ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus GmbHGöttingen, Germany10.5194/acp-15-7605-2015A comprehensive investigation on afternoon transition of the atmospheric
boundary layer over a tropical rural siteSandeepA.RaoT. N.tnrao@narl.gov.indrtnr2001@yahoo.comRaoS. V. B.National Atmospheric Research Laboratory, Gadanki – 517 112, IndiaSri Venkateswara University, Department of Physics, Tirupati – 517 502, IndiaT. N. Rao (tnrao@narl.gov.in, drtnr2001@yahoo.com)14July201515137605761730October201412December201417June201525June2015This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://acp.copernicus.org/articles/15/7605/2015/acp-15-7605-2015.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/15/7605/2015/acp-15-7605-2015.pdf
The transitory nature
of the atmospheric boundary layer (ABL) a few hours before and after the time of
sunset has been studied comprehensively over a tropical station, Gadanki
(13.45∘ N, 79.18∘ E), using a suite of in situ and remote
sensing devices. This study addresses the following fundamental and important
issues related to the afternoon transition (AT): which state variable first
identifies the AT? Which variable best identifies the AT? Does the start time
of the AT vary with season and height? If so, which physical mechanism is
responsible for the observed height variation in the start time of the
transition?
At the surface, the transition is first seen in temperature (T) and wind
variance (σWS2), ∼ 100 min prior to the time of
local sunset, then in the vertical temperature gradient and finally in water
vapor mixing ratio variations. Aloft, both signal-to-noise ratio (SNR) and
spectral width (σ) show the AT nearly at the same time. The T at
the surface and SNR aloft are found to be the best indicators of transition.
Their distributions for the start time of the AT with reference to time of
sunset are narrow and consistent in both total and seasonal plots. The start
time of the transition shows some seasonal variation, with delayed
transitions occurring mostly in the rainy and humid season of the
northeast monsoon. Interestingly, in contrast to the general perception, the signature
of the transition is first seen in the profiler data, then in the sodar data,
and finally in the surface data. This suggests that the transition follows a
top-to-bottom evolution. It indicates that other processes, like entrainment,
could also play a role in altering the structure of the ABL
during the AT, when the sensible heat flux decreases progressively. These
mechanisms are quantified using a unique high-resolution data set to
understand their variation in light of the intriguing height dependency of
the start time of the AT.
Introduction
The behavior of the atmospheric boundary
layer (ABL) during the transition from a well-mixed layer during the day to a
stably stratified layer during the night is quite complex and is also poorly
understood. In recent years, the afternoon transition (AT) and evening
transition (ET) of the ABL have gained attention for various reasons (Lothon
et al., 2014). These transitional regimes are found to be important for the
vertical transport of species, like pollutants, water vapor and ozone (Klein et al., 2014), the inception and strength
of the nocturnal low-level jet (LLJ) (Mahrt, 1981; Van De Wiel et al., 2010),
and the whole structure of the nocturnal boundary layer. Furthermore,
identification of the ABL becomes uncertain and there is no consensus on
which scaling laws (day-time convective scaling due to surface buoyancy flux
or nocturnal boundary layer scaling due to surface wind stress) would work
well during this period (Pino et al., 2006). Furthermore, the start time of
the transition and its duration could be different at the surface and aloft
because the turbulence may not immediately dissipate after the sunset (Busse
and Knupp, 2012).
Researchers defined the transition in a variety of ways employing various
parameters obtained from different instruments. Some of them treated the
transition as an instantaneous process, while the others considered it as a
process of a few hours. The most popular and widely used definition is the
reversal of surface heat flux (positive to negative) (Grant, 1997; Acevedo
and Fitzjarrald, 2001; Beare et al., 2006; Angevine, 2008). A similar
technique is employed by Nieuwstadt and Brost (1986), in which the AT is
assumed to occur following the cessation of upward surface sensible heat
flux. Edwards et al. (2006) noted that the shortwave heating starts to
decrease long before the surface heat flux changes its sign. They included
the shortwave heating in the definition of the AT, which shifted the start of
the afternoon transition to an earlier time. Acevedo and Fitzjarrald (2001)
identified the start time of the transition from a sharp decrease in the
spatial temperature difference and end from the maximum spatial standard
deviation of temperature. As seen above, all these definitions are based on
surface measurements and do not account for the physical processes occurring
aloft during the transition.
The studies that used remote sensing measurements like wind profiling radars,
sodars and lidars focused more on the processes aloft (mostly in the lower
part of ABL) to define the AT. In a seminal study, Mahrt (1981) used a
kinematic definition for the AT period. According to Mahrt (1981), the AT is
a 4–5 h time period, starts from the time of low-level wind deceleration
(typically 2 h before the sunset) and ends when the flow at all levels
turned towards the high pressure. Grimsdell and Angevine (2002) and
Angevine (2008), using radar wind profiler measurements, noticed that both
reflectivity (range-corrected signal-to-noise ratio (SNR)) and the spectral
width (σ) (a measure of turbulence) decrease sharply during the AT.
The applicability of these approaches is always an issue, particularly when
the turbulence is either weak or strong throughout the day or when the
turbulence increases due to some other processes associated with katabatic
winds or land sea-breeze circulations (Sastre et al., 2012). Instead of
defining the start and end times for the AT, Busse and Knupp (2012) studied
the variations in meteorological parameters with reference to the sunset
time. They noted an increase in wind speed and a decrease in sodar return
power in the lower ABL. They found that the AT has a relatively consistent
pattern regardless of season.
A few studies employed models to understand or validate the occurrence of
different types of transition (Brazel et al., 2005; Edwards et al., 2006;
Pino et al., 2006; Sorbjan, 2007; Nadeau et al., 2011; Sastre et al., 2012).
