Evaluation of the Boundary Layer Morning Transition Using the CL-31 Ceilometer Signals
Paulina SOKÓŁ1, Iwona S. STACHLEWSKA1, Ioana UNGUREANU2 and Sabina STEFAN2
1Institute of Geophysics, Faculty of Physics, University of Warsaw, Warsaw, Poland e-mail: [email protected] (corresponding author) 2University of Bucharest, Faculty of Physics, Bucharest, Romania
A b s t r a c t
The morning transition of the atmospheric boundary layer from nighttime to daytime conditions was investigated using the Vaisala’s CL-31 ceilometer, located at Magurele, Romania (44.35°N, 26.03°E). Based on the 5-days backward trajectories, we rejected those measure-ments which were related to the intrusions of long-range transported par-ticles. In the several discussed cases, which are representative for the morning transition in spring and summer seasons over Magurele, the in-creasing depth of the boundary layer related to the local aerosol load was well discernible. The dynamic change of its depth was estimated with er-rors using a simple method based on finding the minimum of the first de-rivative of the ceilometer signal. In the summer, the increase of the boundary layer depth due to the morning transition from the nighttime to daytime conditions starts on average of about 80 min earlier and the growth rate of this depth is 143 ± 6 m/h and about 37% slower than in the spring case.
1. INTRODUCTION The structure of the boundary layer during the day and at night is signifi-cantly different. In the morning, due to heating of the Earth’s surface by the
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Sun and the changes in heat fluxes, a stable nocturnal layer of about 100 m depth is evolving into a turbulent mixing layer of 1-2.5 km height, where as a result of this mixing the potential temperature, the relative humidity and the concentration of aerosols are almost constant with height (Wallace and Hobbs 2006). The phenomenon of the increase of the boundary layer depth from the nighttime to the daytime conditions, called a morning transition, is a good example of dynamic changes occurring in the lower troposphere.
Lidar techniques allow for remote sensing of a vertical structure of the atmosphere with an exceptional spatial and temporal accuracy. Application of this technique to the regular measurements has a large impact on our un-derstanding of various processes occurring in the atmosphere, e.g., mixing and accumulation of pollutants, formation of clouds and transport of aero-sols. Lidar observations allow also for determination of the boundary layer (mainly daytime mixing layer depth), which is a vital parameter for use in a weather forecasting and climate models. Inexpensive and easy-to-use ceil-ometers, based on a lidar principle, are widely used for regular remote meas-urements of cloud-bases present at the low, medium, and high level. They can be useful also to study the dynamic processes occurring in the boundary layer over night and day, as will be shown in this paper.
The main difference between a ceilometer and an elastic lidar is the type of laser. A typical elastic lidar uses a solid-state laser (e.g., Nd-YAG operat-ing at an ultraviolet, visible and/or infrared wavelength) with a high laser pulse energy (in the order of 10–1 J) and a relatively low pulse emission fre-quency (a few Hz). In ceilometers the diode lasers are commonly used. Due to a low laser pulse energy (order of magnitude of 10–6 J), ceilometer signal is characterized by a relatively low signal-to-noise ratio (SNR), which is several times smaller than for a typical elastic lidar, e.g., as it was shown in the SNR comparison study of the PollyXT lidar signals versus the Jenoptik’s CHM_15kx ceilometer signals (Heese et al. 2010). The high emission fre-quency of laser pulses of the ceilometer (in the order of a few kHz) does not improve the SNR sufficiently. Therefore, the ceilometers are most common-ly used to record information about the height of clouds-base, as an altitude at which cloud occurs can be easily determined due to the high optical densi-ty of clouds (the observed signal is several orders of magnitude higher than for an atmosphere surrounding the cloud). In contrast to the lidars, which are used to perform series of short-period measurements (due to the need to sup-port lidars by qualified operators and the short lifetime of expensive flash-lamps), ceilometers are designated to work continuously. This creates an is-sue of acquisition of vast amounts of ceilometer signals. Therefore, usually only the data of the height of clouds-base are stored. However, when the en-tire backscatter signals are stored, they have a potential to be evaluated in a
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similar manner and by using a similar methods as the elastic lidar signals (Wiegner and Geiβ 2012, Stachlewska et al. 2012, Belegante et al. 2014).
