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Estimate of boundary-layer depth in Nanjing city using aerosol lidar data during 2016–2017 winter
Atmospheric Environment 205 (2019) 67–77
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Technical note
Estimate of boundary-layer depth in Nanjing city using aerosol lidar data during 2016–2017 winter
T
Sihui Fana, Zhiqiu Gaoa,b,∗, John Kalogirosc, Yubin Lia, Jian Yina, Xin Lia a Climate and Weather Disasters Collaborative Innovation Center, Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing, 210044, China b State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China c Institute of Environmental Research and Sustainable Development, National Observatory of Athens, Athens, 11810, Greece
A R T I C LE I N FO
A B S T R A C T
Keywords: Inter-comparison PBL depth Lidar Retrieval algorithms Air pollution
The planetary boundary-layer (PBL) structure was investigated using observations from single aerosol lidar, eddy covariance (EC) system, and automatic meteorological station (AWS) in the north of Nanjing city during an air pollution episode in 2016–2017 winter. Based on seven days’ observations under clear to polluted day, we present the temporal variations of the aerosol extinction profiles observed by lidar, and then inter-compare PBL depth retrieved from individual gradient methods. The results show that the gradient method (GM) generated the lowest PBL depth. In contrast to the cubic root gradient method (CRGM), which determined PBL depth ranging from 172 m to 1575 m during the observation period, the logarithm gradient method (LGM) and normalized gradient method (NGM) generated similar results and both tended to overestimate PBL depth on polluted days. The CRGM performed better than LGM and NGM in case of multiple backscatter layers and could detect low level layers, while the GM was biased at low heights probably due to the effect of lidar overlap function. Based on these measurements, the evolution of boundary layer structures and PBL depth over clean days and polluted days were compared. The results show that (1) on clean days, the strong surface turbulence exchange make the PBL depth fully developed and the PBL depth had obvious characteristics of diurnal variation; the maximum depth of PBL was 1560 m for CRGM; and (2) on polluted days, the high pressure system and lower wind was favorable to the accumulation of air pollutants, and thus generating less turbulence by reducing surface radiation. These conditions on polluted days led to smaller PBL depth than those on clean days, and the maximum depth of PBL was 660 m for CRGM. Besides, the diurnal variation of PBL depth on polluted days was weaker than those on clean days.
1. Introduction
determining the PBL depth (Flamant et al., 1997). The lidar has been used in many field experiments to detect the PBL depth continuously (Cohn and Angevine, 2000; Flamant et al., 1997). Strawbridge and Snyder (2004) used the advantage of a scanning lidar instrument to determine the PBL depth over Pacific during 2001. Regular aerosol extinction and backscatter measurements using a UV Raman Lidar have been performed for almost 3 years in Hamburg in the frame of the German Lidar Network (Matthias and Bösenberg, 2002). In China, Qiu et al. (2003) carried out detection experiment of high cloud and aerosol in the troposphere based on the multi-wavelength lidar. Mao et al. (2006, 2007) determined the vertical distribution and changes of urban boundary layer height using lidar and gave a preliminary analysis of the ground meteorological environment impacts on
The planetary boundary-layer (PBL) depth illustrates the relationships between air pollution intensity, duration, and scope (Yang et al., 2017). The evolution of boundary-layer structure has significant impacts on the diffusion, transmission and disappearance of pollutants in the lower atmosphere (Quan et al., 2013; Tie et al., 2007). It is observed that an increase of air pollutants is often associated with a shallow PBL depth, while a decrease of pollutants is accompanied by obvious uplift of the PBL depth (Quan et al., 2014). Accordingly, the measurements of PBL depth are crucial to the study of air pollution. The superior spatial and temporal resolutions make aerosol lidar technique one of the most suitable systems for analyzing the boundary layer structure and
∗ Corresponding author. Climate and Weather Disasters Collaborative Innovation Center, Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing, 210044, China. E-mail addresses: [email protected] (S. Fan), [email protected] (Z. Gao), [email protected] (J. Kalogiros).
https://doi.org/10.1016/j.atmosenv.2019.02.022 Received 7 October 2018; Received in revised form 18 January 2019; Accepted 17 February 2019 Available online 23 February 2019 1352-2310/ © 2019 Elsevier Ltd. All rights reserved.
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air pollution episode. An aerosol Lidar (marked ‘A’ in Fig. 1d), EC system (marked ‘B’ in Fig. 1d), and an automatic meteorological station (AWS, marked ‘C’ in Fig. 1d) were used at this experiment.
the diurnal variation of the boundary layer. Wang et al. (2008) studied atmospheric boundary layer structure characteristics during summer in Beijing, as well as the extinction characteristics of aerosols in the atmospheric boundary layer. However, few researches focused on the PBL under polluted weather conditions based on lidar despite the significant impact of PBL on air pollution process evolution. Consequently, this paper deals with lidar observations of atmospheric layers to sample the PBL structure under clear to polluted days over Nanjing. The paper is organized as follows: Section 2 describes the materials, including observational site, meteorological measurement systems, data processing and existing algorithms; Section 3 presents the main results and discussions; and Section 4 summarizes and concludes this paper.
2.2. Meteorological measurement systems 2.2.1. Lidar observation system A Raymetrics elastic lidar model LB110-ESS-D200 was installed on the rooftop of Beichen building during the experiment. It can operate 24 h per day in an unattended mode under no rain conditions. It emits laser pulses at 355 nm wavelength with a repetition rate of 20 Hz. For this study the raw temporal resolution of the retrieved aerosol profiles was set at 5 min, while a spatial resolution of 7.5 m was used, but only for altitudes higher than around 150 m due to an incomplete overlap between the field of telescope view and the laser beam. Lidar observations were originally represented by the range-squared-corrected signal (RSCS). Apart from this, the aerosol extinction profiles presented in our work were retrieved using the improved Klett's stable inversion algorithm. Usually, k is in the range 0.67–1.3. In general, the value k = 1 was used in the computations and a lidar ratio = 20 in this work. This algorithm is based on a far-end backward iterative technique, taking into account also the atmospheric molecular contribution as described by Klett (1985). Details on the lidar instruments and various
2. Measurements 2.1. Observation site Nanjing (32.04°N, 118.78°E), as the Yangtze River Delta Regional Economic Development Center, is located on the southwest border of Jiangsu province. An intensive observation campaign was carried out from December 28, 2016 to January 3, 2017 on the campus of Nanjing University of Information Science and Technology (NUIST) during an
Fig. 1. Site description and observation systems. (a) Map of eastern China; the location of the experimental site is marked by a red star; (b) 70-m tower; (c) Raymetrics elastic lidar; and (d) the experimental site. ‘A’ is the building on whose top Raymetrics elastic lidar was placed; ‘B’ is the field in which the eddy covariance (EC) system was installed; and ‘C’ is the field in which automatic meteorological station (AWS) was placed. ‘A’, ‘B’ and ‘C’ are indicated by three solid-line red rectangles. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.) 68
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method (CRGM). The gradient methods are the simplest class of available methods. The GM is the most classic and widely used way to retrieve the PBL depth which gives the PBL depth as the altitude of the minimum gradient of the RSCS. The negative gradients near surface may cause the misjudgment of PBL depth, so the LGM takes the logarithm of the lidar signal before searching for the most significant negative gradient. The effect of signal strength as a function of height can be removed by NGM. CRGM considers the linear instability theory of gravity waves which is associated with the complex vertical distribution of aerosol to determine the PBL depth. More complex methods (like wavelet method) may give better results in some cases but they are less direct methods and, thus, it is difficult to tune their parameters for general use. Therefore, in this work we inter-compare PBL depth retrieved from individual gradient methods. The lidar signal can be expressed by Eq. (1) below:
data processing techniques are provided by Tsaknakis et al. (2011). 2.2.2. EC system The eddy covariance (EC) system installed on a 70-m tower (marked ‘B’ in Fig. 1d) is about 500 m away to the west of the lidar, including a H2O/CO2 analyzer (LI-7500, LI-COR Biotechnology, Lincoln, NE, USA) and a three-dimensional sonic anemometer (WindMaster Pro, Gill Instruments Limited, Lymington, Hampshire, UK). The instruments were installed at the height of 60 m, and all signals for the sensors were recorded at a sampling rate of 10 Hz and were averaged over 30-min periods. 2.2.3. Automatic meteorological station Meteorological data were continuously collected at the Automatic meteorological station (AWS, marked ‘C’ in Fig. 1d), about 600 m to the west of the lidar site. The automatic meteorological station was about 22 m above the sea level. The air temperature, air pressure, humidity and wind (including speed and direction) sensors were placed at 1.5 m, 1.5 m, 1.5 m and 10 m height. Each of these sensors sampled on an hourly basis, and the wind speed and wind direction were averaged at 10-min intervals.
r
∫ α (r ) dr⎤⎥
⎡ P (r ) = CP0 r −2β (r )exp ⎢−2 ⎣
⎦
0
(1)
where C is a constant for a given lidar system; P0 is the laser output energy; r is the range between the laser source and the target; β (r ) is backscatter coefficients; and α (r ) is extinction coefficient. The RSCS is then defined in Eq. (2) by
2.3. Data processing and existing algorithms
RSCS = P (r ) r 2
2.3.1. Pre-processing Due to low signal-to-noise ratio, a moving average of 15-points in height was applied to raw RSCS in accordance with the work of Wang et al. (2012), who previously reported that the selection of smoothed data will undoubtedly affect the results of the gradient method to extract the height of PBL depth and gave the influencing trend of the smoothed data. Combined with the lidar observations in this experiment, a 15-points moving averaging was selected in this paper. In addition, the data comes from clear air (non-cloudy or precipitation) days and, thus, there was no impact of clouds on lidar data. In this paper, the software EddyPro 5.2.1 developed by LI-COR Biotechnology was used to process the EC data. The EddyPro software applied the following corrections: despiking algorithm (Vickers and Mahrt, 1997), spectral corrections (Moncrieff et al., 2004), compensation for density fluctuations (Burba et al., 2012), time lag compensation, double rotation for tilt correction, block averaging, and statistical tests (Vickers and Mahrt, 1997). Then, half-hour averages of heat fluxes were derived. Ambiguous meteorological data and flux data were eliminated by using a criterion of X (t ) < X − 4σ or X (t ) > X + 4σ , where X (t ) is the measurement, X is the mean value over the interval and σ is the standard deviation.