Brazel et al. (2005) studied the evening transition under weak synoptic
forcing that favors the local thermal circulations and compared the observed
transitions with models. Recently, Sastre et al. (2012) identified
three types of evening transitions and evaluated the performance of the
Weather Research and Forecasting Advanced Research (WRF-ARW) model in
reproducing these transitions by varying PBL parameterization schemes. They
noted that all parameterizations reproduced the observed behavior of AT in
certain circumstances. Noting the need to understand the transitions in a
better way, several field campaigns were conducted in recent years, employing
both in situ and remote sensors, exclusively for better
characterization and
modeling of the transitions, for instance,
the Cooperative Atmosphere-Surface Exchange Study (CASES-99) (Poulos et al.,
2002), Boundary Layer Late Afternoon and Sunset Turbulence (BLLAST)
(http://bllast.sedoo.fr/) (Lothon et al., 2014) and the Phoenix Evening
Transition Flow Experiment (TRANSFLEX) (Fernando et al., 2013). Recently,
manned and unmanned aerial vehicles were used to study the vertical structure
of the lowest part of the ABL during the AT (Bonin et al., 2013; Lothon et
al., 2014).
Most of the above studies focused on the variations in state variables like temperature, humidity,
wind and turbulence in the surface layer, as they are easily accessible.
Other studies characterized the evening transitions aloft, but neglected the
variations at the surface. Only a few studies that were based on campaign
data and/or a few months of data dealt with the transitions in totality,
i.e., studied the variations at the surface and aloft (Busse and Knupp, 2012;
Fernando et al., 2013; Lothon et al., 2014). Again, the data employed in
those studies were limited: a few days to 2 months. Certainly there is a need
to characterize and understand the transitions at the surface and aloft in
different seasons through systematic observations on a long-term basis.
Furthermore, earlier studies used different state variables to define the
transition. Only a few studies focused on how these state variables vary with
reference to the time of sunset (Busse and Knupp, 2012). Although some
tower-based observations exist in the literature, the complete understanding
of the transition over a deeper layer is certainly far from complete. This
forms the basis for the present study. In particular, the study tries to
answer the following questions: how do the surface state variables and
radar/sodar attributes vary during the transition and with reference to the
time of sunset? Which state variable better identifies the transition? How
does the start time of the transition vary with height and season? Which
physical processes are responsible for the vertical evolution of the
transition?
The paper is organized as follows: Sect. 2 introduces the measurement site,
data and instrumentation employed. The variation of different state variables
at the surface and aloft is studied with the help of a typical case study in
Sect. 3. The start time of AT as identified by different state variables and
their mean characteristics at the surface and aloft are studied with
reference to the time of sunset. The questions posed above are discussed in
light of present observations in Sect. 4. The important forcing terms on the
ABL are estimated using a unique data set to understand the role of
entrainment in the afternoon transition. The important results are concluded
in Sect. 5.
Data and site description
The present study follows an integrated approach, wherein several instruments
available at the National Atmospheric Research Laboratory (NARL), Gadanki
(13.45∘ N, 79.18∘ E), are extensively used. This site is
located ∼ 375 m above the mean sea level in a rural area in
southeastern peninsular India and is surrounded by hillocks (300–800 m
within a 10 km region) distributed in a complex fashion. The rainfall in
this region is influenced primarily by two monsoons, southwest
(June–September) and northeast (October–December) (Rao et al., 2009).
Summer and winter are the other two seasons, covering the months of
March–May and January–February, respectively.
Instruments used in the integrated approach, their operating
frequency, height coverage, vertical and temporal resolutions and duration of
data.
InstrumentFrequencyMeasured parametersHeightVerticalTemporalPeriod usedof operationcoverageresolutionresolutionSODAR1.8 kHzSNR, winds and σ0.03–1.5 km30 m27 s2007–2010LAWP1.357 GHzSNR, winds and σ0.3–4.2 km150 m∼ 11 min1999–2000WPR8×81.280 GHzSNR, winds and σ0.3–6.15 km150 m∼ 10 min2010WPR16×161.280 GHzSNR, winds and σ0.75–5.025 km75 m∼ 10 min2010–2011MBLMT, r, pressure, WS,5–15 m5 m1 s2009–2011WD and shortwave radiationGPST, RH, pressure0–30 km100 m3 h17–19 Jan 2011Radiosonde21–24 Jul 201150 m towerSonic temperature, vertical wind8 m0.05 s17–19 Jan 201121–24 Jul 2011
The present study relies on a variety of instruments, both in situ and remote
sensors (Table 1), whose measurements cover the entire ABL. Though these
instruments provide several other parameters, those used in the present study
are only listed in Table 1. Two kinds of data sets (we refer to them here as
data set 1 and data set 2) are used in the present study, but for different
purposes. Data set 1 was collected with a suite of non-continuously operated
instruments, spanning a 3-year period. This data set is being used to examine
the seasonality and height dependence of AT. It includes long-term
observations made by an instrumented 15 m tower (hereafter referred to as
the Mini Boundary Layer Mast – MBLM), a Doppler sodar and three UHF wind
profilers (operated at NARL, but during different years). Data set 2 is
comprised of the intense observations, which include the instrumentation of
data set 1 along with a flux tower having a sonic anemometer (RM Young 8100)
at 8 m level and radiosondes (Meisei 90) launched every 3 h. Data set 2 was
collected over two 3-day campaigns (one during the monsoon and one during the
winter). This data set is being used to understand the role of surface
forcing and entrainment in triggering the AT.
Details of measured parameters and sensors (make, model
number, resolution and accuracy) on MBLM.
