ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-17-10051-2017PathfinderTURB: an automatic boundary layer algorithm. Development,
validation and application to study the impact on in situ
measurements at the JungfraujochPolteraYannhttps://orcid.org/0000-0001-5740-8056MartucciGiovannigiovanni.martucci@meteoswiss.chCollaud CoenMartineHervoMaximehttps://orcid.org/0000-0003-3614-1297EmmeneggerLukashttps://orcid.org/0000-0002-9812-3986HenneStephanhttps://orcid.org/0000-0002-6637-4887BrunnerDominikhttps://orcid.org/0000-0002-4007-6902HaefeleAlexanderFederal Office of Meteorology and Climatology, MeteoSwiss, Payerne,
SwitzerlandSwiss Federal Laboratories for Materials Science and Technology,
Dübendorf, SwitzerlandInstitute for Atmospheric and Climate Science, ETH Zurich, Zurich,
SwitzerlandGiovanni Martucci (giovanni.martucci@meteoswiss.ch)28August20171716100511007028October201623January20177June20176July2017This 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/17/10051/2017/acp-17-10051-2017.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/17/10051/2017/acp-17-10051-2017.pdf
We present the development of the PathfinderTURB algorithm for the analysis
of ceilometer backscatter data and the real-time detection of the vertical
structure of the planetary boundary layer. Two aerosol layer heights are
retrieved by PathfinderTURB: the convective boundary layer (CBL) and the
continuous aerosol layer (CAL). PathfinderTURB combines the strengths of
gradient- and variance-based methods and addresses the layer attribution
problem by adopting a geodesic approach. The algorithm has been applied to
1 year of data measured by two ceilometers of type CHM15k, one operated at
the Aerological Observatory of Payerne (491 m a.s.l.) on the Swiss plateau
and one at the Kleine Scheidegg (2061 m a.s.l.) in the Swiss Alps. The
retrieval of the CBL has been validated at Payerne using two reference
methods: (1) manual detections of the CBL height performed by human experts
using the ceilometer backscatter data; (2) values of CBL heights calculated
using the Richardson's method from co-located radio sounding data. We found
average biases as small as 27 m (53 m) with respect to reference method 1
(method 2). Based on the excellent agreement between the two reference
methods, PathfinderTURB has been applied to the ceilometer data at the
mountainous site of the Kleine Scheidegg for the period September 2014 to
November 2015. At this site, the CHM15k is operated in a tilted configuration
at 71∘ zenith angle to probe the atmosphere next to the Sphinx
Observatory (3580 m a.s.l.) on the Jungfraujoch (JFJ). The analysis of the
retrieved layers led to the following results: the CAL reaches the JFJ
41 % of the time in summer and 21 % of the time in winter for a total
of 97 days during the two seasons. The season-averaged daily cycles show that
the CBL height reaches the JFJ only during short periods (4 % of the
time), but on 20 individual days in summer and never during winter. During
summer in particular, the CBL and the CAL modify the air sampled in situ at
JFJ, resulting in an unequivocal dependence of the measured absorption
coefficient on the height of both layers. This highlights the relevance of
retrieving the height of CAL and CBL automatically at the JFJ.
Introduction
During convective periods, particles and gases are mixed homogeneously within
the convective boundary layer (CBL). The upper limit of the CBL corresponds
to the interface between the well-mixed region and the free troposphere (FT)
above it. This interface, also called entrainment zone (EZ), is a turbulent
transition of a few tens to hundreds of metres thick, characterized by
negative buoyancy flux. The study of the EZ and the way the CBL air is mixed
through it has drawn particular attention in recent decades. There are
various methods to study the CBL and the EZ, based on profiles of
temperature, backscatter or turbulence measured either by radio sounding or
by passive and active remote sensing or calculated by numerical models.
Amongst the different observational methods, the remote sensing technique
ensures the largest amount of profile data. Active remote sensing (acoustic
or laser-based) provides the best vertical resolution, allowing the
resolution of the multiple transitions (including the EZ) between different
layers in the CBL and the FT. Probably the best-suited instrument to study
these dynamics at high temporal and vertical resolution is the ceilometer, a
low-power, compact and cost-effective version of a research lidar. A
ceilometer is a laser-based instrument normally emitting in the near-infrared
spectral band (800–1100 nm), highly sensitive to aerosols and cloud
droplets. In the early 2000s, the first manufacturers (e.g. Vaisala,
Leosphere, MPL, Jenoptik) started producing ceilometers with the capability
to store the entire backscatter profile in addition to the cloud base height.
Rapidly, ceilometers have been recognized by the meteorological services and
research centres in Europe and worldwide as an efficient and affordable way
to study the troposphere using aerosols as tracers (e.g. Münkel, 2007;
Flentje et al., 2010; Martucci et al., 2010a, b; Heese et al., 2010; Wiegner
and Geiss, 2012; Wiegner et al., 2014). Over the last decade, ceilometers
have increased significantly in number especially in Europe, the United
States and Asia, now reaching nearly 1000 units in Europe alone
(http://www.dwd.de/ceilomap). If combined in a single large network,
all ceilometers could provide helpful information on the vertical and
horizontal distribution of aerosols and on the status of the CBL over a very
large geographical domain in near-real time.
In order to automatically process a large amount of data over a large and
geographically diverse domain, we need an algorithm capable of retrieving the
vertical structure of the boundary layer (BL) during both convective and
stable conditions and over both flat and complex terrain. The conditions
inside the stable BL (SBL) are generally stratified and characterized by
strong radiative cooling, especially on clear nights. However, the CBL is
characterized by an active mixing due to the daytime cycle of thermals
updraft and downdraft. Several aerosol layers can form inside the BL (and
into the FT by advection), so the difficulty of discriminating one layer from
another is directly proportional to the number of layers. An efficient
retrieval method shall solve the attribution problem (layer
categorization), i.e. shall detect unambiguously the different aerosol layers
and the EZ. The attribution problem still remains one of the major sources of
uncertainty related to the CBL and SBL height retrieval. In order to address
the attribution problem, we have further developed the pathfinder algorithm
originally described by de Bruine et al. (2017). We then validated our own
self-developed version of the pathfinder algorithm and applied it to
real-time detections of the vertical structure of the BL above complex
terrain. This improved version of the pathfinder algorithm is called
PathfinderTURB (pathfinder algorithm based on TURBulence), to highlight the
use of aerosol distribution temporal variability (variance) to detect the BL
height. PathfinderTURB has been applied to the data of a ceilometer installed
at the Kleine Scheidegg to probe the air sampled by the in situ
instrumentation at the high Alpine station Jungfraujoch (JFJ). The JFJ is
part of numerous global observation programs like GAW (Global Atmospheric
Watch), EMEP (European Monitoring and Evaluation Programme), NDACC (Network
for the Detection of Atmospheric Composition Change) and AGAGE (Advanced
Global Atmospheric Gases Experiment). Most importantly, in the context of
this study, JFJ participates with in situ observations as a level-1 station
in the ICOS project. In contrast to other ICOS sites located over flat
terrain, it was decided to install the ceilometer at KSE to characterize the
CBL below and above the JFJ. The presence of the aerosols detected by the
ceilometer and the frequency at which these reach the JFJ are directly
compared to the optical, chemical and physical in situ measurements of
aerosols and trace gases at the JFJ. Several in situ instruments are
installed at the JFJ and operate continuously over many years to measure
aerosols, trace gases and several meteorological parameters (Bukowiecki et
al., 2016). Instruments of direct interest to our study are a condensation
particle counter (CPC; TSI Inc., Model 3772), which measures the particle
number concentration and two instruments providing aerosol absorption
coefficients; a multi-angle absorption photometer (MAAP) measuring at
637 nm; and an Aethalometer (AE-31, Magee Scientific) measuring at seven
different wavelengths. The use of a ceilometer to remotely measure the
presence of the CBL air in real time, close to the JFJ, for more than 1 year
is unprecedented. A recent study by
Zieger et al. (2012) used a scanning lidar tilted at 60∘ Zenith angle
for 9 days to probe the air close to the JFJ. Also based on the results of
the study by Zieger, we have decided to improve their instrument set-up and
to install a ceilometer probing even closer (few metres) to the JFJ and for
more than 1 year. This has allowed us to create a statistics of CBL-events
and to describe quantitatively the relation between the CBL dynamics (rising
and falling) and the aerosols optical properties at JFJ. The relevance of
such measurements also becomes clear in the framework of ICOS, where the
detection of the CBL height in the vicinity of a level-1 ICOS station is a
requirement to validate the atmospheric transport models. This is crucial
when observations of atmospheric compounds at different concentrations must
be translated into greenhouse gas fluxes between the atmosphere and the land
surface.