(2)
The GM, which is the most commonly used method to retrieve the PBL depth, assigns the PBL depth to the altitude (hGM ) of the minimum gradient of the RSCS (Flamant et al., 1997; Hayden et al., 1997), is described by
hGM ,where min(ΔRSCS /Δr )
(3)
The LGM determines the PBL depth at the altitude, hLGM , where the minimum of the first gradient of RSCS logarithm is reached (Senff et al., 1996). This altitude is calculated by the equation
hLGM , wheremin[Δ ln(RSCS )/ Δr ]
(4)
The NGM, described below, estimates the PBL depth at the altitude where the normalized RSCS gradient reaches a minimum (He et al., 2006).
hNGM , wheremin[ΔRSCS /(Δr × RSCS )]
(5)
The CRGM, a relative new algorithm for PBL depth determination, the PBL depth corresponds to the altitude (hCRGM ) where the cubic root gradient of RSCS reaches a minimum (Yang et al., 2017). This method integrates the impact of gravity waves on the atmospheric structure in determining the PBL depth, and it is expressed by
2.3.2. Introduction of existing algorithm for PBL depth determination Numerous methods have been proposed and widely applied to retrieve the PBL depth from lidar. These methods include the maximum variance method (Hooper and Eloranta, 1986), the fitting idealized profile method (Steyn et al., 1999), the first point method (Boers and Melfi, 1987), the threshold method (Dupont et al., 1994), the wavelet transform method (Baars et al., 2008; Davis et al., 2000), and the composite method of threshold and fractional methods (Huang et al., 2016). However, the individual gradient methods are the most classic and widely accepted. In normal conditions, a large amount of aerosol particles is gathered in the PBL under the temperature inversion layer which is at the top of the boundary layer, while the aerosol particle concentration and water vapor content decrease rapidly above the inversion layer. Consequently, the gradient of the RSCS exhibits a strong negative peak at the transition between the boundary layer and the free atmosphere. Based on this principle, gradient algorithms were proposed and widely used. In this paper, we focus on four gradient methods, including the gradient method (GM), the logarithm gradient method (LGM), the normalized gradient method (NGM), and the cubic root gradient
hCRGM , wheremin[Δ (RSCS1/3)/ Δr ]
(6)
The PBL depth retrieved from GM, LGM and NGM was compared against the CRGM results, on the basis of three statistical analysis (Colello et al., 1998) as follows: n
Bias =
∑ (hi − hCRGMi)/n
(7)
i=1
n
SEE =
∑ (hi − hCRGMi)2 /(n − 2) i=1 n
NSEE =
n
∑ (hi − hCRGMi)2 / ∑ (hCRGMi)2 i=1
(8)
i=1
(9)
Where bias is the difference between other retrieved methods and CRGM; n is the total number of data points (n = 2016); SEE is the standard error of the other retrieved results from CRGM; and NSEE is a normalized SEE, denoting an estimate of relative uncertainty. 69
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1 and January 3. The ground synoptic situation was mainly driven by the cold and high pressure system which prevented the vertical diffusion of pollutants and conducted to the accumulation of pollutants during the observation period.
Table 1 Air quality during the period from December 28, 2016 to January 3, 2017. Date December December December December January 1 January 2 January 3
28 29 30 31
Air quality levels
PM2.5 (μgm−3)
Excellent Good Light pollution Medium pollution Medium pollution Light pollution Medium pollution
30 68 101 119 134 109 128
3.2. Meteorological conditions Temporal variations of PM2.5 concentration, horizontal wind speed (WS), wind direction, air temperature, relative humidity (RH), and air pressure from December 28, 2016 to January 3, 2017 are shown in Fig. 3. The PM2.5 concentration was low and stable on December 28, 2016 with a 24 h average PM2.5 concentration of 20.4 μgm−3 and rose rapidly on December 29, 2016 varying from 35 to 114 μgm−3. The maximum value of PM2.5 concentration was 194 μgm−3 at 13:00 on December 31, 2016. The data gap on January 1, 2017 was caused by equipment failure (Fig. 3a). On the last two days of the observation period, the PM2.5 concentration was maintained in the range 77–153 μgm−3. During the observation period, the site was under the low wind regime, and the WS showed evident diurnal variation with the maximum WS (varying from 1.4 to 3.5 ms−1 on different days) occurring at noon every day (Fig. 3b). WS showed a decreasing trend, and the mean WS during the observation was 1.2 ms−1, while the maximum of WS was 3.5 ms−1 at 11:00 on December 30. After December 30, 2016, wind speeds were mostly less than 2 ms−1. This low wind speed corresponded to the stable weather background shown in Fig. 2 and conducted an unfavorable situation for air pollutants horizontal diffusion. The easterly wind prevailed (about 36.4%) during the period (Fig. 3c) and the westerly wind accounting for 17.3%. The air temperature (Fig. 3d) also significantly varied with a diurnal cycle with a maximum of 287.2 K at 15:00 on January 1, and minimum of 271.4 K at 5:00 on December 28. The difference between the highest and lowest air temperature was 15.8 K for the whole experimental period and the overall average air temperature was 278.5 K. Furthermore, the air temperature demonstrated a rising trend. Mostly, RH varied from about 50% to 100% during a day with a mean RH of 75.43% during the experiment period. Generally, the maximum RH (Fig. 3e) corresponding to the minimum air temperature at about 2:00, while the minimum RH corresponded to the maximum air temperature at about 14:00. In the daytime, the increased air temperature decreased RH near surface, and there was a larger RH in nighttime than in daytime. The variation of air pressure is presented in Fig. 3f. During the observation period, the value of air pressure varied from 1019.6 hpa (at 18:00 on January 1) to 1035.6 hpa (at 10:00 on December 29). The mean air pressure during the observation was 1027 hpa. Overall, the air pressure varied without obvious diurnal variation and it was in the uniform pressure field in the last two days (Fig. 2). During the polluted days, the wind speeds were often less than 2 ms−1, indicating weak horizontal transport of aerosols. The average RH also increased gradually (from 69.5 to 88.1) in polluted days from December 30 to January 2, and decreased to 74.6 on January 3. This increased water vapor content of the atmosphere was in favor of the hygroscopic growth of aerosol. Additionally, on the polluted days, the average pressure decreased from 1030.8 (December 30) to 1022.6 hpa (January 1), then remained static in the last two days. The surface pattern throughout this pollution process is consistent with the synoptic pattern described in Fig. 2.
3. Results and discussions 3.1. Synoptic situation There was a pollution event in the Nanjing area during this observation period. The concentration of fine particulate matter with a size smaller than 2.5 μm (PM2.5) from Nanjing Environmental Monitoring Station is shown in Table 1. Although fine particles are only a small component of the Earth's atmospheric composition, they have important effects on air quality and visibility. PM2.5 can be suspended in the air for a long time, and the higher its concentration in the air, the more serious the air pollution is. PM2.5 is, therefore, suitable for representing short-term air quality status and trends in cities. The air quality level was divided into 6 categories according to the 24-h average concentration of PM2.5: I (0–35 μgm−3, Excellent quality/unpolluted), II (35–75 μgm−3, Good), III (75–115 μgm−3, Light pollution), IV (115–150 μgm−3, Medium pollution), V (150–250 μgm−3, Severe pollution), and VI (> 250 μgm−3, Serious pollution). During this period, there were two days of light pollution (category III) and three days of medium pollution (category IV). It was found that, from December 28 to 29 2016, the daily mass concentration of PM2.5 was below the Grade II National Ambient Air Quality standard (75 μgm−3 per 24 h). Therefore, December 28 and 29 were defined as clean days. The beginning of the pollution episode was on December 30 with the daily mass concentration of PM2.5 increased significantly and exceeded the Grade III National Ambient Air Quality standard (75 μgm−3 per 24 h). Subsequently, the daily mass concentration of PM2.5 reached the maximum of 134 μgm−3 on January 1, 2017, and turned to moderate air pollution level. After January 3, 2017, precipitation in the Nanjing area reduced the air pollution level. This showed a clear air pollution process from December 30, 2016 to January 3, 2017, with the daily concentration of PM2.5 exceeding the limit of China's Grade II National Ambient Air Quality Standard (75 per 24 h). On January 4, it started to rain and the observation was stopped. The large spacing between the synoptic charts isobars also indicated the large scale stagnation near the surface. Usually, the change of surface weather situation is a good indicator of the potential of air pollution prediction (McGregor and Bamzelis, 1995) and the change of weather situation changes the air pollution spatial distribution. Fig. 2 shows the surface weather maps at 08:00 December 28, 2016, January 1 and January 3, 2017 (Beijing local time), respectively. On December 28, a high-pressure system in Yangtze River delta area controlled the weather situation in Nanjing, and the stable synoptic situation conducted a light mist enveloping most of the area of Jiangsu province (Fig. 2a). The low wind was conductive to the accumulation of pollutants over Nanjing. On January 1, the high-pressure system moved to South Korea and Nanjing was located at the back of this high-pressure system (Fig. 2b). During this day, small pressure gradient and near surface low wind speed led to a limited diffusion conditions, which was favorable to the accumulation of pollutants. On January 3, Nanjing city was controlled by uniform pressure field without apparent high pressure or low pressure system (Fig. 2c). The consistent transmission of easterly wind brought sufficient water vapor for pollutants on January
3.3. Turbulent heat fluxes Temporal variations of momentum flux, sensible heat flux, and latent heat flux from December 28, 2016 to January 3, 2017 were demonstrated in Fig. 4. The data gaps on December 28, 2016 were caused by equipment failure. During the observation period, the momentum flux (τ ) showed evident diurnal variation with overall average of 0.1 kgm−1s−2 . The maximum τ occurred at noon every day with a value varying from 0.2 to 0.6 kgm−1s−2 on different days (Fig. 4a). In the first 70
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Fig. 2. Surface weather maps (Data source: NCEP FNL) at 08:00 on (a) December 28, 2016, (b) January 1, 2017, and (c) January 3, 2017 (Beijing local time). The black lines, shadings and blue vectors indicate the geopotential heights (units: gpm), air temperature (units: K) and winds, respectively. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
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Fig. 3. Temporal variations of (a) PM2.5 concentration, (b) wind speed, (c) wind direction, (d) air temperature, (e) relative humidity, and (f) surface atmospheric pressure from December 28, 2016 to January 3, 2017. The clean days and polluted days are marked in yellow and blue, respectively. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
three days, the value of τ was relatively larger and it showed obvious diurnal variation. In the next four days, the value of τ was significantly decreased with daily maximum of about 0.39 kgm−1s−2 . This smaller momentum fluxes occurred with less wind speed (Fig. 3a). This decrease of wind speed and τ both clearly indicated a weak surface turbulent exchange between December 31 and January 3, and this stable condition was beneficial to the accumulation of pollutants. The sensible heat (H) and latent heat flux (LE) also showed a clear diurnal cycle. The maximum of H occurred around noon with range from 78.7 to 159.4 Wm−2 on different days, and slightly decreased with time (Fig. 4b). The largest value of LE was 127.7 Wm−2 at 15:00 on January 1, and the smallest value of LE was −31.1 Wm−2 at 9:00 on January 1. On clean days, the average τ , H and LE were more than 0.15 kgm−1s−2 , 25.4 Wm−2 and 16.5 Wm−2 , respectively. While during the polluted days, the average τ , H and LE were less than 0.08 kgm−1s−2 , 15 Wm−2 and 15 Wm−2 , respectively. Specifically, the average H and LE decreased to a minimum value of 4.2 Wm−2 and 5.2 Wm−2 on January 1. Overall, during the polluted days, the value of τ , LE, H were smaller than that on clean day. This also suggests a weaker surface turbulent exchange on the polluted days.