ParameterMakeModel no.ResolutionMeasurement heightAccuracyWind speed and wind directionRM Young05103V1 Hz5, 10 and 15 m0.3 m s-1 and 2∘Temperature and relative humidityRotronicsHygroclip S31 Hz5, 10 and 15 m0.3 ∘C and 2 %PressureKomolineKDS-0211 Hz1.2 m1 hPaShortwave radiationKipp & ZonenCMP 61 Hz1.2 m1 W m-2
Major specifications of SODAR, LAWP, WPR8×8 and
WPR16×16.
ParameterSODARLAWPWPR16×16WPR8×8Operating frequency1.8 kHz1357.5 MHz1280 MHz1280 MHzPeak power100 W1 kW1.2 kW0.8 kWAntenna array1 m × 1 m3.8 m × 3.8 m2.8 m × 2.8 m1.4 m × 1.4 mPulse width180 ms1 µs (uncoded)4 µs (coded)1 µs (uncoded)Inter pulse period (µs)9 × 106405555No. of coherent integrations1706432No. of incoherent integrations11002020No. of FFT points409612810241024Beam width (deg)4356.5Range resolution (m)3015075150Beam directions*N16, Z, E16E15, Z, N15E15, W15, Z, N15, S15E10N10, W10S10, Z, W10N10, E10S10
* E, W, Z, N and S denote eastern, western, zenith, northern
and southern directions, respectively, and the number indicates the
off-zenith angle.
The MBLM provides temperature (T), relative humidity (RH), wind speed (WS)
and wind direction (WD) data at three levels (5, 10 and 15 m) with 1 s
temporal resolution. The type of sensors used and their accuracies are given
in Table 2. A Doppler sodar operating at a frequency of 1.8 kHz and a peak
power of 100 W provides the SNR, σ and wind information at 27 s and
30 m temporal and height resolutions, respectively (Anandan et al., 2008)
(see Table 3 for more details about different remote sensing instruments).
The UHF wind profiler data consist
of the data from three wind profilers, operated during different years. An
old UHF wind profiler (referred to as the Lower Atmospheric Wind Profiler –
LAWP) was operated at a frequency of 1.375 GHz during the period 1999–2000.
Complete description of the system and specifications can be found in Reddy
et al. (2001) and Rao et al. (2001). It was operated in two modes; low mode
covering 0.3 to 4.8 km and high mode covering 0.9 to 6.8 km, sequentially
switching between each mode, providing a temporal resolution of
∼ 11 min. Recently, NARL has indigenously developed two UHF wind
profilers with the same frequency (1.28 GHz) but with different antenna
dimensions and transmitted powers. The smaller UHF wind profiler that uses an
8 × 8 antenna array covering an area of 1.4 m × 1.4 m
transmits a power of 0.8 kW (hereafter referred to as WPR8×8).
Whereas, the larger profiler has a bigger antenna array of
2.8 m × 2.8 m with 16 × 16 elements and high-transmitting
power of 1.2 kW (hereafter referred to as WPR16×16). Complete
description of these systems and their capabilities can be found in
Srinivasulu et al. (2011, 2012). The WPR8×8 was operated at NARL
during May–September 2010, while the bigger WPR16×16 has been in
operation from October 2010. It can be seen from Tables 1 and 3 that these
instruments provide a unique long-term data set from the surface to top of
the ABL.
Details of data set 1 grouped as a function of season, showing the
total number of days for which data are available, number of discarded days
due to cloudy sky/rain or data gaps and number of clear days finally used in
the present study. Win, Sum, SWM and NEM stand for, respectively, winter,
summer, southwest monsoon and northeast monsoon.
Season15 m tower (2009–2011) Sodar (2007–2010) Profiler (1999–2000, 2010–2011) WinSumSWMNEMWinSumSWMNEMWinSumSWMNEMTotal no. of days113195263221207333414255108238381264Discarded days255515813010515228218941101227140Clear days88140105911021811326667137154124
A series of automated tests were performed on tower time series data to
identify instrumentation problems, flux sampling problems, and physically
plausible but unusual situations (Burba, 2013). Furthermore, clear-sky days
are identified from shortwave radiation measurements made by a pyranometer
(Kipp and Zonen CMP6) located near the MBLM. Omitting the days with large
data gaps and rain/dense clouds, 423 days of surface data were available for
further analysis from 3 years of MBLM measurements. The range-time plots of
spectral moments (SNR, vertical velocity (w) and σ) from sodar and
the wind profiler are examined for the clear growth and decay of ABL and
convection/precipitation contamination (Grimsdell et al., 2002; Rao et al.,
2008). Based on the above criteria, a total of 530 and 482 clear-sky days of
sodar and profiler, respectively, were only selected (from data set 1) for
further analysis. To examine whether the filtering of data for clear-sky days
has caused any bias towards the dry season (winter and summer), the data are
segregated on the basis of the season. Table 4 shows the number of days for
which the measurements were available, the number of discarded days due to
rain, dense cloud or bad data quality and the number of days considered for
the present study as a function of the season. Though considerable data were
filtered out in the rainy seasons (southwest and northeast monsoons),
the number of available days is large enough to represent the season. Also,
the number of days available in the rainy season is on the same order as that of other seasons, indicating that the
filtering has not biased the results towards any season.
Note that MBLM, sodar and wind profilers were operated during different
years. Only 19 days of simultaneous clear-sky measurements (without large
data gaps) from all the above sensors were available. Measurements from these
19 days are used to understand the behavior of AT at different altitudes. The
total data (from different years, i.e., data set 1) are used to obtain robust
statistics on the mean behavior of the AT.
Diurnal variation of state variables at the surface and aloft on
11 May 2010, MBLM-derived surface (a)T, (b)r,
(c) WS (d)σWS2 and (e) WD and
sodar-derived (f) range-corrected SNR, (g)σ,
(h) WS, (i)w and (j) WD.