Overview of existing algorithms
Traditionally, the retrieval of the BL height from the backscatter profile of
a lidar can be done using two types of methods: (i) the gradient-based
algorithms that track gradients in the vertical distribution of aerosols
(gradient of the backscatter profiles) and (ii) the variance-based
algorithms that track fluctuations in the temporal distribution of aerosols
(variance of the backscatter profiles). Some algorithms combine both
techniques, which makes the BL height retrieval more robust, especially in
convective conditions when the BL dynamics change rapidly.
The gradient-based algorithms retrieve the BL height by tracking the
well-marked drop in the aerosol concentration that often occurs at the base
of (or within) the EZ in convective conditions or at the level of the
temperature inversion capping the residual layer (RL), in
neutrally stratified conditions. All vertical negative gradients found
starting from the ground are transitions between different aerosol layers and
correspond to peaks along the lidar backscatter gradient profile. All peaks
are labelled as possible candidates of the BL height (layer
attribution) at each time step. The traditional approach, using numerical
approximations of the first or second derivatives of the lidar signal (e.g.
Menut et al., 1999), has been improved by using the wavelet covariance
transform and the fact that the strong gradient occurring at the top of a
layer exists on both small and large scales, allowing the wavelet-based
methods to reduce the uncertainty when assigning the BL height (Davis et al.,
2000; Cohn and Angevine, 2000; Brooks, 2003; Baars et al., 2008; Angelini et
al., 2009; de Haij et al., 2010; Frey et al., 2010). Alternatively, the
derivative of Gaussian wavelets is used in Morille et al. (2007) or the
Daubechies wavelets in Engelbart et al. (2008). The Canny edge detection
method (Canny, 1986) also help to improve the retrieval of aerosol layers
(e.g. STRAT2D: Morille et al., 2007). It is also worth mentioning the method
proposed in Steyn et al. (1999), which consists of fitting an idealized
backscatter profile at the transition between the BL and the FT. In the more
recent literature there are examples of different methods combining the lidar
gradient-based retrievals with temporal height-tracking techniques, for
example observational (Martucci et al., 2010a, b), predictive (Tomás et
al., 2010) or model-based first guesses (Di Giuseppe et al., 2012). Pal et
al. (2013) proposed a simplified bulk model combined with surface turbulence
measurements and atmospheric variance measurements, to help select the BL
height amongst all candidates. Collaud Coen et al. (2014), used a
gradient-based temporal continuity criterion to reduce the problem's
degeneration and improve the attribution skill. In the study described by de
Bruine et al. (2017), presenting the pathfinder algorithm, the
gradient field and guiding restrictions are taken as core information to
retrieve the BL height based on the identification of the most cost-effective
path (called a geodesic) along the gradient lines in a graph.
The variance-based algorithms use the temporal fluctuations in the aerosol
backscatter as a function of the height z to retrieve the BL height. Within
the EZ, cleaner, drier free tropospheric air is entrained repeatedly and
mixed in with the rising aerosol-laden, moister air coming from the BL. A
variance-based algorithm can detect the BL height at the level where the
backscatter variability reaches a maximum at the base or within the EZ.
Variance-based algorithms calculate the temporal variance of the backscatter
profile at each range bin, usually over periods shorter than 1 h.
Similarly to the gradient-based, the variance-based algorithms use peaks in
the smoothed variance profile as candidates for the BL height (e.g. Hooper
and Eloranta, 1986; Piironen and Eloranta, 1995; Menut et al., 1999;
Martucci et al., 2007).
Because the transitions between different aerosol layers and between the BL
and the FT are characterized by both a sharp gradient in aerosol
concentration and by mixing of air through the interface, the variance- and
gradient-based algorithms normally provide similar retrievals of the BL
height. Still, the gradient-based and the variance-based algorithms have
their specific advantages and disadvantages under different atmospheric
conditions. Indeed, the depth and structure of the BL depend on non-linear
interactions at different timescales, induced by mechanical and thermodynamic
mixing. When retrieving the BL height it is then important to include in the
algorithm more than one source of information (e.g. gradient, variance, a
priori information) in order to account for the largest number of atmospheric
conditions and then to minimize the attribution uncertainty. Combining the
variance- and gradient-based methods allows us to compare the two retrievals at
each time step (Lammert and Bösenberg, 2006; Martucci et al., 2010a, b,
Haeffelin et al., 2012; Toledo et al., 2014). The retrieval method STRAT+
(Pal et al., 2013), based on STRAT2D, uses the Canny edge detection applied
to gradient profiles along with the information brought by the variance
profiles and by the radiosoundings to detect the main BL height and internal
boundaries as well as the growth rate.
Description of instruments and sites
Two ceilometers of type CHM15k-Nimbus (hereafter referred to as only CHM15k)
manufactured by Lufft have been deployed for this study at two sites in
Switzerland: the Aerological observatory of MeteoSwiss at Payerne (PAY,
491 m a.s.l., 46.799∘ N, 6.932∘ E) and the Kleine
Scheidegg (KSE, 2061 m a.s.l., 46.547∘ N, 7.985∘ E). The
CHM15k is a bi-static lidar with a Nd : YAG solid-state laser emitting
linearly polarized light at a wavelength of 1064 nm. It has a repetition
rate ranging between 5 and 7 kHz, a maximum vertical resolution of 5 m, a
maximum range of 15 km, a first overlap point at 80 m and a full overlap
reached at 800 m (specific for KSE and PAY ceilometers; Hervo et al., 2016).
The standard instrument output is the background-, range- and
overlap-corrected, normalized signal S defined at range r and time t
as follows:
Sr,t=P(r,t)-B(t)r2CCHM15k(t)OCHM15k(r),
where B is the background, CCHM15k is a normalization factor
accounting for variations in the sensitivity of the receiver and
OCHM15k is the temporally constant overlap function provided by
the manufacturer. At both sites, PAY and KSE, the overlap function
OCHM15k has been corrected for temperature variations following
Hervo et al. (2016).
Site descriptions
The PAY site is situated in the centre of the Swiss Plateau between the Jura
Mountain range (25 km to the north-west) and the Alpine foothills (20 km to
the south-east), as shown in Fig. 1a. The measurement site is characterized
by a rural environment leading to biogenic aerosols sources combined with
moderate urban emissions characterized by anthropogenic aerosol sources
especially related to car exhausts and house heating. PAY is equipped with
numerous meteorological measurements allowing the interpretation and
validation of the measurements from the CHM15k. The most relevant
measurements and instruments in the framework of the presented study are the
operational Meteolabor SRS-C34 radiosondes launched twice daily at 00:00 and
12:00 UTC (Philipona et al., 2013), and the surface sensors of temperature
and humidity. The measurements used for this study at PAY have been collected
during the period January–December 2014.
The KSE is located in the Bernese Oberland Alpine region, (Fig. 1b). KSE is on a saddle point between the mountain peak Lauberhorn
(2472 m a.s.l.) to the north-west and the Jungfraujoch (3465 m a.s.l.) to
the south-east, and it is a pass between the semi-urban areas of Wengen and
Grindelwald. This topographic configuration has a considerable influence on
the local wind circulation. Winds at the KSE are mostly blowing along the
south-west–north-east axis (Ketterer et al., 2014), whereas the prevailing wind at JFJ are
from the north-west toward the south-east. The JFJ itself is located on the ridge formed between the
Mönch and the Jungfrau mountains and is 4.5 km to the south-east and
1.5 km higher than KSE. Most of the atmospheric observations at the JFJ are
obtained at the Sphinx observatory (3580 m a.s.l.).
Topography of PAY (elevation profile along the 127.2∘
azimuth) and KSE (elevation profile along the 151.6∘ azimuth) as
provided by the federal office of topography
(http://www.geo.admin.ch/). The red stars mark the position of the
ceilometers at PAY and KSE; the black diamond marks the JFJ position.
