 1 km (approximately) spatial resolution domains, respectively, and 50 vertical
levels. The outputs (i.e. temperature, wind and humidity profiles)
of the 1 km domain are analysed. The initial and boundary conditions for the
WRF model runs are taken from the NCEP (National Centers for Environmental Prediction) high-resolution Global Forecast
System data set (http://www.emc.ncep.noaa.gov) every 6 h.
The choice of the model's physical parameterization is based on
the results of previous evaluation studies conducted in the study area
(Arbizu-Barrena et al., 2015). Particularly, the
Mellor–Yamada–Nakanishi–Niino level 2.5 model is selected for the PBL
parameterization (Nakanishi and Niino, 2009). The parameterizations used for
the rest of physical schemes are the Eta (Ferrier) microphysics
parameterization scheme (Rogers et al., 2005), the RRTM long-wave radiation
parameterization (Mlawer et al., 1997), the Dudhia scheme for short-wave
radiation parameterization (Dudhia, 1989), the 5-layer thermal diffusion land
surface parameterization (Dudhia, 1996) and, for coarser domains, the
Kain–Fritsch (new Eta) cumulus parameterization (Kain, 2004).
R2 among zPBLPOL,
zPBLMWR and
zPBLWRFduring ChArMEx 2012 and 2013. Points are
the number of values used to retrieve the correlation factor.
ΔPBLPOL-WRF‾,
ΔPBLMWR-WRF‾ and
ΔPBLPOL-MWR‾ are the mean differences
between the zPBLPOL,
zPBLMWR and zPBLMWR.
Daytime is considered between 06:00 and 19:00 UTC (PBL means the ML) and
night-time is the rest of the day (PBL means the RS).
Daytime RPOL-WRF2PointsΔPBLPOL-WRF‾RMWR-WRF2PointsΔPBLMWR-WRF‾RPOL-MWR2PointsΔPBLPOL-MWR‾(m)(m)(m)ChArMEx20129 July0.236128500.664124400.5981238010 July0.763266800.605264100.7182624011 July0.6612611700.441265200.361261700201316 June0.122268300.3952613300.8032657017 July0.018264200.094262800.3042640Night-time RPOL-WRF2PointsΔPBLPOL-WRF‾RMWR-WRF2PointsΔPBLMWR-WRF‾RPOL-MWR2PointsΔPBLPOL-MWR‾(m)(m)(m)20129 July0.660289400.364171900.46317115010 July0.640289300.03291800.0579113011 July0.440287700.230113800.062111130201316 June0.030283900.09994000.0289730Comparison of the PBL heights determined by WRF, POLARIS and
microwave radiometer
Figures 6 and 7 show the temporal evolution of the PBL heights determined by
means of POLARIS (zPBLPOL), the MWR (zPBLMWR) and WRF
(zPBLWRF) during the ChArMEx campaigns in 2012 (09:00 UTC 16 June–20:00 UTC 17 June) and 2013 (12:00 UTC 9 July–06:00 UTC 12 July).
During daytime on both campaigns, WRF underestimates the zPBL (lower
values) with respect to zPBLPOL and zPBLMWR in agreement
with the study presented by Banks et al. (2015) and Banks and Baldasano
(2016). For example, zPBLWRF is 1 km below zPBLPOL and
zPBLMWR on 16 June 2013 (Fig. 6) and on 9 and 10 July 2012 (Fig. 7).
Nevertheless, the zPBL time series of all methods show similar
patterns. Table 1 shows the determination coefficient R2 and the mean
of the differences (i.e. bias) among zPBLWRF, zPBLPOL and
zPBLMWR during night- and daytime.
During free-cloud daytime, the correlation between
zPBLWRF and zPBLPOL can be well
differentiated. RPOL-WRF2 is larger on 10 and 11 July 2012 than
on 9 July 2012 and 16 June 2013. According to the time series of δ
(Figs. 6 and 7), it can be seen that the coupling of the dust layer to the
PBL is stronger on 10 and 11 July 2012 than on 9 July 2012 and 16 June 2013.
Additionally, the mean of bias values between POLARIS and WRF
(ΔPBLPOL-WRF‾), larger than 800 m, points
to the aforementioned underestimation of the convective processes. In this
regard, several possibilities are feasible: (i) too stringent conditions for
the WRF parameterization, which can directly influence the results (Xie et
al., 2012; Banks et al., 2015); (ii) insufficient number of the WRF model
vertical levels within the PBL limits; (iii) the different definitions of the
PBL applied to each method; and (iv) the presence of the Saharan dust layer
(Figs. 6 and 7). Among these causes, (i) and (ii) should affect to the whole
period, not only the periods with the strongest coupling of the dust layer to
the PBL. In addition, the different definitions of PBL seem to contribute to such a large bias. In fact, POLARIS and
the parcel method use different tracers (e.g. temperature and aerosol) but
they generally show better agreement than WRF. Thus, the more plausible cause
is the inability of the used WRF PBL parameterization to account properly for
this particular kind of event.