(k–o) Same as (f–j), except for
profiler-derived state variables. The solid vertical line indicates the time
of sunset.
Results and discussionTypical evolution of the AT from the surface to the top of the ABL
Figure 1 shows the diurnal variation of surface state variables (T, water
vapor mixing ratio (r), WS, wind variance (σWS2) and wind
direction (WD)) and sodar and profiler attributes (range-corrected SNR
(hereafter referred to simply as SNR), horizontal wind speed, σ, w
and wind direction) on 11 May 2010, providing a comprehensive paradigm of the
typical evolution of the transitional boundary layer at the surface and aloft
(up to 3.6 km). The surface state variables (at 5 m level) exhibit larger
variations during the transition period than during the rest of the night.
During the AT, as the shortwave heating decreases, the temperature decreases
monotonically (Fig. 1a) in clear-sky conditions, if temperature advection is
neutral. Another signature of this transition can be seen in short-term
variability of surface parameters, highly variable during the noon
(associated with thermals) to smaller fluctuations in the night. The
weakening of thermals (both magnitude and their vertical extent) in the
afternoon reduces the convective turbulence and σWS2
(Fig. 1d). This reduction weakens the downward transport of momentum and
low-level wind speed (Fig. 1c) (Mahrt, 1981; Acevedo and Fitzjarrald, 2001).
The surface winds also became less gusty during the transition. During the
day, when the convective turbulence is active, the low-level moisture gets
diluted because of the transport of moisture by turbulence. As the turbulence
decreases during the transition, the low-level moisture having most of its
sources on the Earth's surface increases in the absence of strong mixing
(Fig. 1b). On some days, this increase appears as a sudden jump, as also
noted by earlier studies (Busse and Knupp, 2012), and on the other days it is
more gradual. The wind direction nearly remains the same from
∼ 14:00 IST (Indian Standard Time
(IST) = UTC + 05:30 h) to
midnight (Fig. 1e).
To understand the transitions aloft, variation of sodar and profiler
attributes are examined in detail (Fig. 1f–o). Figure 1 clearly shows the
transition of the ABL from a highly convective to a more stable regime. When
the convective turbulence is active during the day time, the thermals are
clearly apparent as columns of enhanced backscatter in the time-height SNR
plot (Fig. 1k). Though the thermals do not appear clearly in the SNR of sodar
in this case, they appear very clearly in other cases. These plumes are also
visible in the w plot (Fig. 1i and n) as enhanced up- and down-ward motions
with w values exceeding ±2 m s-1 and as columns of enhanced
turbulence (Fig. 1g and l). The backscatter for sodar and profiler depends on
the refractive index irregularities caused primarily by turbulence-driven
temperature and humidity variations. The SNR is, therefore, high during the
day, when the convectively driven turbulence is active. Nevertheless, about
2 h before the sunset, both the intensity and vertical extent of thermals
start to decrease continuously till the sunset occurs.. The minimum
backscatter (SNR) is seen just before the sunset, mainly due to the weak
turbulence. The magnitude of backscatter and vertical extent of sodar data
again increase in accordance with the deepening of the inversion layer. As
noted by Busse and Knupp (2012), the winds within the nocturnal boundary
layer generally decrease during the AT, but increase above the nocturnal
boundary layer. It makes the identification of the start time of the AT using
wind speed somewhat ambiguous. On the other hand, it is rather easy to
identify the start time of the AT from the variations of SNR and σ.
The wind direction does not change much with altitude below 1.5 km and
remains mostly easterly to southeasterly (Fig. 1j and o). It
also does not
change much with time around the time of sunset (a few hours before and after
the time of sunset), ruling out the possibility of advection of different air
masses causing the above changes.
When the surface heating reverses to cooling in the evening, both convection
and turbulence gradually reduces till the subsequent development of a stable
boundary layer with well-defined surface inversion layer. As a result, all
state variables at the surface and aloft, manifested primarily by the
turbulence, vary considerably during this period. To better depict this
variability, MBLM- (T, r, σWS2 and ΔT
(T5–T10 indicating the stability of the lower ABL; the suffixes 5
and 10 indicate the height of temperature measurements in m)), sodar- and
profiler-derived state variables (SNR and σ at three representative
levels; 150, 300 and 450 m for sodar and 900, 1500 and 2100 m for profiler)
during the period 15:00–21:00 IST are plotted in Fig. 2. To minimize random
fluctuations and the chosen level more representative, the data are averaged
both in time (5 min for sodar and no temporal integration for profiler) and
height (three heights centred on the chosen level). The time series surface
data are then low-pass filtered using local regression using weighted linear
least squares and a first-order polynomial model (using the function
“lowess” in
matlab). On 11 May 2010, the temperature (Fig. 2a) starts to decrease
monotonically, at the rate of 1–1.5 ∘C per 1 h, from 16:10 IST
(dashed line), 152 min prior to the time of sunset (solid vertical black
line). Though the temperature decrement starts little early, but is not
consistent and also weak in magnitude. Another surface characteristic showing
a significant change during the AT is the mixing ratio (Fig. 2b), which
clearly shows a gradual increase from 16:10 IST. The temperature gradient
(Fig. 2c) also reverses from positive to negative few minutes after the 5 m
level temperature starts to decrease. The wind variance (Fig. 2d),
representing small-scale wind fluctuations and turbulence, also shows a
decreasing trend from 16:25 IST.
Temporal variation of state variables (at the surface and aloft) a
few hours before and after the time of sunset (indicated with a black solid
vertical line). Temporal variation of MBLM-derived (a)T,
(b)r, (c)ΔT and
(d)σWS2, sodar-derived
(e) range-corrected SNR and (f)σ and
profiler-derived (g) range-corrected SNR and (h)σ.