aerosol loading with weak thermal convection condition (Ji et al., 2018). Therefore, CRGM results were used as the reference value of the PBL depth. Usually the larger aerosol extinction coefficient was obtained at lower heights than PBL depth because of more aerosols inside PBL. The maximum values of PBL depth were generally up to 1600 m on clean days and were around 600 m on polluted days. Overall, the maximum value of daily PBL depth decreased in the first five days, reached a minimum value on January 1, then increased on January 2 and decreased on January 3. Notably, the maximum PBL depth was inversely proportional to the daily concentration of PM2.5 described Table 1. Under the polluted conditions, the depth of PBL was lower. The shallow boundary layer limited the vertical dispersions of air pollutants and facilitated the formation of the haze episodes. The PBL depth retrieved from GM was smallest (almost 220 m) in most of the times. This indicates that this method is not accurate in this experiment and this is consistent with the study of Steyn in the detection of mixed layer depth (Steyn et al., 1999). The results of LGM and NGM were close to each other and most of the overestimating abnormal values (marked in the white circles) were from these two methods. In the last four days, the points in the white circles were invalid and mainly because of the low SNR. This also shows that CRGM is optimal for severe haze days. An intensive radiosonde campaign was conducted over the Nanjing site (31.9°N, 118.9°E) two times per day: 07:15 and 19:15 local standard time in 2016, but unfortunately it was terminated on December 31, 2016. At 19:15 on December 28, the radiosonde-retrieved PBL depth was 1282 m, compared with four-retrieved results 240 m, 1335 m, 1335 m and 1335 m, respectively. At 19:15 on December 29, the PBL depth retrieved by CRGM was 1012.5 m, in better agreement with the actual PBL depth determined by radiosonde (1191 m), as opposed to 240 m, 1395 m and 1395 m determined by GM, LGM and NGM. The beginning of the pollution episode was on
3.4. Comparison of existing algorithms for PBL depth estimation Fig. 5 shows the vertical evolution of aerosol extinction coefficient and the temporal variations of PBL depth retrieved from four methods during the period from December 28, 2016 to January 3, 2017. Compared with other algorithms, CRGM considers the linear instability theory of gravity waves which is associated with the complex vertical distribution of aerosol to determine the PBL depth. This method is optimized for severe haze days and it is more accurate under high 72
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Fig. 4. Temporal variation of half-hourly mean (a) momentum flux (τ ), (b) sensible heat flux (H), and (c) latent heat flux (LE) during the period from December 28, 2016 to January 3, 2017 at the Agricultural Meteorological Experiment Station. The clean days and polluted days are marked in yellow and blue, respectively. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
is probably caused by air pollutants, near surface. Comparing the twomoment radiosonde-retrieved PBL depth (997 m and 761 m) on December 30 with the algorithms’ results (247.5 m, 967.5 m, 957 m, 270 m; 240 m, 870 m, 870 m, 247.5 m) illustrates that the PBL depth retrieved by LGM and NGM agrees well with the radiosonde-retrieved result. Fig. 6 shows the temporal variations of PBL depth retrieved from four methods and the Planetary Boundary Layer Height surface on
December 30. The aerosols usually were not ideally distributed because of the lack of sufficient turbulent mixing, which resulted in the accumulation of aerosol near the ground and multi-layer structure would exist within the boundary layer. This leads to the large discrepancies between the PBL depth from the CRGM and LGM &NGM. LGM and NGM looks like they perform better than the reference method CRGM in this time period. But, in the corresponding RSCS plot (not shown here) it can be seen more clearly that there is strong backscatter layer, which
Fig. 5. Vertical evolution of aerosol extinction coefficient (355 nm) and temporal variations of PBL depth retrieved from four methods. The color scale indicates the intensity of aerosol extinction coefficient, and warm colors represent stronger light scattering. The diurnal PBL depth derived from GM, LGM, NGM, and CRGM are illustrated as magenta plus, green pentagram, red circle, and black dot, respectively. Abnormal values are marked in the white circles. The radiosonde-retrieved results are marked by triangles surrounded by green solid-line. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.) 73
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Fig. 6. Temporal variations of PBL depth retrieved from four methods and the Planetary Boundary Layer Height surface on 32°N, 119°E from NCEP FNL data. The Planetary Boundary Layer Height surface on that point is illustrated as black dotted line. The diurnal PBL depth derived from GM, LGM, NGM, and CRGM are illustrated as magenta plus, green pentagram, red circle, and black dot, respectively. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
the cases with CRGM values less than 300 m, in order to avoid bias of the statistics by those cases.
32°N, 119°E from the National Center for Environmental Prediction (NCEP) Final Analysis (FNL) dataset during the period from December 28, 2016 to January 3, 2017 at 0200 LST, 0800 LST, 1400 LST, and 2000 LST, respectively. FNL data has low resolution and represents the spatial average of 1° × 1°. So the variable of planetary boundary layer height cannot be regarded as the true value, but it still has a certain reference value. From the FNL data, the overall trend of the diurnal variation of PBL depth is basically consistent with the retrieved results. However, the nocturnal PBL depth from FNL data at 2000 LST and 0200 LST often to be 22 m, which is relatively unrealistic and largely deviated from our nocturnal results. The retrieved results at night were often found to be larger than 200 m.
3.6. Case analysis In order to analyze the influence of pollution conditions on the evolution of PBL structure, we focus on the characteristics of PBL depth on December 28, 2016 (as a case of clean day) and January 1, 2017 (as a case of polluted day) in this section. 3.6.1. Clean day Fig. 8 shows the boundary layer evolution in terms of the timeheight cross-sectional observation of aerosol extinction coefficient in the 355 nm channel collected on December 28, 2016. The PBL depth changed slowly in the late night (0000–0700 LST). After sunrise, the depth of PBL shoots sharply up (around 0900 LST) and decreased slowly after sunset. The PBL deepened to a maximum value of 1560 m for CRGM at 1310 LST in the daytime (0700–1700 LST); the whole layer remained convectively unstable and well-mixed during the daytime hours. After sunset, the turbulence weakened, and the PBL depth decreased slowly. The mean PBL depth (determined from the median value to avoid bias effects by outliers) was 438.8 m for CRGM at the late night (0000–0700 LST) and it was 671.3 m in the early evening (17000000 LST). It was worth noting that the PBL depth in the early evening was larger than that at the late night. It can be explained by the natural diurnal cycle over land surfaces due to solar input of heat at the surface only during daylight hours. According to Fig. 4, the turbulent heat fluxes are much greater during the day than at night. On clean days, PBL depth had obvious characteristics of diurnal variation, relatively lower in the nighttime, relatively larger in the daytime, and reached a maximum value at noon. In the early evening, the nocturnal boundary layer was usually stable because of the negative sensible heat fluxes and near zero positive latent heat fluxes.