An additional advantage of some ceilometers, which is explored also in this study, is due to their low full-overlap height between the divergent laser beam emitted into the atmosphere and the full-field of view of the ceilome-ter’s receiver. In lidars, this full-overlap is often accomplished at the range of hundreds of meters or even more than 1 km from the instrument (Tsaknakis et al. 2011). In ceilometers, full-overlap can be fulfilled at about 100 m or even almost immediately above the instrument, especially in a sin-gle-lens design ceilometers (Münkel et al. 2007).
Since the price of ceilometers is much lower than the price of lidars, they are commonly used by meteorological institutions and airports. The low price along with easy and unattended operation makes ceilometers attractive also for performing measurements at the rural regions and in the developing countries, especially if one takes the advantage of the additional qualities stored in the ceilometer backscatter signals.
This paper shows a feasibility of using ceilometer’s backscatter signals for a detailed evaluation of the changes occurring in the atmospheric bound-ary layer during its transition from the nighttime to daytime conditions, a challenging issue for any lidar/ceilometer.
2. OBSERVATIONS WITH CL-31 CEILOMETER OVER MAGURELE, ROMANIA
For this study we used measurements of the Vaisala’s CL-31 ceilometer. This instrument is equipped with the InGaAs diode laser, which emits pulses at a wavelength of 910 nm, with a low pulse energy of 1.2 × 10–6 J and a high emission frequency of 10 kHz. It allows to obtain backscattered vertical profile of the atmospheric signal with temporal resolution of 2 s and spatial resolution of 5 m. The entire range of the signal is limited to the height of 7.7 km. The received signals are automatically range and overlap corrected. According to the instruction manual (Vaisala User’s Guide 2009), the full-overlap is achieved already at 10 m. However, Martucci et al. (2010) reports the full-overlap of this system as being completed only at 70 m, which is also in the case of our instrument.
At the Faculty of Physics of the University of Bucharest, the CL-31 ceil-ometer located in Magurele, the SSW district of Bucharest, Romania, is used to perform regular measurements of cloud-base height and of the attenuated backscattered signals (Ungureanu et al. 2010). From the latter data we se-lected several measurement cases with an aim to infer dynamic changes which were occurring in the atmospheric boundary layer under the morning transition conditions. Romania is exposed to many pathways of aerosol
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Fig. 1. The temporal evolution of the range-corrected backscattered signal recorded by Vaisala’s CL-31 ceilometer on 8 August 2011 with spatial resolution of Δh = 10 m and temporal resolution if Δt = 5 min. The black points correspond to the boundary layer heights estimated using radiosonde (RS), where RL denotes the height of the residual layer at 812 m, NL the nocturnal layer at 155 m, and ML the mixing layer at 815 m.
intrusions (Nemuc et al. 2014). As we wanted to focus our study on the changes of the aerosols of local origin only, we rejected the days when there was evidence of a long-range aerosol transport based on the results of the 5-days backward trajectories analyses at 3 levels (200 m, 800 m, and 2 km) obtained by using the simulations of the HYbrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model (Draxler and Rolph 2012). Addi-tionally, our focus was on the estimation of the boundary layer growth rates for the spring versus summer morning transition. In winter, the boundary layer at Magurele is generally very low and often below or very close to the range of completed overlap. In autumn, vast amount of measurements con-tained clouds and precipitation, so that the morning transition phenomenon was hard to observe using this instrument. Thus, we selected those days where this phenomenon was well visible, as, e.g., on 7 August 2011, shown in Fig. 1.
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3. DETERMINATION OF BOUNDARY LAYER HEIGHT
A decrease of humidity and aerosol concentration occurring at the height of the boundary layer, which is formed at the interface between the mixed layer and the free troposphere, results in a rapid decrease of the backscatter signal measured with a lidar or ceilometer (Fig. 2a, boundary layer height is at about 200 m). There are many methods to determine the boundary layer height HBL using the lidar signals, which rely on this decrease of the meas-ured signal. Three most commonly used methods (Sicard et al. 2006) are the following: (i) the strongest negative gradient method, which involves finding an altitude at which the local minimum of the first derivative of the range corrected signal is present; (ii) the inflection point method, which determines the boundary layer height defined as the altitude of the minimum of the se-cond derivative of the range corrected signal; and (iii) the method based on finding the minimum of the derivative of the logarithm of the range correct-ed signal. Other approaches use a simple signal threshold (Boers and Eloranta 1986) or wavelet method (Steyn et al. 1999), and its modifications (Cohn and Angevine 2000).