The sodar- and profiler-derived parameters are plotted at three
representative levels each (150, 300 and 450 m for sodar and 900, 1500 and
2100 m for profiler). Vertical dashed lines indicate the start time of the
transition as identified by different state variables.
The sodar and profiler backscatter, depends primarily on turbulent
irregularities of refractive index, decreases with the waning of sensible
heat flux (and thermals) during the afternoon transition. On 11 May 2010, the
SNR of sodar starts to decrease ∼ 2 h 40 min prior to the time of
sunset at all heights. Interestingly, the start time of SNR reduction shows
height dependence with higher altitudes showing the reduction earlier. The
SNR minimum is observed 10–20 min before the sunset at all heights, mainly
due to the reduction in turbulent fluctuations in temperature. Nevertheless,
the SNR increases again after the sunset, following the formation of an
inversion layer. The σ (Fig. 2f) variations are quite similar to that
of SNR during the transition. The σ shows a decreasing trend 2 h
10–20 min prior to the sunset, whereas its minimum is observed 10–30 min
from the time of sunset. The profiler SNR and σ variations are
similar to that of sodar, except that their reduction starts little early.
The profiler SNR and σ start to decrease ∼ 3 h prior to the
time of sunset. Also, the SNR and σ minima are observed at around the
time of sunset. It is very interesting to note the height dependency in the
time at which state variables show large variation; i.e., it is seen first in
profiler attributes, then in sodar attributes and finally in surface
parameters.
Distributions for start time of transition with reference to
the time of sunset
It is clear from the case study that surface parameters and sodar/profiler
attributes show large variations during the AT. The first and foremost
problem, therefore, is to properly and objectively identify the start time of
the AT from these state variables. It is also important to recognize the
state variable that unambiguously identifies the start time of the
transition. As seen in case studies, state variables like T, ΔT,
r and σWS2 at the surface and SNR and σ aloft
can be used for this purpose. For identifying the start time of AT, 19 days
on which measurements of all instruments (MBLM, sodar and profiler) are
available are considered. The start time of AT is identified manually from
temporal variation of each state variable (like those shown in Fig. 2). The
temporal gradients are estimated for each state variable from all 19 cases,
which are then finally used to fix the thresholds. The start time of AT is
identified from the variation of each state variable as follows.
Temperature: the time at which T starts to decrease by ≥ 0.5∘C in 30 min.
Water vapor mixing ratio: the time at which r increases by ≥ 0.5 g kg-1 in 30 min.
Wind variance: the time at which σWS2 decreases by ≥ 0.1 m2 s-2 in 30 min.
Temperature gradient: the time at which ΔT becomes positive to
negative and remains negative for at least an hour.
SNR: the time at which SNR decreases by > 1 dB in 30 min.
Spectral width: the time at which σ decreases by ≥ 0.1 m s-1 in 30 min.
Note that all the above conditions should hold good for at least an hour from
the start time of the transition. Also, all the above conditions are checked
only in the data during 15:00–20:00 IST.
First, the average behavior of the start time of the AT, as identified by
selected state variables, with reference to the sunset (i.e., start time of
the AT – time of sunset) has been studied at the surface and aloft. The
distributions (from data set 1) for the start time of the AT with reference
to the sunset (hereafter referred to as Transsunset (start time of
the AT – time of sunset)) as obtained by various state variables are shown
in Fig. 3. These distributions are shown as boxplots, where the box comprises
50 % of values (25th and 75th percentiles) and whiskers represent 5th and
95th percentile values. On average, σWS2 and T show the
first signature of the AT among all surface state variables (Fig. 3a),
∼ 1 h 40 min prior to the time of sunset, followed by ΔT
(1 h 18 min before sunset). The last characteristic for the transition is
seen in r as a gradual increase (or jump) occurring 1 h 10 min prior to
the time of sunset. The signature of the transition can be seen as early
(late) as 165 (45) min before (after) the sunset in σWS2
(r) on some days. Except for temperature, all other surface state variables
show the signature of the transition even after the sunset. Though not many
such cases are found at Gadanki (can be seen from Fig. 3a), late transitions
are not uncommon, as they are widely reported elsewhere (Acevedo and
Fitzjarrald, 2001). The distribution of Transsunset is wider for
r than for any other state variable, indicating that the jump in r occurs
at different timings with reference to the time of sunset. On the other hand,
the start time of the AT as obtained by T is relatively consistent
throughout the year, as evidenced by the narrow distribution (Fig. 3a).
Figure 3b–g shows distributions for Transsunset as identified by
selected sodar and profiler attributes (SNR and σ) at three selected
altitudes (150, 300 and 450 m for sodar and 900, 1500 and 2100 m for
profiler). At any particular altitude, both SNR and σ show the
signature of the transition around the same time. Though small differences
exist, they are not significant. Nevertheless, the identification of the
transition start time is somewhat easy with SNR and is also consistent, as
evidenced by its relatively narrow distribution.
As seen in the case study, the mean start time of the AT also shows height
dependency and follows top-to-bottom evolution; i.e., the signature of the AT
is seen first in the profiler data (∼ 2 h 40 min before the time of
sunset), then in sodar data (∼ 2 h before the time of sunset) and
finally in MBLM measurements. Angevine (2008) also noted the deterioration of
the ABL structure aloft with the wind profiler preceding the start time of
the AT at the surface. It contradicts the general perception that the entire
ABL is controlled primarily by the underlying Earth's surface and the start
time of transition should follow a bottom-up evolution. It is true that
surface forcing is the defining mechanism during the day, but it seems not
the case during the transition, the time during which other forces could also
be important.