Special instrument settings for KSE
The CHM15k ceilometer at KSE was installed in August 2014 on the roof of the
maintenance centre of the train station. From September to November 2014 and
from March to November 2015, the ceilometer was tilted at 71∘ zenith
angle with the laser beam passing close above (∼ 20 m) the JFJ. From
the beginning of November 2014 until the end of February 2015, the ceilometer
was set back to the vertical position (5∘ zenith angle) to prevent
the sun shining directly into the ceilometer's telescope.
The tilted setup of the ceilometer was chosen to observe the injections of
CBL air at the level of JFJ and to probe the same air as it is measured by
the in situ instruments at the JFJ. When measuring in slant path the maximum
vertical height, Rmax, depends on the tilt angle and on the
instrument's maximum range (15 km for the CHM15k), at 19∘ elevation
angle Rmax=2.069+15sin19∘=6.64 km a.s.l.
This value of Rmax corresponds to a level in the atmosphere where
aerosols can still be present, this fact represents a problem when the solar
background must be removed from the ceilometer signal. The normal procedure
of solar background removal consists of subtracting from the ceilometer
signal the median value of the signal itself over the last range bins
(far range). This is only possible when the far range is
not contaminated by aerosols or clouds. In order to overcome this problem a
new technique of background removal depending on VAR(S) has been developed
and applied to each profile. VAR(S) is calculated within spatial windows of
120 to 1600 m width (in steps of 120 m) and computed for all range bins
between 390 and 14 970 m. The background corresponds to the median value of
S over an optimal window. The optimal window's position is the one
minimizing the average of its VAR(S) values. The optimal window's width is
the one corresponding to the 75th percentile of the VAR(S) values at the
optimal window's position. If it is true that the background correction needs
more attention when measuring at a tilted angle, a clear advantage related to
in the slant path is that once the ceilometer's beam reaches the JFJ at
4.8 km, the received signal is already in the full overlap region.
PathfinderTURB
PathfinderTURB adds a variance criterion to the original pathfinder scheme
to retrieve the continuous aerosol layer (CAL) and the CBL. The uncertainty
related to the retrieval of the CBL and CAL is minimized by using the
geodesic approach, which also allows a better adaptability of the algorithm
to complex topography normally characterized by multiple aerosol layers. In
the framework of de Bruine's work, the pathfinder technique was applied to
the measurements of the tall-tower at the Cabauw site in the Netherlands and
successfully validated by radiosonde (RS) data. Compared to other
algorithms, pathfinder (and PathfinderTURB) can solve directly the
attribution problem by building a time series of CBL (and CAL) heights using
the geodesic approach between adjacent points (minimization of the cost
function). PathfinderTURB has been applied to the ceilometer data at PAY and
KSE.
Calculation of the top of continuous aerosol layer
The CAL is defined as the uninterrupted aerosol region along the backscatter
profile starting from the ground and reaching the first discontinuity in the
aerosol distribution. The top of the CAL (TCAL) is defined as the height of
the retrieved discontinuity. The criteria to define the CAL are the following
(see also Supplement S4):
Signal condition: the total (aerosol plus molecular) attenuated backscatter is larger than a
threshold Th that depends on the purely molecular backscatter profile at the
ceilometer's wavelength.
SNR condition: the signal-to-noise ratio (SNR) is larger than 0.6745.
Over flat homogeneous terrain, the TCAL usually corresponds to the top of the
RL during the night and to the height of the CBL during the day. In complex
and mountainous terrain, during daytime, the TCAL corresponds rather to the
top of the so-called injection layer. The injection layer has been
defined by Henne et al. (2004) as the layer formed by injections of CBL air
at higher levels. The injections are engendered by thermally driven
converging slope winds along the topography reaching higher than the average
in-valley CBL top. In contrast to the CBL, the injection layer is only
sporadically mixed and indirectly connected to the surface. The SNR
condition, imposes that the SNR is larger than the 1σ value of the
signal noise. In other words, because the background signal (dark current
plus stray light) is range-independent and is considered to be
Gaussian-distributed, the backscatter signal is considered noisy when it lies
within the 50 % confidence interval of the background signal. The noise
is calculated in the far range of the total signal. If the SNR condition is
included, the retrieved TCAL can be shallower compared to when only the
signal condition is taken into account. That happens especially during
daytime when the SNR drops below the value 0.6745 already at low altitudes due to the
enhanced solar background. In cases like this we
cannot speak anymore of TCAL, but rather of maximum detected range. When
clouds are present, the height of the first cloud layer detected by the
ceilometer combined with the heights obtained by the SNR condition and
signal condition also determine the TCAL.
Calculation of the convective boundary layer height
For a given day, the temporal evolution of the ceilometer signal is a matrix
in time and space. Each column of the matrix represents a profile at time t
and constant range resolution. The noise level is calculated from the photon-counting signal using the method described by Morille et al. (2007). The
signal is smoothed in space and time at resolutions of 30 m and 1 min at
PAY and, to compensate the reduced range due to the slant path, of 45 m and
2 min at KSE, respectively. We provide here a description of the main
selection criteria and the main assumptions on which the CBL retrieval by
PathfinderTURB is based. Further details about the algorithm, including the
calculation of the atmospheric variability (signal variance) and of the
turbulence-enhanced zones, and the mathematical steps leading to the
expressions of the measured variables are given in the Supplement (S1, S2).
Lower altitude limit
Close to the ground, for most of the industrial bi-static ceilometers, the
overlap between the transmitter and receiver is close to zero. In this
region, called blind region, the returned signal is extremely weak, dominated
by the noise, and it oscillates around zero. It is thus not possible to
retrieve the CBL height (CBLH) in this region (low clouds or fog detections
are possible, however). Above this region, the overlap increases until
it becomes complete and the noise component becomes negligible compared to the
signal, at least within aerosol layers. A positive gradient is then expected
at the transition between the blind region and the region above. We thus
define the lower altitude limit, minH, as the first range where the
transition from a zero to a positive gradient occurs and we impose
minH not to be higher than 350 m (where the overlap of the
ceilometer is normally sufficiently large to allow physical measurements).
During the morning and until the end of the afternoon, the CBLH exceeds the
height minH due to its convective growth. An additional lower limit for the
altitude is minHTURB, which marks the onset of turbulence starting
from the ground. Turbulence is calculated based on the temporal variation of
the lidar signal for each z-level due to the atmospheric variability. The
lower altitude limit minH is replaced by minHTURB whenever the latter is higher
than the former. The selected minimum limit is called liminf in Fig. 1.
Upper altitude limit
Different criteria are defined to calculate the upper altitude limit,
maxH. These criteria are based on the a priori knowledge of the
climatological CBLH value at a specific site (climatological limit)
and on the retrieval of other aerosol and cloud layers that contribute to
determining the actual CBLH. These layers are the TCAL, the cloud base height
(cloud limit) and the mixing discontinuities (strong negative and
positive gradients). The minimum altitude amongst the three limits determines
the upper altitude limit, limphys, shown in Fig. 1.
Climatological limit
A climatological limit can be set based on visually inspected ceilometer data
from previous years and on model-simulated CBLH. The climatological limit
depends on the site, and consists of a maximal CBLH value kept constant
during the early morning, a maximal mean growth rate until the onset of the
afternoon decay and a maximal CBLH value kept constant after the convective
growth. For the PAY site, the period called “early morning” starts at
sunrise and ends 2.5 h (3 h at KSE) after sunrise. This period accounts for the delay in the onset of the convective plume and is assumed constant through the year. The afternoon period is
considered to end at sunset. For our study we used the limits 1500, 3000 m
a.s.l and 1 km h-1 for PAY and 3069, 4069 and 1 km h-1 for KSE
for morning maximum CBLH, afternoon maximum CBLH and maximum mean growth
rate, respectively.
Cloud limit
Two types of clouds are considered: CBL clouds and non-CBL clouds. All cloud
information (number of cloud layers, cloud base, cloud depth) are provided by
the ceilometer's standard outputs. A CBL cloud is defined as a cloud detected
by the ceilometer in the first (lower) layer, whose vertical depth is less
than 500 m and whose top (cloud base + depth) is lower than the
site-specific climatological CBLH limit set beforehand. This criterion is
purely mathematical, as the cloud depth provided by the ceilometer just gives
the depth of the not-totally-attenuated part of the signal and not the real
depth.