3.5. Statistical analysis of retrieved PBL depth Table 2 presents the three statistical indices bias, SEE, and NSEE for the other retrieved results compared with CRGM results. The bias value shows that GM tend to underestimate the PBL depth and LGM and NGM tend to overestimate with respect to CRGM. Scatterplots of the PBL depth retrieved from GM, LGM, and NGM against CRGM are given in Fig. 7. The GM is frequently biased at a value of about 250 m, which is probably due to the effect of the overlap function of the lidar. On the other hand, the CRGM reference method detects many times a strong low-height backscatter layer, while the LGM and NGM detect the top of the general aerosol layer (see also the discussion of Fig. 5 in Section 3.4 for multiple layering of aerosols). The results in Table 2 do not include Table 2 Computed Bias, Standard Error of the Estimate (SEE), and Normalized Standard Error of the Estimate (NSEE) of GM, LGM, NGM in comparison with CRGM at NUIST from December 28, 2016 to January 3, 2017. Method
Bias
SEE
NSEE
GM LGM NGM
−343.17 m 110.99 m 112.89 m
504.81 m 253.05 m 255.31 m
0.67 0.33 0.34
3.6.2. Polluted day In order to investigate the evolution of PBL depth in the polluted 74
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Fig. 7. Scatterplots of PBL depth retrieved from GM, LGM, and NGM against CRGM results during the period from December 28, 2016 to January 3, 2017.
kgm−1s−2 ) in the late night (Fig. 4) indicated a stable boundary layer structure, and the PBL maintained a lower height at night. After sunrise (around 0900 LST), H gradually increased from −7.9 to only 78.7 Wm−2 and τ slightly increased from 0.08 to 0.23 kgm−1s−2 . This made the PBL depth rise gradually to the relatively low maximum value (660 m) at 1500 LST. It can be concluded from the consistency between PBL depth and H that the small increase H (which was caused by reduction of solar radiation relatively to a clean day) was the main reason for the small development of PBL on this polluted day. After about 2100 LST, H reached 39.1 Wm−2 at 2200 LST and went down to −19.5 Wm−2 at 2230 LST.
episode, the evolution of aerosol extinction coefficient on January 1, 2017 (as a case of polluted day) and the PBL depth retrieved from the four detection methods is shown in Fig. 9 (similar to Fig. 8). The PBL deepened to a maximum value (around 660 m) at about 1500 LST in the daytime (0700–1700 LST). The maximum value of PBL depth on polluted day was on average 900 m lower than that of clean day. PBL depth was 315 m for CRGM in the early evening (1700-0000 LST) and was 236.3 m at the late night (0000–0700 LST). In addition, the diurnal variation of PBL depth on polluted day was smaller than that on clean day. The decrease of PBL depth further causes the accumulation of pollutants. There were some abnormal values between 1200 and 1500 LST retrieved by LGM and NGM that are marked in white circle. On the polluted day, the negative H and small τ (less than 0.1
Fig. 8. Same as Fig. 5 but for December 28, 2016. Sunrise and sunset times are marked by triangles. 75
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Fig. 9. Same as Fig. 5 but for January 1, 2017. Sunrise and sunset times are marked by triangles.
4. Conclusions
of PBL depth was about 660 m. In addition, the diurnal variation of PBL depth on polluted day was smaller than that on clean day. The lower PBL depth made aerosol particles to confine in a shallow PBL and prevented the vertical dispersion of aerosol particles, leading to an increase in the surface aerosol concentrations and, thus, possibly lower solar radiation at the surface and PBL growth.
During 2016–2017 winter, a high-resolution aerosol lidar, eddy covariance system and automatic meteorological station, were deployed in the north of Nanjing city with the aim of estimating boundary-layer depth and its diurnal evolution. This study compared PBL depth retrieved from GM, LGM, NGM and CRGM and the differences between these methods. Through the comparison of the evolution of PBL depth over clean day and polluted day, the boundary layer structure and PBL depth under different weather conditions can be displayed. The surface synoptic situation from NCEP FNL data showed that Nanjing was mainly driven by the cold and high pressure system which prevented the vertical diffusion of pollutants and conducted to the accumulation of pollutants during the observation period. The meteorological conditions collected at the automatic meteorological station and turbulent heat fluxes from EC system showed that the site was under the low wind regime that promoted the accumulation of pollutants at the beginning of the air pollution process. The elastic lidar data were used to show the condition of PBL and the evolution of PBL depth. Higher aerosol extinction coefficient values were obtained at lower heights than PBL depth suggesting that the haze pollution was predominantly formed and evolved inside the atmospheric boundary layer. The retrieved results by CRGM, which are based on a detection method that takes into the linear instability theory of gravity waves, were considered as the reference value of PBL depth. The PBL depth were generally up to 1600 m on clean day and were around 600 m on polluted day. Overall, the variation of PBL depth varied opposite to the daily concentration of PM2.5 and the maximum values of daily PBL depth showed a decreasing trend. The PBL depth retrieved from GM was smallest (almost 220 m) in most of the times and GM results did not agree well with PBL values calculated by the other algorithms evaluated in this study. The results of LGM and NGM were close to each other and most of the overestimating abnormal values (marked in the white circles) were from these two methods. The GM was biased at low height values probably due to the effect of the overlap function of the lidar, while the LGM and NGM could not detect low height multiple layers as the CRGM could do. On clean day, PBL depth had obvious characteristics of diurnal variation. The PBL depth was relatively lower in the nighttime, relatively larger in the daytime, and reached the maximum value at noon. On polluted day, the weak WS and τ with the stable surface situation inhibited the development of PBL depth; therefore, the maximum value
Conflicts of interest None. Acknowledgments This research was supported by grants from National Key Research and Development Program of Ministry of Science and Technology of China (2016YFC0203304), and National Natural Science Foundation of China (41875013, 41275022, and 41711530223). We are very grateful to anonymous reviewers for their careful review and valuable comments, which led to substantial improvement of this manuscript. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.atmosenv.2019.02.022. References Baars, H., Ansmann, A., Engelmann, R., Althausen, D., 2008. Continuous monitoring of the boundary-layer top with lidar. Atmos. Chem. Phys. 8, 7281–7296. Boers, R., Melfi, S.H., 1987. Cold air outbreak during MASEX: lidar observations and boundary-layer model test. Boundary-Layer Meteorol. 39 (1), 41–51. Burba, G., Schmidt, A., Scott, R., Nakai, T., Kathilankal, J., Fratini, G., et al., 2012. Calculating CO2 and H2O eddy covariance fluxes from an enclosed gas analyzer using an instantaneous mixing ratio. Glob. Chang. Biol. 18 (1), 385–399. Cohn, S.A., Angevine, W.M., 2000. Boundary layer height and entrainment zone thickness measured by lidars and wind-profiling radars. J. Appl. Meteorol. 39 (8), 1233–1247. Colello, G.D., Griver, C., Sellers, P.J., Berry, J.A., 1998. Modeling of energy, water, and CO2 flux in a temperate grassland ecosystem with SiB2: May–October 1987. J. Atmos. Sci. 55, 1141–1169. Davis, K.J., Gamage, N., Hagelberg, C.R., Kiemle, C., Lenschow, D.H., et al., 2000. An objective method for deriving atmospheric structure from airborne lidar observations. J. Atmos. Ocean. Technol. 17 (11), 1455–1468. Dupont, E., Pelon, J., Flamant, C., 1994. Study of the moist convective boundary layer structure by backscattering lidar. Boundary-Layer Meteorol. 69 (1), 1–25. Flamant, C., Pelon, J., Flamant, P.H., Durand, P., 1997. Lidar determination of the entrainment zone thickness at the top of the unstable marine atmospheric boundary layer. Boundary-Layer Meteorol. 83 (2), 247–284. Hayden, K.L., Anlauf, K.G., Hoff, R.M., Strapp, J.W., Bottenheim, J.W., et al., 1997. The
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layer under different weather conditions, and its impact on aerosol concentrations. Particuology 11 (1), 34–40. Quan, J., Tie, X., Zhang, Q., Liu, Q., Li, X., et al., 2014. Characteristics of heavy aerosol pollution during the 2012–2013 winter in Beijing, China. Atmos. Environ. 88, 83–89. Senff, C., Bösenberg, J., Peters, G., Schaberl, T., 1996. Remote sensing of turbulent ozone fluxes and the ozone budget in the convective boundary layer with DIAL and RadarRASS: a case study. Contrib. Atmos. Phys. 69, 161–176. Steyn, D.G., Baldi, M., Hoff, R.M., 1999. The detection of mixed layer depth and entrainment zone thickness from lidar backscatter profiles. J. Atmos. Ocean. Technol. 16 (7), 953–959. Strawbridge, K.B., Snyder, B.J., 2004. Planetary boundary layer height determination during Pacific 2001 using the advantage of a scanning lidar instrument. Atmos. Environ. 38 (34), 5861–5871. Tie, X., Madronich, S., Li, G., Ying, Z., Zhang, R., et al., 2007. Characterizations of chemical oxidants in Mexico City: a regional chemical dynamical model (WRF-Chem) study. Atmos. Environ. 41 (9), 1989–2008. Tsaknakis, G., Papayannis, A., Kokkalis, P., Amiridis, V., Kambezidis, H.D., et al., 2011. Inter-comparison of lidar and ceilometer retrievals for aerosol and Planetary Boundary Layer profiling over Athens, Greece. Atmos. Meas. Tech. 4 (6), 1261–1273. Vickers, D., Mahrt, L., 1997. Quality control and flux sampling problems for tower and aircraft data. J. Atmos. Ocean. Technol. 14 (3), 512–526. Wang, Z., Li, J., Zhong, Z., Liu, D., Zhou, J., 2008. LIDAR exporation of atmospheric boundary layer over downtown of Beijing in summer. J. Applied Optics 29 (2), 96–100. Wang, L., Xie, C., Han, Y., Liu, D., Wei, H., 2012. Comparison of retrieval methods of planetary boundary layer height from lidar data. J. Atmos. Environ. Opt. 7 (4), 241–247. Yang, T., Wang, Z., Zhang, W., Gbaguidi, A., Sugimoto, N., et al., 2017. Technical note: boundary layer height determination from lidar for improving air pollution episode modeling: development of new algorithm and evaluation. Atmos. Chem. Phys. 17 (10), 6215–6225.