Distributions (in terms of boxplot) of Transsunset
(= start time of AT – time of sunset) for different state variables,
depicting the behavior of the transition start time with reference to the
sunset time. Distributions for Transsunset at (a) the
surface (obtained from T, r, ΔT and σWS2),
(b–d) 150, 300 and 450 m, respectively (obtained from
sodar-derived range-corrected SNR and σ) and
(e–g) 900, 1500 and 2100 m, respectively (obtained from
profiler-derived range-corrected SNR and σ).
A sensitivity analysis is carried out to know the impact of the above chosen
thresholds on Transsunset as obtained by different state variables.
The chosen thresholds are varied by ±20 % in steps of 10 % and
the mean Transsunset as obtained by different state variables is
estimated at different altitudes. The mean Transsunset as a
function of altitude is plotted in Fig. 4, which clearly shows that the
important results do not change much, even if we vary the thresholds by
±20 %. For instance, the mean Transsunset does not change
much with the variation of thresholds. Also, the height dependence of
Transsunset is strikingly apparent with all used thresholds. It
suggests that the observed variability in Transsunset, like
top-to-bottom evolution, is not an artefact arising due to the chosen
thresholds. Regarding the usability of these thresholds at other sites, it
appears (from Fig. 4) that they possibly can be used at other tropical sites,
which are in similar climatic conditions as Gadanki region. Although we
expect similar variations in most of the state parameters at mid- and
high-latitudes, the magnitude of variation could be different because of the
differences in the solar zenith angle and rate of reduction of solar
radiation during the transition. Therefore, some tuning of thresholds may be
required at different latitudes.
Variation of mean Transsunset as obtained by different
state variables for different thresholds, depicting the sensitivity of
thresholds used in the present study to the start time of the transition.
Seasonal variation in the start time of the transition
Gadanki experiences different seasonal patterns: very hot and dry summer, hot
and rainy southwest monsoon, cool and rainy northeast monsoon and cool
and dry winter. These seasonal factors (solar exposure, synoptic flow, soil
condition, etc.) will have a different impact on the ABL, in general, and
transitions, in particular. Therefore, the distributions of
Transsunset for different seasons (Fig. 5) have been studied to
understand the impact of the above factors on the start time of the
transition. Figure 5a–d reveals that the order in which the surface state
variables show the transition remain nearly the same (the monsoon season is
an exception), but their occurrence time with reference to the sunset varies
considerably. Although reduced compared to the total data (Fig. 3), the
distribution, representing the variability within the season, of the
transition start time for each state variable is quite wide. The
Transsunset distribution for T shows a consistent pattern
regardless of the season, with small variability within the season, and the
transition starts 80–100 min prior to the time of sunset. Nevertheless, it
exhibits a clear seasonal variation with dry seasons (winter and summer)
showing the transition early (∼ 110 min prior to the sunset time)
compared to rainy seasons (80 min prior to the sunset time). The
distributions for other state variables also show some seasonal variation,
with warm seasons showing the transition a little earlier than cold seasons.
But, their distributions are much wider than the observed weak seasonal
variation. Among all state variables, the Transsunset distribution
for r shows not only large seasonal variability, but also a wide
distribution, indicating the highly variable nature of the r jump (i.e.,
starts at different timings with reference to the sunset).
The distributions of Transsunset as obtained by different
surface state variables for (a) winter (b) summer,
(c) southwest monsoon and (d) northeast monsoon,
depicting the seasonal variability in the start time of the transition. The
distributions for Transsunset as obtained by sodar-derived
range-corrected SNR and σ at 300 m for (e) winter,
(g) summer, (i) southwest monsoon and
(k) northeast monsoon, respectively. (f), (h),
(j) and (l) are the same as (e), (g),
(i) and (k), except for profiler-derived range-corrected
SNR and σ at 1500 m.
Two representative heights, 300 m from the sodar and 1500 m from the wind
profiler, are chosen to study the seasonal variation in the transition start
time aloft (Fig. 5e–l). Like in Fig. 3, there is not much difference in the
start time of transition by SNR and σ in any season and at any
particular altitude. Two observations are strikingly apparent from Fig. 5.
Both profiler- and sodar-derived start times of transition show some
seasonal variation with delayed transition during the northeast monsoon,
consistent with the seasonal variation at the surface.
Irrespective of
the season, the height dependency in the transition start time is intact.
Both these issues are discussed in detail in Sect. 4.
Discussion
The four major questions related to the start time of transition that the
paper tries to answer are (i) which state variable better identifies it,
(ii) does it exhibit any seasonal variation, (iii) does it show any height
dependency, and (iv) which physical mechanism is responsible for the observed
height variation of Transsunset?
Among all state variables, the decrease in temperature at the surface
and SNR aloft are strikingly apparent in all case studies, which makes them
ideal for identifying the start time of the AT. Furthermore, the
distributions of Transsunset for T and SNR are somewhat
consistent and narrower than that for other state variables. Although several
earlier studies employed reversal of sign in surface heat flux as a criterion
for transition (Lothon et al., 2014, and references therein), it is now well
known that such a reversal does not always occur during the transition (Busse
and Knupp, 2012). The formation of an inversion depends on several other
factors and therefore the formation of inversion alone cannot be used to
define the transition. A few studies used deceleration of low-level wind as a
criterion for identifying the transition (Mahrt, 1981). The above criterion
works well in the lower portion of ABL, but fails above the nocturnal
boundary layer, where the wind accelerates in the frictionless fluid.
Therefore, T at the surface and SNR aloft can be used to identify the start
time of the transition, as also suggested by Edwards et al. (2006).
The start time of the transition as defined by different state
variables shows some seasonal variation, with late transitions during the
northeast monsoon season. Though Gadanki receives 55 % of the annual
rainfall in the southwest monsoon, raising instantaneous soil moisture
levels, the high insolation and temperatures immediately consume the soil
moisture for latent heating. On the other hand, this region also gets a good
amount of rainfall during the cool northeast monsoon (Rao et al., 2009).