Strong negative and positive gradients
Strong positive or negative gradients indicate discontinuities in the
vertical aerosol distribution and can then correspond to the CBLH. Strong
positive gradients normally indicate a change from an FT region to an aerosol
layer or a cloud base or from a CBL region to a cloud base. Strong negative
gradients correspond to a signal drop between two adjacent gradient points of
25 % (only 15 % during the early-morning period due to the still-present RL above the forming CBL), whereas strong positive gradients
correspond to a signal gain between two adjacent gradient points of 15 %
(only 5 % during the early-morning period due to the optically thin fog
layer often lifted above the forming CBL).
Growth rate
Once the validity of the limits is accepted (e.g. the lower limit not
exceeding the upper limit), the limits are recalculated back in time from
23:59 to 00:00, imposing a growth rate of ±0.625 m s-1 between two
time steps (i.e. Δz < 37.5 m at PAY and < 75 m
at KSE). This growth rate is larger than the climatological growth rate of
1 km h-1, because it allows larger jumps over shorter time steps in
order to account for the convective dynamics, e.g. the updraft and downdraft
cycle.
Weights
At each time step t, a weight function Ω defines the “cost” of
attributing the CBLH at the altitude z. The weights are calculated by
PathfinderTURB as the product of the gradient weights and the variance
weights. An offset is added to make the weights positive:
Ωt,z=log10ΩGradt,z+ΩVart,z+min…+ΩVar(tall,zall).
The offset is calculated taking the absolute minimum of Ω over the
whole day and at all altitudes. The value of ΩGrad is given
by the inverse negative of the signal gradient, ∇S. The weights
corresponding to positive or zero values of ∇S are set to 1000 times
the largest weights of the inverse negative gradient values so that the cost
of choosing a positive gradient is extremely high. The value of ΩVar is given by the inverse of the signal variance, VAR(S).
For the KSE site the weights are calculated without the contribution of
VAR(S). In fact VAR(S) becomes large when the noise contribution to S
is significant (low SNR); this implies that VAR(S) will show a maximum at
the ranges where the noise is large rather than at the range where the actual
CBLH is. Due to the slant path configuration at KSE, the noise gets larger at
lower altitudes compared to a vertical measurement (the SNR, over the entire
dataset, is on average already < 3 at 850 m a.g.l.). As a
consequence, the value of ΩVar in Eq. (2) could lead to an
incorrect retrieval of the CBLH, placing the CBLH at altitudes lower than
where it should be. For this reason Eq. (3) is used instead.
Ωt,z=log10ΩGradt,z+minlog10ΩGradtall,zall
For the same reason, at KSE the SNR condition for calculating the TCAL is not used, since
the TCAL could be biased towards the maximum detected range.
Shortest path
The shortest path in a graph (the geodesic in the metric space defined by the
weights) is calculated using the Dijkstra's algorithm (Dijkstra, 1959) and is
based on the original method described by de Bruine et al. (2017). The
Ω-weighted graph is constructed using the signal profiles starting
from sunrise (midnight at KSE) over consecutive intervals of 30 min
(overlapping at the first and last time steps) until sunset (23:59:59 UTC at
KSE). Within the lower and upper altitude limits, the graph only allows
connections of one time step in the positive time direction and of maximum
37.5 m (75 m KSE) in the altitude direction. Every shortest path starts at
time ti when the previous shortest path has ended. In case of failure of
shortest path calculation, the corresponding time window is skipped, and the
next shortest path starts at (ti, z) corresponding to the first local
minimum weight. At the first time step (sunrise for PAY, midnight for KSE),
the CBLH is set at the first local minimum weight and constraint by the lower
graph limit. The CBLH time series calculated after sunset at PAY is
discarded.
Ratio quality check
The retrieved CBLH is checked for quality at each time step by a binary
quality index (0/1), where 0 corresponds to no CBLH detection. In case of
rain or fog, the quality index is set to 0. For all the other sky conditions,
in order to perform a quality check we calculate the ratio of the mean
ceilometer signal over 150 m above the CBLH to the mean signal over the
150 m below the CBLH. When the ratio is larger than 0.85 (i.e. the signal
drop is less than 15 %), the quality is set to 0; otherwise it is set to 1.
Example of PathfinderTURB's TCAL and CBLH calculation
The retrieval's procedure of TCAL and CBLH can be summarized in three phases:
pre-processing of S, CBLH and TCAL retrieval, and quality-check. In the
pre-processing phase, ∇log10(S), liminf and
limphys are calculated. In phase two, the time series of the
range-restricted ∇log10(S) is transformed into a weighted graph
and the CBLH is determined as the geodesic calculated using the Dijkstra's
algorithm within predefined successive subintervals during the temporal
interval between sunrise and sunset. Finally, in phase three, the quality of
the CBLH retrieval is assessed using the ratio quality check.
Based on Eqs. (2)–(3), the geodesic can be calculated in the metric space
defined by the weights. PathfinderTURB calculates a line connecting the
(ti, z) pairs that minimize the cost function defined by the weights.
The connecting line is the geodesic and has the property to strongly reduce
the occurrence of unphysical jumps between different layers when boundaries
disappear or reappear due to real atmospheric dynamics. PathfinderTURB uses
VAR(S) in addition to ∇S in order to solve the attribution problem
in a more physical way, identifying regions characterized by large values of
VAR(S) and using it to retrieve the CBLH. The CBLH is attributed to a
layer's boundary in (ti, z) when this point minimizes the cost
function, i.e. minimizes the term COST=ΩGrad×VAR(S)-1. In this way, the influence of artificial and static
aerosol gradients, present in some models of ceilometers and due to an
incorrect overlap correction, is largely reduced. The different steps of the
PathfinderTURB algorithm are illustrated in Fig. 2, in four time series
(panels a, b, c and d) for the case of 15 July 2014 in PAY.
In Fig. 2a, the logarithm of the range-corrected signal is displayed. The
cloud base height (CBH) is directly provided by the ceilometer manufacturer's
software and displayed in grey throughout all panels. The TCAL (shown in
green) is the combination of the altitudes determined by applying the signal
condition and the SNR condition (Sect. 4.1) plus the height of the first
cloud layer. The signal condition and the CBH play a critical role in
this example. The development of the CBL is limited in altitude by the TCAL,
but it can also be limited below the TCAL by the other limits contributing to
limphys (Sect. 4.2.2). The limphys is the minimum height
amongst the climatological limit, the TCAL, the CBH and that of strong
negative and positive gradients (indicating mixing discontinuities). During
the period 02:00–03:30 UTC, limphys (magenta) was determined by
strong positive gradients at about 1500 m a.s.l.; during 20:00–24:00 UTC
and at about 1750 m a.s.l. by strong negative gradients.
In Fig. 2b, the VAR(S)is displayed. VAR(S)is calculated using spectral
analysis; more precisely it is the result of integrating the spectrum of
band-pass-filtered, 1 h long S time series at each altitude (as in Pal et
al., 2013). The band-pass filter aims to remove mesoscale and noisy
fluctuations so that only fluctuations due to short-lived aerosol load
variability are taken into account (Supplement S2). The lower altitude limit
(liminf) is calculated based on the VAR(S) value, and displayed in
magenta. PathfinderTURB does not search for a CBLH value within the
[0- liminf] region; that allows the correct retrieval of the
CBLH at the level of enhanced VAR(S) corresponding to the EZ at the top of
the CBL.
In Fig. 2c, the weights Ωt,z are displayed. Based on
Eqs. (2) and (3) the CBLH-path follows the deep-blue regions corresponding to a
minimum in Ωt,z. The path can only follow the
positive time direction, and altitude changes are limited to
0.625 m s-1. The CBLH is characterized by a drop in the aerosol
concentration (large negative ∇S) and high entrainment activity
(large VAR(S)), which corresponds to minimum Ωt,z.
Time series of the different processing steps of the PathfinderTURB
for 15 July 2014 at PAY. (a) Time series of log10(S) with
superimposed TCAL, CBH and limphys for altitude. (b) Time
series of VAR(S) with superimposed CBH, limphys and
liminf for altitude. (c) Time series of Ωt,z with superimposed TCAL, CBH and retrieved CBLH (geodesic from
sunrise to sunset). (d) Time series of log 10(S) with superimposed
TCAL, the CBH and the retrieved CBLH.
In Fig. 2d, a final overview is given, with the retrieved CBLH (black line)
displayed on top of the log 10(S) time series, together with the TCAL and
the CBH.