vertical chemical and meteorological structure of the boundary layer in the Lower Fraser Valley during Pacific '93. Atmos. Environ. 31 (14), 2089–2105. He, Q.S., Mao, J.T., Chen, J.Y., Hu, Y.Y., 2006. Observational and modeling studies of urban atmospheric boundary-layer height and its evolution mechanisms. Atmos. Environ. 40 (6), 1064–1077. Hooper, W.P., Eloranta, E.W., 1986. Lidar measurements of wind in the planetary boundary layer: the method, accuracy and results from joint measurements with radiosonde and kytoon. J. Clim. Appl. Meteorol. 25 (7), 990–1001. Huang, M., Gao, Z., Miao, S., Chen, F., LeMone, M.A., et al., 2016. Estimate of boundarylayer depth over Beijing, China, using Doppler lidar data during SURF-2015. Boundary-Layer Meteorol. 162 (3), 503–522. Ji, X., Liu, C., Xie, Z., Hu, Q., Dong, Y., et al., 2018. Comparison of mixing layer height inversion algorithms using lidar and a pollution case study in Baoding, China. J. Environ. Sci. https://doi.org/10.1016/j.jes.2018.11.003. Klett, J.D., 1985. Lidar inversion with variable backscatter/extinction ratios. Appl. Optic. 24 (11), 1638–1643. Mao, M., Jiang, W., Wu, X., Qi, F., Yuan, R., et al., 2006. LIDAR exploring of the UBL in downtown. ACTA Sci. Circumstantiate 26 (10), 1723–1728. Mao, M., Zhang, Y., Fang, H., Qi, F., Shao, S., et al., 2007. Detection of aerosol distribution by atmospheric environment airbone Lidar over Qingdao and adjacent sea area. Chin. J. Geophys. 50 (2), 370–376. Matthias, V., Bösenberg, J., 2002. Aerosol climatology for the planetary boundary layer derived from regular lidar measurements. Atmos. Res. 63 (3), 221–245. McGregor, G.R., Bamzelis, D., 1995. Synoptic typing and its application to the investigation of weather air pollution relationships, Birmingham, United Kingdom. Theor. Appl. Climatol. 51 (4), 223–236. Moncrieff, J.B., Clement, R., Finnigan, J., et al., 2004. Averaging, Detrending, and Filtering of Eddy Covariance Time Series. Handbook of Micrometeorology. Springer, Dordrecht, pp. 7–31. Qiu, J., Zheng, S., Huang, Q., Xia, Q., Yang, L., et al., 2003. Lidar measurements of cloud and aerosol in the upper troposphere in Beijing. Chin. J. Atmos. Sci. 27 (1), 1–7. Quan, J., Gao, Y., Zhang, Q., Tie, X., Cao, J., et al., 2013. Evolution of planetary boundary
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Technical note
Estimate of boundary-layer depth in Nanjing city using aerosol lidar data during 2016–2017 winter
T
Sihui Fana, Zhiqiu Gaoa,b,∗, John Kalogirosc, Yubin Lia, Jian Yina, Xin Lia a Climate and Weather Disasters Collaborative Innovation Center, Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing, 210044, China b State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China c Institute of Environmental Research and Sustainable Development, National Observatory of Athens, Athens, 11810, Greece
A R T I C LE I N FO
A B S T R A C T
Keywords: Inter-comparison PBL depth Lidar Retrieval algorithms Air pollution
The planetary boundary-layer (PBL) structure was investigated using observations from single aerosol lidar, eddy covariance (EC) system, and automatic meteorological station (AWS) in the north of Nanjing city during an air pollution episode in 2016–2017 winter. Based on seven days’ observations under clear to polluted day, we present the temporal variations of the aerosol extinction profiles observed by lidar, and then inter-compare PBL depth retrieved from individual gradient methods. The results show that the gradient method (GM) generated the lowest PBL depth. In contrast to the cubic root gradient method (CRGM), which determined PBL depth ranging from 172 m to 1575 m during the observation period, the logarithm gradient method (LGM) and normalized gradient method (NGM) generated similar results and both tended to overestimate PBL depth on polluted days. The CRGM performed better than LGM and NGM in case of multiple backscatter layers and could detect low level layers, while the GM was biased at low heights probably due to the effect of lidar overlap function. Based on these measurements, the evolution of boundary layer structures and PBL depth over clean days and polluted days were compared. The results show that (1) on clean days, the strong surface turbulence exchange make the PBL depth fully developed and the PBL depth had obvious characteristics of diurnal variation; the maximum depth of PBL was 1560 m for CRGM; and (2) on polluted days, the high pressure system and lower wind was favorable to the accumulation of air pollutants, and thus generating less turbulence by reducing surface radiation. These conditions on polluted days led to smaller PBL depth than those on clean days, and the maximum depth of PBL was 660 m for CRGM. Besides, the diurnal variation of PBL depth on polluted days was weaker than those on clean days.
1. Introduction
determining the PBL depth (Flamant et al., 1997). The lidar has been used in many field experiments to detect the PBL depth continuously (Cohn and Angevine, 2000; Flamant et al., 1997). Strawbridge and Snyder (2004) used the advantage of a scanning lidar instrument to determine the PBL depth over Pacific during 2001. Regular aerosol extinction and backscatter measurements using a UV Raman Lidar have been performed for almost 3 years in Hamburg in the frame of the German Lidar Network (Matthias and Bösenberg, 2002). In China, Qiu et al. (2003) carried out detection experiment of high cloud and aerosol in the troposphere based on the multi-wavelength lidar. Mao et al. (2006, 2007) determined the vertical distribution and changes of urban boundary layer height using lidar and gave a preliminary analysis of the ground meteorological environment impacts on
The planetary boundary-layer (PBL) depth illustrates the relationships between air pollution intensity, duration, and scope (Yang et al., 2017). The evolution of boundary-layer structure has significant impacts on the diffusion, transmission and disappearance of pollutants in the lower atmosphere (Quan et al., 2013; Tie et al., 2007). It is observed that an increase of air pollutants is often associated with a shallow PBL depth, while a decrease of pollutants is accompanied by obvious uplift of the PBL depth (Quan et al., 2014). Accordingly, the measurements of PBL depth are crucial to the study of air pollution. The superior spatial and temporal resolutions make aerosol lidar technique one of the most suitable systems for analyzing the boundary layer structure and
∗ Corresponding author. Climate and Weather Disasters Collaborative Innovation Center, Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing, 210044, China. E-mail addresses: [email protected] (S. Fan), [email protected] (Z. Gao), [email protected] (J. Kalogiros).
https://doi.org/10.1016/j.atmosenv.2019.02.022 Received 7 October 2018; Received in revised form 18 January 2019; Accepted 17 February 2019 Available online 23 February 2019 1352-2310/ © 2019 Elsevier Ltd. All rights reserved.
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air pollution episode. An aerosol Lidar (marked ‘A’ in Fig. 1d), EC system (marked ‘B’ in Fig. 1d), and an automatic meteorological station (AWS, marked ‘C’ in Fig. 1d) were used at this experiment.
the diurnal variation of the boundary layer. Wang et al. (2008) studied atmospheric boundary layer structure characteristics during summer in Beijing, as well as the extinction characteristics of aerosols in the atmospheric boundary layer. However, few researches focused on the PBL under polluted weather conditions based on lidar despite the significant impact of PBL on air pollution process evolution. Consequently, this paper deals with lidar observations of atmospheric layers to sample the PBL structure under clear to polluted days over Nanjing. The paper is organized as follows: Section 2 describes the materials, including observational site, meteorological measurement systems, data processing and existing algorithms; Section 3 presents the main results and discussions; and Section 4 summarizes and concludes this paper.
2.2. Meteorological measurement systems 2.2.1. Lidar observation system A Raymetrics elastic lidar model LB110-ESS-D200 was installed on the rooftop of Beichen building during the experiment. It can operate 24 h per day in an unattended mode under no rain conditions. It emits laser pulses at 355 nm wavelength with a repetition rate of 20 Hz. For this study the raw temporal resolution of the retrieved aerosol profiles was set at 5 min, while a spatial resolution of 7.5 m was used, but only for altitudes higher than around 150 m due to an incomplete overlap between the field of telescope view and the laser beam. Lidar observations were originally represented by the range-squared-corrected signal (RSCS). Apart from this, the aerosol extinction profiles presented in our work were retrieved using the improved Klett's stable inversion algorithm. Usually, k is in the range 0.67–1.3. In general, the value k = 1 was used in the computations and a lidar ratio = 20 in this work. This algorithm is based on a far-end backward iterative technique, taking into account also the atmospheric molecular contribution as described by Klett (1985). Details on the lidar instruments and various
2. Measurements 2.1. Observation site Nanjing (32.04°N, 118.78°E), as the Yangtze River Delta Regional Economic Development Center, is located on the southwest border of Jiangsu province. An intensive observation campaign was carried out from December 28, 2016 to January 3, 2017 on the campus of Nanjing University of Information Science and Technology (NUIST) during an
Fig. 1. Site description and observation systems. (a) Map of eastern China; the location of the experimental site is marked by a red star; (b) 70-m tower; (c) Raymetrics elastic lidar; and (d) the experimental site. ‘A’ is the building on whose top Raymetrics elastic lidar was placed; ‘B’ is the field in which the eddy covariance (EC) system was installed; and ‘C’ is the field in which automatic meteorological station (AWS) was placed. ‘A’, ‘B’ and ‘C’ are indicated by three solid-line red rectangles. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.) 68
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method (CRGM). The gradient methods are the simplest class of available methods. The GM is the most classic and widely used way to retrieve the PBL depth which gives the PBL depth as the altitude of the minimum gradient of the RSCS. The negative gradients near surface may cause the misjudgment of PBL depth, so the LGM takes the logarithm of the lidar signal before searching for the most significant negative gradient. The effect of signal strength as a function of height can be removed by NGM. CRGM considers the linear instability theory of gravity waves which is associated with the complex vertical distribution of aerosol to determine the PBL depth. More complex methods (like wavelet method) may give better results in some cases but they are less direct methods and, thus, it is difficult to tune their parameters for general use. Therefore, in this work we inter-compare PBL depth retrieved from individual gradient methods. The lidar signal can be expressed by Eq. (1) below:
data processing techniques are provided by Tsaknakis et al. (2011). 2.2.2. EC system The eddy covariance (EC) system installed on a 70-m tower (marked ‘B’ in Fig. 1d) is about 500 m away to the west of the lidar, including a H2O/CO2 analyzer (LI-7500, LI-COR Biotechnology, Lincoln, NE, USA) and a three-dimensional sonic anemometer (WindMaster Pro, Gill Instruments Limited, Lymington, Hampshire, UK). The instruments were installed at the height of 60 m, and all signals for the sensors were recorded at a sampling rate of 10 Hz and were averaged over 30-min periods. 2.2.3. Automatic meteorological station Meteorological data were continuously collected at the Automatic meteorological station (AWS, marked ‘C’ in Fig. 1d), about 600 m to the west of the lidar site. The automatic meteorological station was about 22 m above the sea level. The air temperature, air pressure, humidity and wind (including speed and direction) sensors were placed at 1.5 m, 1.5 m, 1.5 m and 10 m height. Each of these sensors sampled on an hourly basis, and the wind speed and wind direction were averaged at 10-min intervals.