The soil moisture levels, therefore, remain high in this season. It is known
from earlier studies that the abundance of soil moisture not only produces
shallow ABL, but also delays the growth of the ABL (Sandeep et al., 2014). It
appears from present observations that not only the growth but also the
descent (or transition) is getting delayed due to the excess soil moisture.
The total and seasonal distributions of Transsunset for
different state variables at the surface and aloft clearly show the height
dependency in the start time of transition, following a top-to-bottom
evolution. It is known from the literature that there exists an apparent
contradiction between those who think the transition starts in the afternoon
at high levels (Angevine, 2008) and others who believe the AT occurs around
the sunset and follows a bottom-up evolution. The present study supports the
former view, as similar evolution is seen in total and seasonal plots
(Figs. 3 and 5). During the AT, when the surface buoyancy flux decreases
toward zero, the influence of other competing processes like advection, and
entrainment becomes relatively more important (Bosveld et al., 2014).
Therefore an attempt has been made to estimate these fluxes (buoyancy and
entrainment) to understand their roles in the observed height dependency in
transition start time.
The ratio between the vertical kinematic eddy heat flux at the top of the ABL
and kinematic eddy heat flux at the surface (entrainment ratio) (Sun and
Wang, 2008), as given below, therefore, becomes a fundamental and decisive
parameter.
AR=-w|Θ|zi‾w|Θ|s‾
The heat flux at the top of ABL (or entrainment flux) is estimated following
Angevine (1999). The entrainment can occur due to any or all of these
factors: (1) when there is a shift in the ABL height (2) due to wind shear at
the surface, (3) due to wind shear at the top of the ABL and (4) advection.
-(w|Θ|‾)zi=A0+(A2u∗2u+A3Δuh3).(θvo/gd1)+(U∂T∂x+V∂T∂y)
where u∗ is the friction velocity, u the surface horizontal
velocity (8 m in our case), Δuh the wind shear at the top of ABL,
g the acceleration due to gravity, θvo the virtual potential
temperature at the surface, d1 is the depth of entrainment zone and
A2 and A3 are empirical constants, A2=0.005 and A3=0.01
(Stull, 1976). For the estimation of advection (last term in Eq. 2), the
temperature (T), horizontal distance in zonal and meridional planes
(∂x and ∂y, respectively, and is equal to 0.5∘)
and zonal (U) and meridional (V) wind velocities near the top of ABL are
taken from ECMWF Interim Reanalysis data (Dee et al., 2011). A0 is the
entrainment flux in the absence of any mechanical term contribution and is
expressed as weΔΘ,we is the entrainment
velocity and is estimated as follows.
we=dzidt-w‾
where w¯ is the average vertical velocity at the top of the ABL and
Δθ the vertical gradient in θv at the top of the
ABL. As seen above, the timescales and space scales of different entrainment
processes cover a wide range, which makes it difficult to measure or model
accurately (Angevine, 1999). Although it is possible to quantify the
entrainment flux from the heat budget equation (Eq. 2), the uncertainties in
the basic parameters (for instance, those in the advection term and w)
hamper the accuracy of the flux. Therefore, as also pointed out by
Angevine (1999), these numbers need to be considered as the “best available
estimates”.
Diurnal variation of profiler attributes
(a) range-corrected SNR (b)w and (c)σ
on 22 July 2011, illustrating the evolution of the ABL and afternoon
transition. (d) The vertical variation of radiosonde-derived
θv at ∼ 3 h intervals. The solid symbols
in (a) indicate the height of the ABL.
It is clear from the above equations that profiles of meteorological
parameters such as T, RH/r and w are essential to estimate the
entrainment ratio. Though w can be obtained continuously from the wind
profiler, continuous measurement of T and RH/r at the top of the
ABL is a difficult task. We, therefore, considered two 3-day campaign data
(one each from the southwest monsoon and winter), wherein radiosonde
ascents were made once in ∼ 3 h, for a detailed study (data set 2).
Figure 6a–c shows the time–height variation of SNR, w and σ on
22 July 2011, depicting the typical diurnal evolution of the ABL during the
campaign period. The θv profiles during the morning–evening
(at 08:24, 11:54, 14:25 and 17:15 IST) period are shown in Fig. 6d to depict
the height of the ABL (and also the gradients in θv at the top
of the ABL). Clearly, the height of the ABL as obtained by the profiler
(shown with dots on the SNR plot) and radiosonde (the gradient in
θv profile) corresponds well. The agreement between them is
also good in the diurnal variation, with both the measurements showing a
shallow ABL in the morning and evening transition periods and a deep ABL
during the day, when the ABL is convectively active.
The start time of AT as seen by different state variables at the surface and
aloft on all days during the two campaigns is shown in Fig. 7a and b. It
clearly reiterates the height dependency of the start time of AT seen in
Figs. 2–5; i.e., the start time of AT observed by the profiler precedes
surface state variables on all days and in both seasons. Though the same
pattern is seen on all days, the time at which the transition starts varies
considerably from day to day.
The entrainment flux at the top of ABL is estimated by combining the
measurements of radiosonde (Δθ, d1), profiler (w,
Δuh), MBLM (u, θvo) and a meteorological flux
tower (u∗) with ECMWF interim data (advection term). The sensible
heat flux and u∗ at the surface required to quantify the entrainment
ratio (Eq. 1) are estimated following the eddy covariance method by using
20 Hz resolution ultrasonic anemometer measurements at 8 m level. These
fluxes are evaluated at 30 min resolution.