PathfinderTURB validation at Payerne
Although gradient-based algorithms are easy to implement for automatic
operations, the layer attribution remains the main source of uncertainty in
the retrievals. For methods based on aerosol gradients the visual
identification of the correct gradient by human experts still solves the
attribution problem with the least uncertainty. Therefore, PathfinderTURB is
validated here against independent detections by human experts as well as
against the bulk Richardson method applied to co-located radiosonde
profiles. The aim of the validation is to create an as-accurate-as-possible
reference without selecting only golden cases, but filtering out those cases
when fog and precipitation prevent the definition of the CBL.
Comparison with human-expert CBLH retrieval
A graphical user interface has been developed for the human experts to detect
the CBLH manually by clicking on the time–height cross section of S.
Auxiliary information is available from the interface about the following:
∇S; VAR(S) (over 10 min); sunshine duration and vertical heat flux
at the surface; trends of hourly-averaged surface temperatures
ΔT; hourly stability index (as defined in Pal et al., 2013);
sunset and sunrise time;
estimations of the CBLH based on the Parcel method (PM, Holzworth, 1964) and
the bulk Richardson method (bR, Richardson, 1920) from continuous remote
sensing instrumental data (microwave radiometer (MWR), wind profiler, Raman lidar) and twice-daily
radiosounding data (at noon and at midnight). The experts perform a manual detection of the entire
daily cycle with the support of all the ancillary data and information.
Four experts from the remote-sensing division of MeteoSwiss have processed
1 year of data (2014) of the PAY CHM15k. The guidelines and the criteria of
the manual CBLH detection are provided in the Supplement S5.
Analysed dataset
We compared the detections by three experts (test group) against one
expert that acted as reference. For the year 2014, the analysed days
were the 5th, 10th, 15th, 20th, 25th and 30th of each month and the whole
months of January, March, July and October. The test group analysed
the 5th, 10th, 15th, 20th, 25th and 30th of each month. Once the missing data
(due to instrument disruptions) and fog or precipitation days had been
removed from the dataset, the total number of days analysed was 174. Covering
an entire year, the database inspected by the test group and the
reference is comprehensive in terms of diverse synoptic conditions,
sunshine duration, cloudiness and season. The S profiles were analysed by
the test group separately and with no possibility to influence each
other's choice. The detections made by the reference and those made
by the test group were compared at each time step so that a matching
procedure was established between a CBLH point in the reference and
the test group's detections for the same time step. Only the CBLH
points that matched in time were retained for the comparison. If needed, the
test group detections were linearly interpolated in order to match
exactly the time vector of the reference. When comparing the
reference with all the test group detections the two
datasets showed an excellent agreement, with a coefficient of determination
of 0.96 (total of 5097 points over 140 days) and a root mean square error
(RMSE) of 92 m. Nevertheless, some large differences (> 500 m)
in the CBLH detections occurred in less than 3 % of all cases. In
general, discrepancies occurred when there was more than one layer that could
be reasonably followed as CBLH, for example when an advected aerosol layer
entered the profile and got mixed inside the CBL or during the often-ambiguous separation between the RL and the decaying CBL in the afternoon
after the convective peak.
PathfinderTURB validation against the expert consensus
After applying the ratio quality check (Sect. 4.2.6) to the PathfinderTURB
retrievals, the total number of the accepted retrievals covers 34 720 min
of the 43 914 min obtained by the manual detections, i.e. 79 % of the
human expert consensus. The ratio quality check of PathfinderTURB removes
about 20 % of the retrievals because of weak gradients at the level
of the retrieved CBLH. The validated PathfinderTURB retrievals are
distributed over the same number of days (i.e. 135) during the year 2014;
Fig. 3a shows the density scatter plot of the CBLH values
obtained at PAY by the (consensus) manual detections versus PathfinderTURB.
The box plots, along with the histogram shown in Fig. 3b, display the
differences between the two datasets. A coefficient of determination of
0.96, an RMSE of 76 m and an interquartile range of the differences of 96 m
are obtained. The median and mean differences are 27 and 41 m, respectively.
The overestimation is largest during the second half of the afternoon (not
explicitly shown here), when PathfinderTURB tends to follow the top of the
residual layer instead of the decaying CBL. Furthermore, the error is smaller
than 500 m for 98.6 % of the PathfinderTURB retrievals, and 92 % of
the retrievals have a relative error (with respect to the manual CBLH)
smaller than 10 %.
Density scatter plot of CBLHPathfinderTURB versus
CBLHmanual (a). Box plot and histogram of the difference
between the PathfinderTURB and manual datasets (b).
The comparison shows that PathfinderTURB is robust and can address the
attribution problem adequately. Although PathfinderTURB combines both
gradient and variance methods to improve the correctness of the retrieval in
different atmospheric conditions, the retrieval's uncertainty grows larger
during the afternoon due to the decay of convection before sunset, the weak
turbulence and the lack of well-marked aerosol gradients. During this
period, temperature or vertical wind variability profiles may provide more
valuable information than ceilometer profiles.
Comparison with radiosonde-estimated CBLH
The PathfinderTURB retrievals of the CBLH were compared to the retrievals
from two methods based on the thermal structure of the atmosphere: the PM and
the bR. The PM defines the CBLH as the height to which an air parcel with
ambient surface temperature can rise adiabatically from the ground,
neglecting other factors (entrainment or detrainment, advection, subsidence, air
humidity). It relies on profiles of potential temperature (Θ) and
therefore requires vertical profiles and surface values of temperature (T)
and pressure (p). In Payerne, Θ profiles are generated every
10 min by a microwave radiometer, and at noon and midnight also by RS. The
bulk Richardson number (Rib) is a dimensionless
parameter that can be seen as the ratio between the buoyancy and the
wind-shear-generated turbulence. The CBLH is determined as the first height
where Rib exceeds the critical threshold of 0.33
(unstable conditions) or of 0.22 (stable conditions). The required input
values are the profiles of Θ and the wind. The stability conditions,
essential for choosing the correct threshold value, are derived from the sign
of the slope of the linear fit of Θ in the first 30 m. At Payerne,
wind profiles are provided every 30 min by the wind profiler, and at noon
and midnight also by RS. We refer to Collaud Coen et al. (2014) for a more
detailed description of the operational CBLH retrievals at Payerne using the
bR method.
PathfinderTURB is compared to the RS-based bR retrievals of the noon CBLH
during the year 2014. In order to increase the robustness of the bR
retrievals, the comparison is performed only when both bR and PM retrievals
are available. Based on the calculations of Collaud Coen et al. (2014) the
uncertainty of the retrieved CBLH using both methods is of the order of
±50 to ±250 m for the midday peak of the CBLH. Within their
uncertainty intervals, the two methods can then be considered, providing the
same retrievals when the difference between them is equal to or less than
250 m. For this reason, only the retrievals matching closer than 250 m and
with an uncertainty of less than 250 m have been retained for the
comparison. That has resulted in a total of 175 days being considered. Of
these 175 days, PathfinderTURB could retrieve a valid CBLH on only 115 days.
Thereafter and for simplicity, only the bR retrievals will be used in the
comparison with PathfinderTURB (bR and PM pairs were always available for the
considered 115 days).
The median and mean difference between RS and PathfinderTURB CBLH values were
53 and 41 m, respectively, indicating a slight overestimation of the bR
method with respect to PathfinderTURB. From the comparison we obtain a
coefficient of determination of 0.85, a regression slope of 1.02 (Fig. 4a), an RMSE of 162 m and an interquartile range of the difference of
174 m, (larger than the spread observed in Fig. 3). The distribution of the
differences in Fig. 4b has a Gaussian shape with slight
positive offset values. About 98 % of the data have an error smaller
than 500 m, and 82 % have an error smaller than 10 % (plus
100 m) of the CBLH retrieved by bR. In general, the correlation between
PathfinderTURB (ceilometer-based) and the bR retrievals (RS-based) is not as
good as the one between PathfinderTURB and the manual retrievals (both
ceilometer-based). For the comparison shown in Fig. 4, it should be
remembered that the two methods rely on different physical processes, i.e.
thermal structure of the atmosphere (RS) versus the actual state of mixing of the
aerosols (ceilometer). A consequence of the different physical processes is
the slight overestimation of the bR method during the period from the end of
morning to the beginning of afternoon, i.e. when buoyancy-produced
turbulence reaches a maximum. This is because the bR indicates the depth of
the layer where conditions are favourable for vertical mixing, whereas the
aerosol gradient depicts the actual state of mixing. By using the MWR data to
evaluate the entire daily cycle (not only 12:00 UTC by RS), the comparison
between PathfinderTURB and bR shows that the bR-based CBLH generally rises faster
than the aerosol gradient in the morning.