r
∫ α (r ) dr⎤⎥
⎡ P (r ) = CP0 r −2β (r )exp ⎢−2 ⎣
⎦
0
(1)
where C is a constant for a given lidar system; P0 is the laser output energy; r is the range between the laser source and the target; β (r ) is backscatter coefficients; and α (r ) is extinction coefficient. The RSCS is then defined in Eq. (2) by
2.3. Data processing and existing algorithms
RSCS = P (r ) r 2
2.3.1. Pre-processing Due to low signal-to-noise ratio, a moving average of 15-points in height was applied to raw RSCS in accordance with the work of Wang et al. (2012), who previously reported that the selection of smoothed data will undoubtedly affect the results of the gradient method to extract the height of PBL depth and gave the influencing trend of the smoothed data. Combined with the lidar observations in this experiment, a 15-points moving averaging was selected in this paper. In addition, the data comes from clear air (non-cloudy or precipitation) days and, thus, there was no impact of clouds on lidar data. In this paper, the software EddyPro 5.2.1 developed by LI-COR Biotechnology was used to process the EC data. The EddyPro software applied the following corrections: despiking algorithm (Vickers and Mahrt, 1997), spectral corrections (Moncrieff et al., 2004), compensation for density fluctuations (Burba et al., 2012), time lag compensation, double rotation for tilt correction, block averaging, and statistical tests (Vickers and Mahrt, 1997). Then, half-hour averages of heat fluxes were derived. Ambiguous meteorological data and flux data were eliminated by using a criterion of X (t ) < X − 4σ or X (t ) > X + 4σ , where X (t ) is the measurement, X is the mean value over the interval and σ is the standard deviation.
(2)
The GM, which is the most commonly used method to retrieve the PBL depth, assigns the PBL depth to the altitude (hGM ) of the minimum gradient of the RSCS (Flamant et al., 1997; Hayden et al., 1997), is described by
hGM ,where min(ΔRSCS /Δr )
(3)
The LGM determines the PBL depth at the altitude, hLGM , where the minimum of the first gradient of RSCS logarithm is reached (Senff et al., 1996). This altitude is calculated by the equation
hLGM , wheremin[Δ ln(RSCS )/ Δr ]
(4)
The NGM, described below, estimates the PBL depth at the altitude where the normalized RSCS gradient reaches a minimum (He et al., 2006).
hNGM , wheremin[ΔRSCS /(Δr × RSCS )]
(5)
The CRGM, a relative new algorithm for PBL depth determination, the PBL depth corresponds to the altitude (hCRGM ) where the cubic root gradient of RSCS reaches a minimum (Yang et al., 2017). This method integrates the impact of gravity waves on the atmospheric structure in determining the PBL depth, and it is expressed by
2.3.2. Introduction of existing algorithm for PBL depth determination Numerous methods have been proposed and widely applied to retrieve the PBL depth from lidar. These methods include the maximum variance method (Hooper and Eloranta, 1986), the fitting idealized profile method (Steyn et al., 1999), the first point method (Boers and Melfi, 1987), the threshold method (Dupont et al., 1994), the wavelet transform method (Baars et al., 2008; Davis et al., 2000), and the composite method of threshold and fractional methods (Huang et al., 2016). However, the individual gradient methods are the most classic and widely accepted. In normal conditions, a large amount of aerosol particles is gathered in the PBL under the temperature inversion layer which is at the top of the boundary layer, while the aerosol particle concentration and water vapor content decrease rapidly above the inversion layer. Consequently, the gradient of the RSCS exhibits a strong negative peak at the transition between the boundary layer and the free atmosphere. Based on this principle, gradient algorithms were proposed and widely used. In this paper, we focus on four gradient methods, including the gradient method (GM), the logarithm gradient method (LGM), the normalized gradient method (NGM), and the cubic root gradient
hCRGM , wheremin[Δ (RSCS1/3)/ Δr ]
(6)
The PBL depth retrieved from GM, LGM and NGM was compared against the CRGM results, on the basis of three statistical analysis (Colello et al., 1998) as follows: n
Bias =
∑ (hi − hCRGMi)/n
(7)
i=1
n
SEE =
∑ (hi − hCRGMi)2 /(n − 2) i=1 n
NSEE =
n
∑ (hi − hCRGMi)2 / ∑ (hCRGMi)2 i=1
(8)
i=1
(9)
Where bias is the difference between other retrieved methods and CRGM; n is the total number of data points (n = 2016); SEE is the standard error of the other retrieved results from CRGM; and NSEE is a normalized SEE, denoting an estimate of relative uncertainty. 69
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1 and January 3. The ground synoptic situation was mainly driven by the cold and high pressure system which prevented the vertical diffusion of pollutants and conducted to the accumulation of pollutants during the observation period.
Table 1 Air quality during the period from December 28, 2016 to January 3, 2017. Date December December December December January 1 January 2 January 3
28 29 30 31
Air quality levels
PM2.5 (μgm−3)
Excellent Good Light pollution Medium pollution Medium pollution Light pollution Medium pollution
30 68 101 119 134 109 128
3.2. Meteorological conditions Temporal variations of PM2.5 concentration, horizontal wind speed (WS), wind direction, air temperature, relative humidity (RH), and air pressure from December 28, 2016 to January 3, 2017 are shown in Fig. 3. The PM2.5 concentration was low and stable on December 28, 2016 with a 24 h average PM2.5 concentration of 20.4 μgm−3 and rose rapidly on December 29, 2016 varying from 35 to 114 μgm−3. The maximum value of PM2.5 concentration was 194 μgm−3 at 13:00 on December 31, 2016. The data gap on January 1, 2017 was caused by equipment failure (Fig. 3a). On the last two days of the observation period, the PM2.5 concentration was maintained in the range 77–153 μgm−3. During the observation period, the site was under the low wind regime, and the WS showed evident diurnal variation with the maximum WS (varying from 1.4 to 3.5 ms−1 on different days) occurring at noon every day (Fig. 3b). WS showed a decreasing trend, and the mean WS during the observation was 1.2 ms−1, while the maximum of WS was 3.5 ms−1 at 11:00 on December 30. After December 30, 2016, wind speeds were mostly less than 2 ms−1. This low wind speed corresponded to the stable weather background shown in Fig. 2 and conducted an unfavorable situation for air pollutants horizontal diffusion. The easterly wind prevailed (about 36.4%) during the period (Fig. 3c) and the westerly wind accounting for 17.3%. The air temperature (Fig. 3d) also significantly varied with a diurnal cycle with a maximum of 287.2 K at 15:00 on January 1, and minimum of 271.4 K at 5:00 on December 28. The difference between the highest and lowest air temperature was 15.8 K for the whole experimental period and the overall average air temperature was 278.5 K. Furthermore, the air temperature demonstrated a rising trend. Mostly, RH varied from about 50% to 100% during a day with a mean RH of 75.43% during the experiment period. Generally, the maximum RH (Fig. 3e) corresponding to the minimum air temperature at about 2:00, while the minimum RH corresponded to the maximum air temperature at about 14:00. In the daytime, the increased air temperature decreased RH near surface, and there was a larger RH in nighttime than in daytime. The variation of air pressure is presented in Fig. 3f. During the observation period, the value of air pressure varied from 1019.6 hpa (at 18:00 on January 1) to 1035.6 hpa (at 10:00 on December 29). The mean air pressure during the observation was 1027 hpa. Overall, the air pressure varied without obvious diurnal variation and it was in the uniform pressure field in the last two days (Fig. 2). During the polluted days, the wind speeds were often less than 2 ms−1, indicating weak horizontal transport of aerosols. The average RH also increased gradually (from 69.5 to 88.1) in polluted days from December 30 to January 2, and decreased to 74.6 on January 3. This increased water vapor content of the atmosphere was in favor of the hygroscopic growth of aerosol. Additionally, on the polluted days, the average pressure decreased from 1030.8 (December 30) to 1022.6 hpa (January 1), then remained static in the last two days. The surface pattern throughout this pollution process is consistent with the synoptic pattern described in Fig. 2.