Because of a relatively low SNR, not all of those methods can be applied to ceilometer measurements (Steyn et al. 1999). The strongest negative gra-dient method was applied to ceilometers data by Münkel and Roininen (2008), Tsaknakis et al. (2011), Stachlewska et al. (2012), while wavelet method was used by Münkel et al. (2007). The latter method is also used by the STRAT algorithm (Morille et al. 2007) developed in the frame of the EARLINET-ACTRIS activities.
In this paper the strongest negative gradient method was applied due to the fact that the presence of negative values in the analyzed data excluded the use of the logarithm derivative method, and a low SNR ruled out the use of the second derivative method as well as the wavelet method. To apply the chosen method, a range [H1 : H2] wherein the fastest local decrease of the measured signal appeared was specified for each ceilometer profile relying on the overlap and range corrected signal and its gradient. The boundary lay-er height HBL was assumed to be in the middle of this range, HBL = (H1 + H2)/2, and the uncertainty of such estimation was defined as ΔHBL = (H1 – H2)/2. An example of the application of this approach on a single sig-nal profile and its gradient is shown in Fig. 2. For each profile analyzed in this paper, the height HBL was determined individually. A global minimum of the signal gradient, shown in Fig. 2, occurs at an altitude of 40 m; however, the negative gradient associated with the boundary layer height occurs in fact at about 205 m. One should stress out that for the signals analyzed in this paper an incomplete overlap of the ceilometer was below the height of 70 m. Therefore, the boundary layer height HBL is defined by a local minimum of
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Fig. 2. The determination of the boundary layer height from the CL-31 ceilometer signals on 8 August 2011. The range corrected and overlap corrected signal (a) and its gradient (b) were averaged with time resolution of Δt = 16 s, and spatial resolu-tion of Δh = 10 m. The symbol HBL denotes the boundary layer height and ΔHBL de-notes the uncertainty of the estimation of this value.
the signal gradient above these it. Note that in Fig. 3 we show that the global gradient obtained at 40 m has not a physical meaning and it is an artifact, which can be found in the entire data base of the CL-31 signals over Magu-rele. Wiegner et al. (2006) communicated similar issue for the Vaisala’s LD-40 ceilometer signals.
The temporal and spatial averaging is used in order to reduce the noise of ceilometer signals. An optimization of averaging parameters is a non-trivial problem. New signal averaging techniques for recovering profiles with SNR > 1 almost up to the tropopause level were developed and applied to the custom designed version of one of the first Jenoptik’s CHM15k ceilome-ter by Stachlewska et al. (2012). Too long averaging intervals cause loss of information about dynamics of the processes and short-term changes in the atmosphere, such as inflow of local pollutants. Establishing of an appro-priate averaging time should therefore take into account the stability of
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atmospheric conditions. It seems that the 30 min averaging is most often used to determine the height of mixing layer during daytime (e.g., Heese et al. 2010). However, during the early morning, the boundary layer height can increase up to dozens of meters in 30 min. This shift of HBL causes blur and extension of range [H1 : H2] and, hence, an increase of ΔHBL. Fortunately, the high pulse emission frequency of the CL-31 ceilometer allows for short-time integration of signals, for even every few minutes. Thus, 5 min averaging was applied to analyze the data, what allowed for an optimal noise reduction while keeping the above-mentioned blur effect as minimal as possible.
An analysis of these individual profiles shows some differences in the heights determined by the strongest negative gradient method shown in Fig. 2 and the heights estimated based on a temporal evolution of the ceil-ometer signals (Fig. 1).
The boundary layer height can also be estimated from radiosounding profiles (Seidel et al. 2010). We estimated the boundary layer heights by using radiosondes launched at middays and midnights UTC (i.e., 3:00 a.m./p.m. LT) from the meteorological station 15420 LRBS operated by the National Institute of Hydrology and Water Management (INNH) in Bucha-rest (44.5°N, 26.1°E). This station is located about 20 km N from Magurele, on the other side of the Bucharest city. We considered this distance to be close enough to expect similar results for a coarse validation purpose. The values of the boundary layer depth were assessed based on profiles of the equivalent potential temperature and the absolute humidity. They remain in a general agreement (within 20%) with the values obtained from ceilometer profiles. An example of the comparison of boundary layer heights obtained from radiosonde along with the ceilometer signals are given in Fig. 1. Due to the fact that the morning transition occurs usually between sunrise and mid-day LT, the data from radiosonde launches are out of the time-window of our interest. In this sense, they can be used only as coarse validates.