All data shown refer to 12:00 UTC. (a) Scatter plot of
CBLHbR versus CBLHPathfinderTURB. (b) Box plot
and histogram of the difference between the bR and PathfinderTURB datasets.
The decay of the bR-based CBLH occurs also generally faster than that of the
aerosol gradient in the late afternoon, resulting in bR retrievals lower than
the PathfinderTURB CBLH retrievals. This is explained by the fact that the
aerosols remain suspended in the near-neutrally stratified air (transition
from CBL to RL) and that no detectable aerosol gradient forms at the top of
the decaying CBL. The gradient remains thus at about the same altitude as its
midday maximum, leading to a significant overestimation by PathfinderTURB. For
this reason, lidar and ceilometers using aerosols as tracers are not best
suited to detect the CBL decay, but rather the RL. Nevertheless, although at
12:00 UTC the bR still provides a slightly higher CBLH, the comparison shown
in Fig. 4 proves a good agreement between bR and PathfinderTURB.
Measurements of CBL, CAL and aerosol properties at JFJ
Updrafts and downdrafts (initiated and sustained by solar radiation received
at the surface) are the main vertical transport mechanism of the CBL air
above the Swiss Plateau (Collaud Coen et al., 2011). Air lifted from a sunlit
mountain slope is often warmer than the air at the same height over an
adjacent valley, even if the latter was lifted from the valley floor. Hence,
next to the development of up-slope (anabatic) winds, thermals generated at a
mountain slope may rise higher than those generated at the valley floor. When
both the topography and the meteorological conditions are favourable,
up-slope winds can develop and become strong enough to break through the
CBL's capping inversion and inject CBL air into the FT immediately above the
local CBL (LCBL) resulting in the formation of an aerosol layer above
the CBL (Henne at al., 2004). This complex mountain circulation is
characterized by dynamics occurring on different spatial scales (Fig. 5). The
aerosol layer or injection layer is a near-neutral, partly mixed layer that is
more diluted than the LCBL, being the result of LCBL air mixed with FT air.
The LCBL normally follows the topography (scale of a few kilometres),
especially in the morning, and is often topped by a temperature inversion
that marks the transition with the above the aerosol layer. At its upper boundary, the aerosol layer
does not follow individual valleys or ridges, but follows the large-scale
topography (a few tens of kilometres) and can also be overlaid by a temperature
inversion marking the transition with the FT (Henne at al., 2004; de Wekker,
2002). In his work, de Wekker (2002) concludes that in mountainous regions,
the mixing layer height corresponds to the top of the aerosol layer rather than the top
of the LCBL and he renames it “mountain mixing layer”, because the aerosol layer
depicts the height up to which particles can be transported by the various
venting processes. The combination of the LCBL and the aerosol layer forms the CAL
(Fig. 5).
Schematic view of the daytime atmospheric structure and vertical
pollution transport in and above the KSE site. The red line shows the CHM15k
line of sight towards the Sphinx. The annotations denote the different
thermal transport and mixing mechanisms of boundary layer air.
At the JFJ, aerosols and gases have been measured continuously for many
years. Different sources and transport regimes towards the JFJ have been
studied by many authors (e.g. Lugauer et al., 1998; Zellweger et al., 2003;
Balzani Lööv et al., 2008; Henne et al., 2010; Collaud Coen et al.,
2011, 2014; Herrmann et al., 2015), showing that the JFJ resides most of the
time in the undisturbed (“clean”) lower FT. Nevertheless and especially in
summer, the JFJ is influenced by thermally induced uplifted CBL air, and it
is also influenced by additional lifting processes such as frontal passages
and Föhn flows (Zellweger et al., 2003; Ketterer et al., 2014). As
observed by Zellweger et al. (2003) the thermally induced transport of CBL
air towards the JFJ occurs frequently during summer (∼ 35 % of the
time). The previous studies suggest that the direct contact of undiluted LCBL
air with the in situ instruments at the JFJ occurs only rarely and is limited
to summer periods (e.g. Ketterer et al., 2014). Lugauer et al. (1998) provide
a 9-year climatological analysis of the vertical transport of aerosols to the
JFJ and the corresponding synoptic conditions. The thermally induced
transport is nearly absent in winter or under cyclonic conditions and it is
strongest in summer under anticyclonic periods. During favourable conditions,
the aerosol concentration increases at the JFJ during the afternoon with a
peak at around 18:00 UTC, and the peak is stronger in
northern synoptic wind than in southern because of the difference in upwind
topography. Collaud Coen et al. (2011) found as well that the JFJ is mainly
influenced by free tropospheric air masses in winter and largely influenced
by the LCBL (also during the night) in summer during subsidence periods.
In order to understand the impact of the thermally driven dynamics on the
in situ measurements at the JFJ and to quantify, by direct observations, the
number of times that the LCBL and the CAL reach the JFJ throughout the year,
the data from the CHM15k have been analysed using PathfinderTURB during the
period August 2014 to November 2015. PathfinderTURB has been adapted to use
the CHM15k data along the slant-probing direction connecting KSE with JFJ.
The adapted PathfinderTURB version does not use the VAR(S) profiles to
calculate the weights (Eq. 3), but solely to retrieve the first transition to
the enhanced turbulence zone (see Supplement S2). In fact, at close ranges,
where the first transition to the turbulent region is usually found, the S
profile has a much higher SNR and VAR(S) can be measured reliably. At KSE,
the LCBL height (LCBLH), retrieved by PathfinderTURB, corresponds to the first
discontinuity in the vertical mixing of aerosols and can be also estimated
at night-time.
Retrieval of aerosol layers at KSE and JFJ
The CHM15k detects the aerosols that form in the surrounding lower altitude
valleys (e.g. 1034 m a.s.l. at Grindelwald, 566 m a.s.l. at Interlaken) and
that are transported above the KSE. Local generation of aerosols occurs only
sparingly due to the reduced vegetation and the long periods of snow and ice
cover. Nevertheless, when local aerosol production occurs, these can be
transported through the ceilometer's field of view and eventually be
transported up to the JFJ. The local aerosol generation and the advection
from the surrounding valleys lead to different scenarios. During daytime,
both TCAL and LCBLH can be detected, the LCBLH only during periods when the
LCBL air is lifted into the ceilometer's field of view by convection. During
night-time, when there is no convection, only the TCAL can be detected (if it
is present). The nocturnal TCAL can stem from the residual layer formed above
the surrounding valleys. PathfinderTURB is based on the same retrieval
principle during daytime and night-time, and so it looks for the first discontinuity
in the uninterrupted aerosol region. For this reason and for simplicity we
will refer to the retrieved nocturnal boundary layer as to LCBL even when the
mixing is not due to convection, but rather to mechanical mixing from the
surface and katabatic winds.
LCBLH retrieval
The seasonal-averaged daily cycles of the retrieved LCBLH and TCAL during
spring, summer, autumn and winter are shown in Fig. 6. During spring
(Fig. 6a), summer (Fig. 6b) and (partially) autumn (Fig. 6c), the LCBLH grows
through morning until it reaches a peak in the afternoon. In summer, the LCBLH
has been retrieved by PathfinderTURB every day with only a few exceptions. In
spring, (March–May), and in summer (June–August) the LCBL has reached the
JFJ on 20 and 9 individual days, respectively. These occurrences lay
above the 75 percentile of the LCBLH dataset and, hence, are not represented
by the blue-shaded area in Fig. 6a–b. From the systematic visual inspection
and comparison of LCBLH time series at PAY and KSE, we can say that the LCBLH
peak occurs later at KSE than at PAY. During the night, the LCBLH drops, due
to the concurrent effects of aerosol gravitational settling, subsidence and
katabatic winds, which result from radiative cooling of the surface,
triggering katabatic drainage flows. A likely explanation of the delay in the
onset of the LCBL and of the afternoon peak at KSE is the
night-time katabatic winds driving FT air down into the valley underneath.
Depending on the season, these winds can continue to blow for few hours after
sunrise (especially from the shaded mountain side) and work against the
formation of the LCBL. The LCBLH temporal evolution follows the classical
shape of a growing convective boundary layer like over flat terrain, but the
growth and the duration of the LCBL occur over a shorter period. This is
consistent with the delayed onset of the LCBL due to the persisting katabatic
winds in the first hours of the morning and the earlier weakening of
convection due to the shading effect of the surrounding mountains and the
afternoon onset of the katabatic winds. This phenomenon is particularly
enhanced during winter when the solar irradiance is at its minimum and the
katabatic winds tend to suppress LCBL most of the time.