3. Results and discussions 3.1. Synoptic situation There was a pollution event in the Nanjing area during this observation period. The concentration of fine particulate matter with a size smaller than 2.5 μm (PM2.5) from Nanjing Environmental Monitoring Station is shown in Table 1. Although fine particles are only a small component of the Earth's atmospheric composition, they have important effects on air quality and visibility. PM2.5 can be suspended in the air for a long time, and the higher its concentration in the air, the more serious the air pollution is. PM2.5 is, therefore, suitable for representing short-term air quality status and trends in cities. The air quality level was divided into 6 categories according to the 24-h average concentration of PM2.5: I (0–35 μgm−3, Excellent quality/unpolluted), II (35–75 μgm−3, Good), III (75–115 μgm−3, Light pollution), IV (115–150 μgm−3, Medium pollution), V (150–250 μgm−3, Severe pollution), and VI (> 250 μgm−3, Serious pollution). During this period, there were two days of light pollution (category III) and three days of medium pollution (category IV). It was found that, from December 28 to 29 2016, the daily mass concentration of PM2.5 was below the Grade II National Ambient Air Quality standard (75 μgm−3 per 24 h). Therefore, December 28 and 29 were defined as clean days. The beginning of the pollution episode was on December 30 with the daily mass concentration of PM2.5 increased significantly and exceeded the Grade III National Ambient Air Quality standard (75 μgm−3 per 24 h). Subsequently, the daily mass concentration of PM2.5 reached the maximum of 134 μgm−3 on January 1, 2017, and turned to moderate air pollution level. After January 3, 2017, precipitation in the Nanjing area reduced the air pollution level. This showed a clear air pollution process from December 30, 2016 to January 3, 2017, with the daily concentration of PM2.5 exceeding the limit of China's Grade II National Ambient Air Quality Standard (75 per 24 h). On January 4, it started to rain and the observation was stopped. The large spacing between the synoptic charts isobars also indicated the large scale stagnation near the surface. Usually, the change of surface weather situation is a good indicator of the potential of air pollution prediction (McGregor and Bamzelis, 1995) and the change of weather situation changes the air pollution spatial distribution. Fig. 2 shows the surface weather maps at 08:00 December 28, 2016, January 1 and January 3, 2017 (Beijing local time), respectively. On December 28, a high-pressure system in Yangtze River delta area controlled the weather situation in Nanjing, and the stable synoptic situation conducted a light mist enveloping most of the area of Jiangsu province (Fig. 2a). The low wind was conductive to the accumulation of pollutants over Nanjing. On January 1, the high-pressure system moved to South Korea and Nanjing was located at the back of this high-pressure system (Fig. 2b). During this day, small pressure gradient and near surface low wind speed led to a limited diffusion conditions, which was favorable to the accumulation of pollutants. On January 3, Nanjing city was controlled by uniform pressure field without apparent high pressure or low pressure system (Fig. 2c). The consistent transmission of easterly wind brought sufficient water vapor for pollutants on January
3.3. Turbulent heat fluxes Temporal variations of momentum flux, sensible heat flux, and latent heat flux from December 28, 2016 to January 3, 2017 were demonstrated in Fig. 4. The data gaps on December 28, 2016 were caused by equipment failure. During the observation period, the momentum flux (τ ) showed evident diurnal variation with overall average of 0.1 kgm−1s−2 . The maximum τ occurred at noon every day with a value varying from 0.2 to 0.6 kgm−1s−2 on different days (Fig. 4a). In the first 70
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Fig. 2. Surface weather maps (Data source: NCEP FNL) at 08:00 on (a) December 28, 2016, (b) January 1, 2017, and (c) January 3, 2017 (Beijing local time). The black lines, shadings and blue vectors indicate the geopotential heights (units: gpm), air temperature (units: K) and winds, respectively. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
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Fig. 3. Temporal variations of (a) PM2.5 concentration, (b) wind speed, (c) wind direction, (d) air temperature, (e) relative humidity, and (f) surface atmospheric pressure from December 28, 2016 to January 3, 2017. The clean days and polluted days are marked in yellow and blue, respectively. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
three days, the value of τ was relatively larger and it showed obvious diurnal variation. In the next four days, the value of τ was significantly decreased with daily maximum of about 0.39 kgm−1s−2 . This smaller momentum fluxes occurred with less wind speed (Fig. 3a). This decrease of wind speed and τ both clearly indicated a weak surface turbulent exchange between December 31 and January 3, and this stable condition was beneficial to the accumulation of pollutants. The sensible heat (H) and latent heat flux (LE) also showed a clear diurnal cycle. The maximum of H occurred around noon with range from 78.7 to 159.4 Wm−2 on different days, and slightly decreased with time (Fig. 4b). The largest value of LE was 127.7 Wm−2 at 15:00 on January 1, and the smallest value of LE was −31.1 Wm−2 at 9:00 on January 1. On clean days, the average τ , H and LE were more than 0.15 kgm−1s−2 , 25.4 Wm−2 and 16.5 Wm−2 , respectively. While during the polluted days, the average τ , H and LE were less than 0.08 kgm−1s−2 , 15 Wm−2 and 15 Wm−2 , respectively. Specifically, the average H and LE decreased to a minimum value of 4.2 Wm−2 and 5.2 Wm−2 on January 1. Overall, during the polluted days, the value of τ , LE, H were smaller than that on clean day. This also suggests a weaker surface turbulent exchange on the polluted days.
aerosol loading with weak thermal convection condition (Ji et al., 2018). Therefore, CRGM results were used as the reference value of the PBL depth. Usually the larger aerosol extinction coefficient was obtained at lower heights than PBL depth because of more aerosols inside PBL. The maximum values of PBL depth were generally up to 1600 m on clean days and were around 600 m on polluted days. Overall, the maximum value of daily PBL depth decreased in the first five days, reached a minimum value on January 1, then increased on January 2 and decreased on January 3. Notably, the maximum PBL depth was inversely proportional to the daily concentration of PM2.5 described Table 1. Under the polluted conditions, the depth of PBL was lower. The shallow boundary layer limited the vertical dispersions of air pollutants and facilitated the formation of the haze episodes. The PBL depth retrieved from GM was smallest (almost 220 m) in most of the times. This indicates that this method is not accurate in this experiment and this is consistent with the study of Steyn in the detection of mixed layer depth (Steyn et al., 1999). The results of LGM and NGM were close to each other and most of the overestimating abnormal values (marked in the white circles) were from these two methods. In the last four days, the points in the white circles were invalid and mainly because of the low SNR. This also shows that CRGM is optimal for severe haze days. An intensive radiosonde campaign was conducted over the Nanjing site (31.9°N, 118.9°E) two times per day: 07:15 and 19:15 local standard time in 2016, but unfortunately it was terminated on December 31, 2016. At 19:15 on December 28, the radiosonde-retrieved PBL depth was 1282 m, compared with four-retrieved results 240 m, 1335 m, 1335 m and 1335 m, respectively. At 19:15 on December 29, the PBL depth retrieved by CRGM was 1012.5 m, in better agreement with the actual PBL depth determined by radiosonde (1191 m), as opposed to 240 m, 1395 m and 1395 m determined by GM, LGM and NGM. The beginning of the pollution episode was on
3.4. Comparison of existing algorithms for PBL depth estimation Fig. 5 shows the vertical evolution of aerosol extinction coefficient and the temporal variations of PBL depth retrieved from four methods during the period from December 28, 2016 to January 3, 2017. Compared with other algorithms, CRGM considers the linear instability theory of gravity waves which is associated with the complex vertical distribution of aerosol to determine the PBL depth. This method is optimized for severe haze days and it is more accurate under high 72
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Fig. 4. Temporal variation of half-hourly mean (a) momentum flux (τ ), (b) sensible heat flux (H), and (c) latent heat flux (LE) during the period from December 28, 2016 to January 3, 2017 at the Agricultural Meteorological Experiment Station. The clean days and polluted days are marked in yellow and blue, respectively. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
is probably caused by air pollutants, near surface. Comparing the twomoment radiosonde-retrieved PBL depth (997 m and 761 m) on December 30 with the algorithms’ results (247.5 m, 967.5 m, 957 m, 270 m; 240 m, 870 m, 870 m, 247.5 m) illustrates that the PBL depth retrieved by LGM and NGM agrees well with the radiosonde-retrieved result. Fig. 6 shows the temporal variations of PBL depth retrieved from four methods and the Planetary Boundary Layer Height surface on
December 30. The aerosols usually were not ideally distributed because of the lack of sufficient turbulent mixing, which resulted in the accumulation of aerosol near the ground and multi-layer structure would exist within the boundary layer. This leads to the large discrepancies between the PBL depth from the CRGM and LGM &NGM. LGM and NGM looks like they perform better than the reference method CRGM in this time period. But, in the corresponding RSCS plot (not shown here) it can be seen more clearly that there is strong backscatter layer, which
Fig. 5. Vertical evolution of aerosol extinction coefficient (355 nm) and temporal variations of PBL depth retrieved from four methods. The color scale indicates the intensity of aerosol extinction coefficient, and warm colors represent stronger light scattering. The diurnal PBL depth derived from GM, LGM, NGM, and CRGM are illustrated as magenta plus, green pentagram, red circle, and black dot, respectively. Abnormal values are marked in the white circles. The radiosonde-retrieved results are marked by triangles surrounded by green solid-line. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.) 73
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Fig. 6. Temporal variations of PBL depth retrieved from four methods and the Planetary Boundary Layer Height surface on 32°N, 119°E from NCEP FNL data. The Planetary Boundary Layer Height surface on that point is illustrated as black dotted line. The diurnal PBL depth derived from GM, LGM, NGM, and CRGM are illustrated as magenta plus, green pentagram, red circle, and black dot, respectively. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
the cases with CRGM values less than 300 m, in order to avoid bias of the statistics by those cases.