The start time of the AT with reference to the time of sunset as
obtained by different state variables at the surface and aloft during
(a) 17–19 January 2011 and (b) 21–23 July 2011.
Sensible and entrainment fluxes (left axis) and entrainment ratio
(right axis) estimated at ∼ 3 h intervals during
(a) 17–19 January 2011 and (b) 21–23 July 2011,
indicating the forcings on the ABL from the bottom and top.
Figure 8a and b shows the sensible and entrainment fluxes at ∼ 3 h
resolution during the day, depicting the forcing on the ABL from bottom and
top. The sensible heat flux varies considerably during the day, with fluxes
varying from 0.15–0.25 K ms-1 around noon (∼ 11:00 and
∼ 14:00 IST) to 0.02–0.07 K ms-1 during the morning and
evening transitions (∼ 08:00 and ∼ 17:00 IST). On the other
hand, the entrainment flux neither changes drastically during the day nor
shows a clear diurnal cycle (compared to sensible heat flux). The magnitude
of entrainment flux depends mostly on the first term in Eq. (2), while the
shear (2 and 3 terms in Eq. 2) contributes very little to the total
entrainment flux (not shown). Since the buoyancy flux changes considerably,
the entrainment ratio varies significantly during the course of the day. The
entrainment ratio increases to 0.5–1.1 during the morning and evening
transitions. Therefore, it is very clear from these observations that the
forcing from the top (i.e., entrainment flux) becomes very important, when
the buoyancy flux is weak (i.e., during the transitions and night). A few
earlier studies also underscored the importance of buoyancy flux in altering
the structure of the ABL. The entrainment not only modifies the top of the
ABL but also impacts the entire depth of the ABL (Lohou et al., 2010).
Caughey and Kaimal (1977), have shown experimentally that the heat flux
descents suddenly during the transition, approximately an hour before the
sunset, and the reversal of heat flux (from positive to negative) first
occurs at higher altitudes and then propagates downwards to the surface,
indicating the importance of entrainment heat flux in the top-to-bottom
evolution of the transition. Also, with continuous waning of sensible heat
flux during the AT, both the vertical extent and strength of thermals (can be
seen in Figs. 1 and 6) decrease monotonously. At the same time, the surface
forcing (heating) remains good enough to maintain the turbulence close to the
surface and therefore does not show the signature of transition, but delays
it at the surface (Angevine, 2008).
Conclusions
This study presents a comprehensive view of the AT in terms of understanding
the variability of different state variables using a suite of in
situ and remote sensing measurements at Gadanki. The study aims to
address the following issues related to the start time of AT with a unique
and statistically robust data set (∼ 3 years). Which parameter first
shows the signature of transition at the surface and aloft? Which parameter
better defines or identifies it? How does it vary with altitude and season?
Which physical mechanism explains the observed vertical variation of
transition?
Among the surface state variables, the signature of transition is first
seen in σWS2 and T data, both of which start decreasing
monotonically ∼ 100 min prior to the time of sunset. The r increase
is the last signature of transition, while the reversal of ΔT
variation from positive to negative falls in between these extremes. Aloft,
both SNR and σ identify the start of the AT at the same time,
120–160 min prior to the time of sunset, depending on the height
considered. The observed mean start time of the AT (2 h prior to the
sunset), obtained from SNR and σ variations, matches well with that
obtained by Mahrt (1981), who used horizontal wind reduction for identifying
the transition.
At the surface, the start time of AT can be discerned more easily from
variations of T than from that of σWS2, r and ΔT. While σWS2 and ΔT variations show large
modulations with time, r variation is ambiguous at times. Also, the
temperature reduction is more consistent with relatively narrow distribution
and occurs always before the sunset. Aloft, SNR variation is robust in
identifying the transition compared to ambiguous variations in horizontal
wind velocity (decreases at lower altitudes and increases at higher
altitudes).
The start time of the AT as defined by different state variables shows some
seasonal variation, with delayed transitions during the northeast monsoon
at the surface and aloft. Though there is some seasonal variation in the
start time of the AT relative to sunset time, the order in which the
signature of the AT is seen in different state variables (first in T, and
σWS2 followed by ΔT and r) remained nearly the
same in all seasons.
Interestingly, the start time of the AT exhibits a clear height
dependency; i.e., the signature of the transition is seen first in profiler
attributes (∼ 160 min) followed by sodar attributes (∼ 120 min)
and finally in surface state variables (∼ 100 min), suggesting that
the transition follows a top-to-bottom evolution (Angevine, 2008). The fact
that the first signatures of the transition are seen at higher altitudes by
profiler/sodars than at the surface suggests that forces other than the
buoyancy could also play an important role during the transition. With
continuous waning of sensible heat flux (and surface forcing) during the AT,
both the vertical extent and the strength of thermals decrease steadily (as
seen in Figs. 1 and 6), triggering the descent of the ABL or the transition.
However, the surface heating is good enough to maintain the state variables
and delay the decrease in T and σWS2 (considered to be
the signatures of the transition). Furthermore, the impact of forcings from
the top and bottom on the ABL is studied by quantifying the sensible and
entrainment fluxes, using a flux tower and profiler-radiosonde measurements,
respectively. Though the sensible heat flux varied significantly during the
day, the entrainment flux remained nearly the same throughout the day. The
entrainment ratio increases considerably during the morning and evening
transitional periods, primarily due to the weak sensible heat flux.
Therefore, the entrainment flux appears to be playing a major role during the
transition period (and in the night) during which the sensible heat flux
continuously weakens.
Acknowledgements
The authors would like to thank M. Venkat Ratnam for providing the GPS
radiosonde used in the present study (experiments are conducted under the
special campaign of tropical tropopause dynamics (TTD) as a part of the
CAWSES-Phase II program,
India). Edited by: E. Pardyjak
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