In autumn (September–November), the LCBLH shows a less pronounced daily
cycle than in spring and summer, this is probably due to the fact that the
vertical transport of aerosol-rich air is reduced by the stabilization within
the lower troposphere during this period (Lugauer et al., 1998).
In winter, (December–February) PathfinderTURB could retrieve only a few
LCBLH measurements because of the very stable
meteorological conditions, the reduced convection and the prolonged snow and
ice cover limiting the aerosol production at KSE and the surrounding valleys.
For this reason the seasonal-averaged daily cycle in Fig. 6d does not show
any particular pattern of the LCBLH, mainly due to the very low retrieval
counts.
All occurrences of when the LCBLH and TCAL have reached the JFJ during the
different months are listed in Table 1.
TCAL retrieval
During spring and autumn, the daytime TCAL evolution is correlated with the
LCBLH, especially in spring during the first hours after sunrise (convective
growth) and until the afternoon peak. The night-time evolution of the TCAL in
spring and autumn also shows a correlation, although weaker, with the LCBLH.
In summer, the TCAL does not show any significant correlation with the
temporal evolution of the LCBLH during the day or night. During winter, the TCAL
shows no correlation with the LCBLH. Despite an overall absence of a daily
pattern of the winter LCBLH, the TCAL shows a clear outline during the period
00:00–10:00 UTC. This bimodal pattern with higher TCAL during the first
part of the day could be explained by the process of dissipation of the CAL
caused by the wind shear along the line of sight connecting KSE and JFJ when
the solar irradiance modifies the wind dynamics during the central hours of
the day.
Season-averaged daily cycle of the TCAL (red dots) and of the LCBLH
(blue dots) at KSE. The size of the dots corresponds to the number of
measurements available in each temporal bin. (a) Spring (March to
May); (b) summer (June to August); (c) autumn (September to
November); (d) winter (January to February). Shaded areas show the
25–75 % interquartile range (IQR) for LCBL (purple) and TCAL (red). The
altitude of JFJ is indicated by the black dashed horizontal line.
Occurrence frequency of LCBL and CAL reaching JFJ
Table 1 shows, for each month during the studied period, the number of hours
(cumulative 2 min data points over the month), the number of days (number of
days with at least one data point) and the percentage of time (time when the
JFJ was inside LCBL or CAL as a percentage of the total time when the
retrievals existed). On the left-hand side of the table, we show the
statistics corresponding to when the JFJ is reached by or embedded into the
LCBL, and on the right-hand side we show the statistics corresponding to when
the JFJ is either into the LCBL or the CAL (LCBL + AL). The statistics
show that during winter (italic rows in Table 1) the aerosol measurements at
the JFJ are never directly influenced by the LCBL air, which remains
constantly below the JFJ. Moreover, the total duration of time when
PathfinderTURB has detected the LCBL rising above KSE (but not touching the
JFJ) during winter accounts for no more than 65.52 h. However, the CAL
reaches the JFJ about one quarter of the time (21.23 %), which
corresponds to a duration of 109.44 h (distributed over 26 days). The
remaining three quarters of time (78.77 %), corresponding to a duration
of 406.32 h, the JFJ is situated in the FT, i.e. the in situ measurements
are characterized by background (molecular) conditions. Although it is
impossible to establish the exact origin of the air in the aerosol layer
(i.e. the injection layer), we can speculate that winter aerosol layer is
composed of aerosols originating from long-range transport and synoptic-scale
lifting, rather than LCBL injections.
During summer (bold rows in Table 1) the situation changes significantly,
with the LCBL reaching the JFJ during 3.63 % of time, corresponding to
34.56 h (distributed over 20 days).
Statistics of frequency of LCBL and CAL reaching or embedding the
JFJ.
JFJ inside LCBL JFJ inside CAL DateHoursDays%DateHoursDays%09/20144.8721.9409/2014149.031823.88210/20148.0045.8110/201488.701816.2911/20141.6734.2011/201472.231324.4912/20140.0000.0012/201443.30926.6701/20150.0000.0001/201533.531021.7602/20150.0000.0002/201532.70716.4103/20150.210.1203/201545.771310.2404/20155.6733.5904/201580.731514.8205/20155.4352.2105/2015114.071722.8306/20150.5020.1606/2015174.602429.3007/201518.60125.6107/2015380.472856.4908/201515.5065.1708/2015217.631936.3409/20150.9720.5109/201556.301211.5010/20150.0000.0010/201519.8765.5311/20150.210.3611/20154.1021.42
Although the relatively low percentage may imply a marginal effect, the
striking parameter is that during summer the undiluted, aerosol-laden air of
the LCBL is able to reach the JFJ (and potentially strongly affect the in
situ measurements of particle concentrations and their optical properties) on
20 different days. With regard to the frequency and duration when the CAL has
reached or embedded the JFJ, the statistics are even more remarkable, with
40.92 % of the time or 772.8 h distributed over 71 days. Also for the
summer statistics, no quantitative conclusions can be drawn about the origin
and type of the aerosols inside the
aerosol layer. The aerosols could be locally emitted and injected into the aerosol layer or
transported on regional or continental scales and could have been formed
secondarily in the aerosol layer (Bianchi et al., 2016). In any case, the convective
conditions occurring frequently during the summer suggest a significant
mixing of the LCBL air into the FT forming the aerosol layer. Also the measurements by
the in situ instrumentations at the JFJ show that the absorption coefficient
(indirectly proportional to the black carbon concentration) is largest during
the summer period.
As mentioned in the previous sections, these results are in agreement and
confirm the indirect measurements and model simulations done in the previous
works, especially those by Zellweger et al. (2003), Collaud Coen et
al. (2011), Ketterer et al. (2014) and Herrmann et al. (2015). Here, and for
the first time, the occurrence of the convective (LCBL) and injection (aerosol layer)
layers directly reaching the in situ instrumentation at the JFJ has been
statistically analysed based on 1 year of data. The big advantage of
applying PathfinderTURB to the ceilometer profiles is to have an automatic
retrieval of the LCBLH and the TCAL directly at the JFJ. That allows us to
obtain real values of LCBLH or TCAL at the JFJ and not to use detections made in an
atmosphere located many kilometres from the JFJ. Moreover,
measurements that are not co-located require stringent homogeneity conditions of the atmosphere
between the point where the LCBLH and TCAL have been detected and the JFJ.
Comparison with in situ instrumentation
Figure 7 shows the relation that exists between the daily maximum of the
LCBLH retrieved by PathfinderTURB and the corresponding (in time) absorption
coefficient, α, at 637 nm measured by the MAAP at the JFJ. The
vertical red dashed line shows the altitude of the JFJ, and each box collects all
the LCBLH retrievals within 400 m of vertical span and the corresponding
values of α. In each box the number of LCBLH-α pairs is
indicated by N, and the median of each box is connected by the black dashed
line to show the median trend. The data in the box plot are from all seasons,
in order to maximize the number of occurrences and increase the statistical
significance of the trend.
Box plot showing the relation between the absorption coefficient at
637 nm measured by the MAAP at the JFJ versus the LCBLH retrieved by
PathfinderTURB for the period September 2014 to November 2015. The boxes show
the median (within the box), the interquartile range (upper and lower box
boundaries) and the 15.87–84.13 percentile range of α values
(whiskers).
Box plot showing the relation between the absorption coefficient at
637 nm measured by the MAAP at the JFJ versus the TCAL retrieved by
PathfinderTURB for the period September 2014 to November 2015. The boxes show
the median (within the box), the interquartile range (upper and lower box
boundaries) and the 15.87–84.13 percentile range of α values
(whiskers).