32°N, 119°E from the National Center for Environmental Prediction (NCEP) Final Analysis (FNL) dataset during the period from December 28, 2016 to January 3, 2017 at 0200 LST, 0800 LST, 1400 LST, and 2000 LST, respectively. FNL data has low resolution and represents the spatial average of 1° × 1°. So the variable of planetary boundary layer height cannot be regarded as the true value, but it still has a certain reference value. From the FNL data, the overall trend of the diurnal variation of PBL depth is basically consistent with the retrieved results. However, the nocturnal PBL depth from FNL data at 2000 LST and 0200 LST often to be 22 m, which is relatively unrealistic and largely deviated from our nocturnal results. The retrieved results at night were often found to be larger than 200 m.
3.6. Case analysis In order to analyze the influence of pollution conditions on the evolution of PBL structure, we focus on the characteristics of PBL depth on December 28, 2016 (as a case of clean day) and January 1, 2017 (as a case of polluted day) in this section. 3.6.1. Clean day Fig. 8 shows the boundary layer evolution in terms of the timeheight cross-sectional observation of aerosol extinction coefficient in the 355 nm channel collected on December 28, 2016. The PBL depth changed slowly in the late night (0000–0700 LST). After sunrise, the depth of PBL shoots sharply up (around 0900 LST) and decreased slowly after sunset. The PBL deepened to a maximum value of 1560 m for CRGM at 1310 LST in the daytime (0700–1700 LST); the whole layer remained convectively unstable and well-mixed during the daytime hours. After sunset, the turbulence weakened, and the PBL depth decreased slowly. The mean PBL depth (determined from the median value to avoid bias effects by outliers) was 438.8 m for CRGM at the late night (0000–0700 LST) and it was 671.3 m in the early evening (17000000 LST). It was worth noting that the PBL depth in the early evening was larger than that at the late night. It can be explained by the natural diurnal cycle over land surfaces due to solar input of heat at the surface only during daylight hours. According to Fig. 4, the turbulent heat fluxes are much greater during the day than at night. On clean days, PBL depth had obvious characteristics of diurnal variation, relatively lower in the nighttime, relatively larger in the daytime, and reached a maximum value at noon. In the early evening, the nocturnal boundary layer was usually stable because of the negative sensible heat fluxes and near zero positive latent heat fluxes.
3.5. Statistical analysis of retrieved PBL depth Table 2 presents the three statistical indices bias, SEE, and NSEE for the other retrieved results compared with CRGM results. The bias value shows that GM tend to underestimate the PBL depth and LGM and NGM tend to overestimate with respect to CRGM. Scatterplots of the PBL depth retrieved from GM, LGM, and NGM against CRGM are given in Fig. 7. The GM is frequently biased at a value of about 250 m, which is probably due to the effect of the overlap function of the lidar. On the other hand, the CRGM reference method detects many times a strong low-height backscatter layer, while the LGM and NGM detect the top of the general aerosol layer (see also the discussion of Fig. 5 in Section 3.4 for multiple layering of aerosols). The results in Table 2 do not include Table 2 Computed Bias, Standard Error of the Estimate (SEE), and Normalized Standard Error of the Estimate (NSEE) of GM, LGM, NGM in comparison with CRGM at NUIST from December 28, 2016 to January 3, 2017. Method
Bias
SEE
NSEE
GM LGM NGM
−343.17 m 110.99 m 112.89 m
504.81 m 253.05 m 255.31 m
0.67 0.33 0.34
3.6.2. Polluted day In order to investigate the evolution of PBL depth in the polluted 74
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Fig. 7. Scatterplots of PBL depth retrieved from GM, LGM, and NGM against CRGM results during the period from December 28, 2016 to January 3, 2017.
kgm−1s−2 ) in the late night (Fig. 4) indicated a stable boundary layer structure, and the PBL maintained a lower height at night. After sunrise (around 0900 LST), H gradually increased from −7.9 to only 78.7 Wm−2 and τ slightly increased from 0.08 to 0.23 kgm−1s−2 . This made the PBL depth rise gradually to the relatively low maximum value (660 m) at 1500 LST. It can be concluded from the consistency between PBL depth and H that the small increase H (which was caused by reduction of solar radiation relatively to a clean day) was the main reason for the small development of PBL on this polluted day. After about 2100 LST, H reached 39.1 Wm−2 at 2200 LST and went down to −19.5 Wm−2 at 2230 LST.
episode, the evolution of aerosol extinction coefficient on January 1, 2017 (as a case of polluted day) and the PBL depth retrieved from the four detection methods is shown in Fig. 9 (similar to Fig. 8). The PBL deepened to a maximum value (around 660 m) at about 1500 LST in the daytime (0700–1700 LST). The maximum value of PBL depth on polluted day was on average 900 m lower than that of clean day. PBL depth was 315 m for CRGM in the early evening (1700-0000 LST) and was 236.3 m at the late night (0000–0700 LST). In addition, the diurnal variation of PBL depth on polluted day was smaller than that on clean day. The decrease of PBL depth further causes the accumulation of pollutants. There were some abnormal values between 1200 and 1500 LST retrieved by LGM and NGM that are marked in white circle. On the polluted day, the negative H and small τ (less than 0.1
Fig. 8. Same as Fig. 5 but for December 28, 2016. Sunrise and sunset times are marked by triangles. 75
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Fig. 9. Same as Fig. 5 but for January 1, 2017. Sunrise and sunset times are marked by triangles.
4. Conclusions
of PBL depth was about 660 m. In addition, the diurnal variation of PBL depth on polluted day was smaller than that on clean day. The lower PBL depth made aerosol particles to confine in a shallow PBL and prevented the vertical dispersion of aerosol particles, leading to an increase in the surface aerosol concentrations and, thus, possibly lower solar radiation at the surface and PBL growth.
During 2016–2017 winter, a high-resolution aerosol lidar, eddy covariance system and automatic meteorological station, were deployed in the north of Nanjing city with the aim of estimating boundary-layer depth and its diurnal evolution. This study compared PBL depth retrieved from GM, LGM, NGM and CRGM and the differences between these methods. Through the comparison of the evolution of PBL depth over clean day and polluted day, the boundary layer structure and PBL depth under different weather conditions can be displayed. The surface synoptic situation from NCEP FNL data showed that Nanjing was mainly driven by the cold and high pressure system which prevented the vertical diffusion of pollutants and conducted to the accumulation of pollutants during the observation period. The meteorological conditions collected at the automatic meteorological station and turbulent heat fluxes from EC system showed that the site was under the low wind regime that promoted the accumulation of pollutants at the beginning of the air pollution process. The elastic lidar data were used to show the condition of PBL and the evolution of PBL depth. Higher aerosol extinction coefficient values were obtained at lower heights than PBL depth suggesting that the haze pollution was predominantly formed and evolved inside the atmospheric boundary layer. The retrieved results by CRGM, which are based on a detection method that takes into the linear instability theory of gravity waves, were considered as the reference value of PBL depth. The PBL depth were generally up to 1600 m on clean day and were around 600 m on polluted day. Overall, the variation of PBL depth varied opposite to the daily concentration of PM2.5 and the maximum values of daily PBL depth showed a decreasing trend. The PBL depth retrieved from GM was smallest (almost 220 m) in most of the times and GM results did not agree well with PBL values calculated by the other algorithms evaluated in this study. The results of LGM and NGM were close to each other and most of the overestimating abnormal values (marked in the white circles) were from these two methods. The GM was biased at low height values probably due to the effect of the overlap function of the lidar, while the LGM and NGM could not detect low height multiple layers as the CRGM could do. On clean day, PBL depth had obvious characteristics of diurnal variation. The PBL depth was relatively lower in the nighttime, relatively larger in the daytime, and reached the maximum value at noon. On polluted day, the weak WS and τ with the stable surface situation inhibited the development of PBL depth; therefore, the maximum value
Conflicts of interest None. Acknowledgments This research was supported by grants from National Key Research and Development Program of Ministry of Science and Technology of China (2016YFC0203304), and National Natural Science Foundation of China (41875013, 41275022, and 41711530223). We are very grateful to anonymous reviewers for their careful review and valuable comments, which led to substantial improvement of this manuscript. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.atmosenv.2019.02.022. References Baars, H., Ansmann, A., Engelmann, R., Althausen, D., 2008. Continuous monitoring of the boundary-layer top with lidar. Atmos. Chem. Phys. 8, 7281–7296. Boers, R., Melfi, S.H., 1987. Cold air outbreak during MASEX: lidar observations and boundary-layer model test. Boundary-Layer Meteorol. 39 (1), 41–51. Burba, G., Schmidt, A., Scott, R., Nakai, T., Kathilankal, J., Fratini, G., et al., 2012. Calculating CO2 and H2O eddy covariance fluxes from an enclosed gas analyzer using an instantaneous mixing ratio. Glob. Chang. Biol. 18 (1), 385–399. Cohn, S.A., Angevine, W.M., 2000. Boundary layer height and entrainment zone thickness measured by lidars and wind-profiling radars. J. Appl. Meteorol. 39 (8), 1233–1247. Colello, G.D., Griver, C., Sellers, P.J., Berry, J.A., 1998. Modeling of energy, water, and CO2 flux in a temperate grassland ecosystem with SiB2: May–October 1987. J. Atmos. Sci. 55, 1141–1169. Davis, K.J., Gamage, N., Hagelberg, C.R., Kiemle, C., Lenschow, D.H., et al., 2000. An objective method for deriving atmospheric structure from airborne lidar observations. J. Atmos. Ocean. Technol. 17 (11), 1455–1468. Dupont, E., Pelon, J., Flamant, C., 1994. Study of the moist convective boundary layer structure by backscattering lidar. Boundary-Layer Meteorol. 69 (1), 1–25. Flamant, C., Pelon, J., Flamant, P.H., Durand, P., 1997. Lidar determination of the entrainment zone thickness at the top of the unstable marine atmospheric boundary layer. Boundary-Layer Meteorol. 83 (2), 247–284. Hayden, K.L., Anlauf, K.G., Hoff, R.M., Strapp, J.W., Bottenheim, J.W., et al., 1997. The
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