The correlations between MWR and WRF (RMWR-WRF2) are between
0.395 and 0.664 during free-cloud daytime without a clear dependence with the
presence or the coupling of the dust layer. The lowest RMWR-WRF2
and the largest ΔPBLMWR-WRF‾ occurs on
16 June in coincidence with the lowest RPOL-WRF2. On this day,
the WRF model estimates that the convective processes start at 13:35 and end
at 16:15 UTC, whereas MWR detects convective processes between 10:30 and
18:00 UTC (i.e. 5 h difference). The good agreement between POLARIS and MWR
(RPOL-MWR2=0.803) indicates that the main cause of the
differences in the PBL height is the short duration of the convective
processes estimated by the WRF model.
During night-time, zPBLWRF and
zPBLMWR agree, with differences below 0.38 km (see
Table 1). However, a low temporal correlation is observed
(RMWR-WRF2 values between 0.032 and 0.364), showing the opposite
behaviour observed during daytime. The large
ΔPBLPOL-WRF‾ and
ΔPBLPOL-MWR‾ values evidence that POLARIS
detects the residual layer whereas MWR and WRF detect the top of the stable
layer. Despite POLARIS and WRF detecting different layers, we find a larger
correlation among them (RPOL-WRF2) than between MWR and WRF
(RMWR-WRF2).
Finally, the lowest RPOL-WRF2 coincides with the lowest
ΔPBLPOL-WRF‾ and
ΔPBLMWR-WRF‾ values on 17 June. The
presence of clouds from midday (cloud base at 9.32 km a.g.l.) until the end
of the measurements (cloud base at 1.32 km a.g.l.) may explain this
behaviour since (i) the systematic underestimation from WRF might be
compensated by the cloudy conditions inhibiting the strength of convective
processes and (ii) the track of the PBL evolution is more difficult to fit
during cloudy conditions, considering the different tracers (i.e. aerosol and
temperature).
To sum up, during night-time, zPBLWRF and zPBLMWR values
agree but more similar temporal evolution is found between WRF and POLARIS.
However, during daytime, the WRF model underestimates the zPBL. Since
POLARIS allows detecting reliable PBL heights under Saharan dust outbreaks,
it might be used for the improvement of the WRF parameterization.
Conclusion
The perpendicular-to-parallel signal ratio (i.e. the uncalibrated volume
linear depolarization ratio), together with the lidar range-corrected signal,
is used to develop a new algorithm, called POLARIS, for the detection of the
planetary boundary layer height (zPBL). The zPBL
provided by POLARIS, zPBLPOL, is optimized by
comparison with the zPBL derived from microwave radiometer
measurements (temperature profiles), zPBLMWR, using
continuous 36 h lidar and MWR measurements. zPBLPOL is
validated by comparison with the zPBLMWR, using
continuous 72 h lidar and MWR measurements. These measurements were
performed during the ChArMEx campaigns conducted in 2012 and 2013. These
continuous-term measurements are crucial for the adjustment and validation of
POLARIS since they allow the tracking of the evolution of the coupling
between advected aerosol layers and the planetary boundary layer. A better
agreement is obtained between POLARIS and the methods applied to the MWR
measurements compared with the WCT method exclusively applied to the
range-corrected signal during complex scenarios (e.g. when a Saharan dust
layer is coupled to the PBL). Despite the fact that POLARIS is validated using dust layers
coupled to the PBL, a priori, it can be used for any layer coupled to the PBL
if the aerosol particle shape is different enough to be detected by the
depolarization profile. This is a remarkable improvement compared to previous
methods based on the WCT applied to the RCS.
The zPBL is also determined by means of the WRF model, zPBLWRF,
under Saharan dust outbreaks. During daytime, zMLWRF is considerably
lower than zMLPOL and zMLMWR with larger differences under
coupling-layer situations. However, WRF and MWR provides a similar zPBL
during night-time, although zSLWRF shows a better temporal
correlation with zRLPOL than with zSLMWR. The comparison
between POLARIS and WRF evidences the model difficulties in determining the
zPBL when advected layers are coupled to the PBL. Since POLARIS allows
the detection of reliable PBL heights under Saharan dust outbreaks, it might
be used for the improvement of the WRF parameterization.
This study demonstrates that the depolarization measurement is an
interesting proxy for the PBL detection since it provides reliable PBL
heights under coupling-layer situations. Moreover, considering the next
ceilometer generations with depolarization capabilities, this study will be
useful for automatic and unsupervised PBL detection. In this regard,
further investigations will lead to a proper PBL height detection in all
atmospheric conditions.
Data used in this paper are available upon request from
corresponding author (jabravo@ugr.es).
The authors declare that they have no conflict of
interest.
Acknowledgements
This work was supported by the Andalusia Regional Government through project
P12-RNM-2409, by the Spanish Ministry of Economy and Competitiveness through
projects CGL2013-45410-R and CGL2016-81092-R, and by the European Union's
Horizon 2020 research and innovation programme through project ACTRIS-2
(grant agreement no. 654109). The authors thankfully acknowledge the FEDER
program for the instrumentation used in this work. This work was also
partially funded by the University of Granada through the contract “Plan
Propio. Programa 9. Convocatoria 2013” and by EU COST ES1303 (TOPROF). The
authors express their gratitude to the ChArMEx project of the MISTRALS
(Mediterranean Integrated Studies at Regional and Local Scales;
http://www.mistrals-home.org) multidisciplinary research
programme. Edited by: E.
Gerasopoulos Reviewed by: two anonymous referees
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