A linear median trend characterized by a small slope could be fitted to the
LCBLH-α pairs for LCBLH lower than the JFJ (2000–3380 m). For this
range of altitudes the LCBL grows deeper getting closer to the height of the
JFJ. The injections of LCBL air into the aerosol layer (embedding the JFJ) are then more
likely to occur when the LCBLH reaches its maximum, injecting LCBL air past
the in situ sensors with a resulting higher value of α. As soon as
the LCBLH reaches the JFJ, the injections into the aerosol layer reaching the in situ
instrumentation become more important, and this is shown by the change in
slope of the median trend. When the LCBLH maxima are higher than the JFJ, the
in situ instrumentation are reached by undiluted, aerosol-laden LCBL air and
the absorption coefficient α grows even more. In addition to the
slope of the median trend, it is important to explain the interquartile
variability of each box and their physical meaning. The first box, centred at
about 2000 m, shows a large interquartile range of α values; this is
due to Saharan dust events occurring mainly during autumn and winter above
the LCBLH and increasing significantly the value of α. The box
centred at the JFJ height also shows a large interquartile range of α
values, in this case the variability is due to the large α values
corresponding to the LCBLH higher than the JFJ and the smaller α
values corresponding to the LCBLH lower than the JFJ. In conclusion, Fig. 7
clearly shows the impact of the LCBL air on the absorption coefficient
α measured at the JFJ.
Box plot of α and FT, CAL and LCBL pairs. Each portion collects
all pairs over the corresponding atmospheric region for all seasons. The
boxes show the median (within the box), the interquartile range (upper and
lower box boundaries) and the 15.87–84.13 percentile range of α
values (whiskers).
In the same way as in Fig. 7 for the LCBLH, Fig. 8 shows the relation between
α and the TCAL. Differently from Fig. 7, it is not only the maxima of
α and TCAL that are shown in the
box plot, but also all the hourly data from all seasons. The TCAL represents
the upper boundary of the aerosol layer, when the TCAL is below the JFJ, the
in situ instrumentation on the JFJ is located inside the FT, showing very
little absorption. Within the range of altitudes 2000–3380 m, the slope of
the median trend is smaller than the one in Fig. 7; this is because even when
the TCAL grows deeper towards the JFJ, the strength of the injections coming
from beneath the aerosol layer is insufficient to significantly influence the
absorption measurements. As for the LCBL, when the TCAL reaches the JFJ the
α values also become larger and the slope changes accordingly. Because the aerosols injected into
the aerosol layer do not undergo a convective mixing, they tend to settle
under the gravity force leading to higher aerosol concentration at the bottom
than at the top of the
aerosol layer. For this reason the absorption grows larger proportionally to a higher
TCAL (3380–4580 m) and the slope of the trend remains almost constant
showing the linearity of the physical process. For higher altitudes
(z > 4580 m) α continues to grow, but at a lower rate
and with a decreasing number of occurrences. For the TCAL–α
relation, the interquartile range of the box centred at the height of the JFJ
is larger than the other boxes, showing larger values of α for
TCAL > JFJ and smaller α for TCAL < JFJ.
In order to summarize the results shown in Figs. 7 and 8, we provide in
Fig. 9 the overall impact of the LCBL, CAL and FT on the in situ measurements
of α. Each box collects all data from all seasons corresponding to
the background atmosphere (FT), the partially mixed air (CAL) and the
undiluted, aerosol-laden air (LCBL) with respect to the position of the JFJ.
This box plot perfectly represents the impact of the three atmospheric
regions and confirms the importance of an automatic monitoring of the
atmosphere at the JFJ and in general in the mountainous regions where the
dynamics are complex due to topography and wind circulation.
Conclusions
A novel algorithm, PathfinderTURB, has been developed, validated and applied
to retrieve the vertical structure of the planetary boundary layer.
PathfinderTURB provides reliable estimates of the daytime convective
boundary layer height and of the top of the continuous aerosol layer operationally and without need of ancillary data or any a priori
information (except for climatological limits) from a model. PathfinderTURB
can also be adapted to different probing line's angles and types of
instrument. For this study, two settings have been tested and applied to the
data of two CHM15k types, the vertical-pointing and tilted-pointing.
PathfinderTURB has been applied to 1 year of data measured by two CHM15k
ceilometers operated at the Aerological Observatory of Payerne, on the Swiss
Plateau, and at the Kleine Scheidegg, in the Swiss Alps. The algorithm has
been thoroughly evaluated and validated at Payerne. The CBLH retrievals
obtained by PathfinderTURB have been compared against two references, (i) the
manual detections by human experts and (ii) the noon CBLH values retrieved by
two methods based on radiosounding data: the parcel method and the bulk
Richardson method. Based on the excellent agreement with the two references,
PathfinderTURB has been applied to the ceilometer's backscatter profiles
between the Kleine Scheidegg and the Jungfraujoch for the period
September 2014–November 2015. The real-time monitoring of the local CBL and the TCAL at the JFJ has allowed the quantification of the occurrence of
these two layers and understanding of their impact on the absorption
coefficient, α, measured in situ at JFJ. The results have shown that
the CAL reaches or includes the JFJ 40.92 % of the time in
summer and 21.23 % of the time in winter for a total of 97 days during the two
seasons. The LCBL reaches or includes the JFJ for short periods (3.94 %
of the time) on 20 days in summer and never during winter. The impact of the
LCBL and CAL on the in situ measurements of α at the JFJ is
unambiguously shown in Figs. 7 and 8 for different ranges of altitudes. The
relation of the LCBLH-α and TCAL-α pairs is linear, but with
different slopes for altitudes below and above the JFJ, with a clear
modification of α due to the injections of the LCBL air into the
aerosol layer reaching the in situ instrumentation at the JFJ. In a more
general way, the overall impact of the LCBL, CAL and FT on the in situ
measurements of α is shown in Fig. 9. As expected the LCBL modifies
the in situ measurements at the JFJ more in terms of absolute value of
α, but it is outnumbered by a factor of 10 in terms of occurrences by
the CAL. The CAL is in fact more diluted than the LCBL but embeds the JFJ 10
times more frequently than the LCBL, and then its impact on the in situ
measurements is significant. The rest of the time the JFJ is within the FT
with values of absorption characteristic of a molecular atmosphere.
The results obtained at KSE and JFJ are in agreement and confirm the
indirect measurements and model simulations of previous works, especially
those by Zellweger et al. (2003), Collaud Coen et al. (2011), Ketterer et
al. (2014) and Herrmann et al. (2015). Differently from the previous works, our
study has provided for the first time the possibility to calculate the
occurrences of the convective (LCBL) and injection (aerosol layer) layers directly at
the JFJ. The added value is the real-time application of PathfinderTURB to
the ceilometer profiles connecting KSE to the JFJ and the possibility to have
automatic LCBLH and the TCAL values at the JFJ. Indeed, before our study,
lidars, ceilometers and wind profilers have always been used for vertical
probing at a fixed distance (5–15 km) from the JFJ, which required
stringent assumptions about the homogeneity of the atmosphere between the
measurement site and the JFJ. The results presented have proven the
importance of an automatic monitoring of the atmosphere at the JFJ and in
general in the mountainous regions where the dynamics are complex due to
topography and wind circulation.
Overall, based on the adaptability of PathfinderTURB to diverse
topographic conditions and on the fact that it does not require real-time
ancillary data, PathfinderTURB is best suited to treat a large dataset from
networks of ceilometers in real time.
The underlying research data used to create the graphics in
this article are available online at
https://doi.org/10.3929/ethz-b-000179601.
Table of acronymsAcronymDescriptionALAerosol layerBLBoundary layerbRBulk RichardsonCALContinuous aerosol layerCBHCloud base heightCBLConvective boundary layerCBLHConvective boundary layer heightEZEntrainment zoneFTFree troposphereIQRInter-quartile rangeJFJJungfraujochKSEKleine ScheideggLCBLLocal convective boundary layerLCBLHLCBL heightLidarLight detection and rangingliminfMinimum altitude limitlimphysphysically meaningful altitude limitMAAPMulti-angle absorption photometerMLMixed layerMWRMicrowave radiometerPathfinderTURBPathfinder method based on turbulencePAYPayernePMParcel methodRibBulk Richardson numberRLResidual layerRMSERoot mean square errorRSRadiosondeSNRSignal-to-noise ratioTCALTop of continuous aerosol layer
The Supplement related to this article is available online at https://doi.org/10.5194/acp-17-10051-2017-supplement.
The authors declare that they have no conflict of
interest.
Acknowledgements
This study has been financially supported by the SNF through ICOS-CH. The
authors would further like to thank Nicolas Bukowiecki for giving access to
Jungfraujoch aerosol measurement data. The authors are grateful to Kornelia Pönitz
and Holger Wille (Lufft) for technical information about the CHM15k.
Edited by: Ernest Weingartner
Reviewed by: three anonymous referees
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