4. MORNING TRANSITION – SPRING VERSUS SUMMER The measurement of an example of the morning transition one of the days under investigation is depicted in Fig. 1. Almost every day the measurements were performed from 6:00 to 12:00 LT, despite the case on 8 August 2011 where the data were available only from 00:00 to 10:30 LT. During the measurements, the ceilometer worked in one of the two configurations. The first one, called further the cloud-base configuration is a setting optimized to observe clouds with a time resolution of Δt = 2 s and a spatial resolution of Δh = 20 m. The second one, called further the aerosol configuration is a setting optimized to observe aerosol structures with a time resolution of Δt = 16 s and a spatial resolution of Δh = 10 m.
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In Figure 1, the temporal evolution of the range and overlap corrected signals obtained on 7 August 2011 is shown. On this day, between 00:00 and 8:00 LT the stable nocturnal boundary layer reached an altitude of about 150 m. Above it, the residual layer with aerosols remaining from the previ-ous day is well visible. The morning transition started shortly after 8:00 LT and after 2 h of an intensive increase of the boundary layer it reached a depth of about 400 m. Note that an application of the range correction (which liter-ally means the multiplication of the received signal by the squared vector of height) results in a well visible, gradual increase of the level of noise with height. The boundary layer heights obtained from the radiosonde ascends are marked in Fig.1 by the black dots. According to the radiosonde profiles at 3:00 a.m. LT, the residual layer reached depths above 812 m and the stable nocturnal layer depth was over 155 m. The height of the well-developed mixing layer at 3:00 p.m. LT was estimated at above 815 m.
A regular signal deviation is visible in all measurements collected at Magurele. An example of this problem is depicted in Fig. 3, where the aver-age profile of all signals measured during the entire day on 22 March 2010, 7 July 2010, and 7 August 2011 are shown. Note that the same distortions in the signal are visible in Fig. 1. Every few hundred meters, at heights of about 490 and 670 m (marked with arrows in Fig. 3), an increase of a signal values of about 10 sr–1m–1 occurs. Note also the artifact at the height of 40 m (com-pare with Fig. 2). This problem occurs at very similar altitudes in the entire data set spanned over 3 years of observations. Similar artifacts were ob-served in the case of measurements performed with the CL-31 ceilometer at Ny-Ålesund, Spitsbergen, by Ritter and Neuber (2012). Most probably, these deviations have an origin in electronic distortions associated with the auto-matic data processing chain used internally by the instrument and not with the actual values of the received signal.
The boundary layer height during the morning transition duration was determined every 30 min from 6:00 to 12:00 LT using profiles averaged over 5 minutes. The determined heights are shown in Fig. 4, where on the left panel the selected spring cases, and on the right panel the summer cases are depicted.
For further analysis, the concept of elapsed time was used instead of the local time. For the boundary layer heights retrived during the morning transi-tion period, the linear function hBL = a(tLT – tSUNRISE) + b was fitted using the least squares method. Two fittings ware carried out. Firstly, for each day a linear fit was performed, for which the obtained coefficients are listed in Ta-ble 1. Secondly, a seasonal linear fit for all points for the summer cases (aSUMMER = 142.6 ± 3.9; bSUMMER = –166.8 ± 15.0) and the spring cases (aSPRING = 226.48 ± 5.8; bSPRING = –400.4 ± 21.2) was done.
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Fig. 3. A few examples of the abnormalities at altitudes of 40, 490, and 670 m (arrows) present in the range and overlap corrected signals averaged over the dura-tion of the entire measurement period on particular day using CL-31 ceilometer.
The coefficient a is related to the intensity of the morning transition increase. In spring, the morning transition increase is more intensive as the slope of the fitted line is greater than in summer (aSPRING > aSUMMER). In summer, the morning transition starts about 7:50 LT. In spring it begins later (average at about 9:00 LT, however the variety of tSTART is much larger here.
It is interesting to note that the difference between the start time of morn-ing transition in spring and summer is of about 80 min and this shift cannot be only due to the difference in sunrise in both seasons, which is of about 20 min (compare Fig. 4 and Table 1), but also a result of the change of the heat fluxes. The difference Δt between sunrise time and the morning transition beginning is proportional to –b (compare Eq. 2). The relation of bSPRING < bSUMMER explains the much longer delay Δt in spring than in summer.
The growth rate of the morning transition is a complex problem. Ange-vine et al. (2001) examine the relationship between an increase of a mixing
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layer and the meteorological conditions, such as the surface temperature, humidity, heat fluxes, and wind velocity. The dynamics of weather condi-tions plays an important role here. Unfortunately, any data on the temporal changes of those physical quantities are not available for Magurele during the time of our measurements.
As shown in Table 1, the highest values of coefficient a, and hence the most intensive increase took place in spring cases, i.e., on 17 April 2010 it is almost 400 m/h. The slowest increase was observed on 7 August 2011 with a value of about 120 m/h.
The knowledge of the nocturnal boundary layer height hNIGHT allows to compute the delay between the sunrise time tSUNRISE and the time when the increase started.
Fig. 4. The boundary layer height derived during the morning transition in spring (left) and in summer (right) with marked uncertainties and fitted seasonal trends. The sunrise time is corresponding to 0 h of the elapsed time.
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NIGHT START SUNRISE( )h = a t T +b− (1)
so
NIGHTSTART SUNRISEΔ .
h bt = t t =
a−
− (2)
The delay time Δt in summer is in the range of 1 h 40 min to 2 h 30 min. In spring, the delay time varied from almost 2 h to even 3 h and 40 min. Angevine et al. (2001) obtained the delay time of 2 h 40 min; however, in contrary to our work, their methodology of finding the nocturnal layer inver-sion was set arbitrarily to 200 m above ground level.
Table 1 The coefficients a, b and their uncertainties δa, δb, obtained from fitting a linear function hBL = a(t – tSUNRISE) + b to the boundary layer heights during the morning
transition in spring and in summer cases
Date tSUNRISE hNIGHT a [m/h] δa [m/h] b [m] δb [m] Δt
22 Mar 2009 6:15 125.0 336.9 7.9 –523.4 25.0 1:54
22 Mar 2010 6:16 100.0 331.4 24.4 –1121.7 100.3 3:42
08 Aug 2011 6:10 120.0 142.5 10.5 –166.8 32.8 2:00
Explanations: The tSUNRISE is the sunrise time, hNIGHT is the nocturnal boundary layer height, and Δt is delay between the sunrise and the beginning of increase of the boundary layer in the morning.
5. CONCLUSIONS There is a variety of methods used to determine the boundary layer height from the backscatter lidar signals. Unfortunately, most of them encounter problems when they are applied for the analysis of signals with low signal-to-noise ratio, which is a characteristic feature of any ceilometer data. The plots of the temporal evolution of the backscattered ceilometer signals may be used for qualitative analysis of the structure of the boundary layer, but the quantitative analysis, especially the determination of the boundary layer height during dynamic changes of the lower troposphere, requires analysis of individual, appropriately averaged profiles. A simple, commonly used meth-od for the retrieval of the boundary layer height, which is based on finding of the local minimum in the first derivative of the Vaisala’s CL-31 ceilometer signals, was used in this paper and gave satisfactory results. Thanks to an
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analysis performed on individual profiles it was possible to obtain the boundary layer heights with low uncertainties (10-100 m) for the high time resolution of 5 min for a single profile. The measurements with CL-31 ceil-ometer at Magurele allowed for a study of the structure and the dynamic processes which were taking place in the boundary layer, during the morning transition of the boundary layer from the nighttime to the daytime condi-tions. This phenomenon, observed for spring and summer seasons, which were inter-compared, shows that the morning transition in the spring case starts over an hour later than in summer case. The increase of the boundary layer height in spring is also much more intense. The delay between the sun-rise and the beginning of the increase of boundary layer depth in the morning is not only longer but it is also much more spanned in time.
Acknowledgments . The trajectories discussed in this paper were calculated using the HYSPLIT model available on-line via http://www.arl.noaa.gov developed at the NOAA Air Resources Laboratory (ARL).
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Received 1 October 2012 Received in revised form 1 February 2013
