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Technical Note

The Influence of Temperature Inversion on the Vertical Distribution of Aerosols

by
Hui Li
1,2,3,
Boming Liu
2,*,
Xin Ma
2,
Yingying Ma
2,
Shikuan Jin
2,
Ruonan Fan
2,
Weiyan Wang
2,
Jing Fang
3,
Yuefeng Zhao
3 and
Wei Gong
1
1
School of Electronic Information, Wuhan University, Wuhan 430079, China
2
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430079, China
3
Shandong Provincial Engineering and Technical Center of Light Manipulations & Shandong Provincial Key Laboratory of Optics and Photonic Device, School of Physics and Electronics, Shandong Normal University, Jinan 250014, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2022, 14(18), 4428; https://doi.org/10.3390/rs14184428
Submission received: 9 June 2022 / Revised: 13 July 2022 / Accepted: 19 August 2022 / Published: 6 September 2022
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Versions Notes

Abstract

:
Temperature inversion plays an important role in the accumulation and diffusion of aerosols. In this study, the relationship between temperature inversion and the vertical distribution of aerosols is investigated based on Raman lidar observations taken from January 2010 to September 2015 at the Atmospheric Radiation Measurement site in the Southern Great Plains, USA. First, the diurnal and seasonal variations of the surface-based inversion (SBI) and elevated temperature inversion (EI) are investigated. The results indicate that the occurrence frequency of SBI and EI have different seasonal trends. SBI has the highest frequency in summer, while EI has the highest frequency in winter. The diurnal variation of SBI is obvious, with a higher frequency in nighttime and a lower frequency in daytime. The inversion intensity (ΔT) and inversion depth (ΔZ) of SBI and EI have consistent diurnal and seasonal trends. The effects of SBI and EI on the vertical distribution of aerosols are then analyzed. The mean aerosol optical depth (AOD) below the SBI height shows a clear seasonal variation, which is consistent with the seasonal trends of ΔT and ΔZ. This phenomenon also occurs on the AOD below EI top height. The sensitivity analysis shows that the mean AOD below SBI height or EI top height increases with an increase of the ΔT and ΔZ of SBI (EI). It indicates that ΔT and ΔZ are the key factors affecting the vertical distribution of aerosols. In addition, the variation of AOD below and above EI top height is opposite to that of AOD below and above EI bottom height under different ΔT and ΔZ conditions. The correlation coefficients between ΔT (ΔZ) of EI with AOD in EI were 0.62 (0.65). These results indicate that the space between EI bottom height and EI top height can store aerosols. The larger the ΔZ of EI, the more aerosols are stored. These findings contribute to our understanding of the effect of temperature inversion on the vertical distribution of aerosols.

Graphical Abstract

1. Introduction

Temperature inversion (TI), typically characterized by an increase of temperature with height, is a frequent feature of air pollution events [1]. When the surface temperature starts to cool, the normal temperature distribution is altered [2,3]. It leads to the formation of a warmer layer in the near surface, known as the TI [4]. The TI inhibits the flow of heat from the surface into the free air and slows down the diffusion of pollutants [5,6,7,8,9]. TIs, both surface-based inversion (SBI) and elevated inversion (EI), effectively ensnare aerosols in the boundary layer and keep them from dispersing [10,11,12].
Aerosols are a major contributor to air quality and climate change [13,14,15,16]. The vertical distribution of aerosols in dust flow over the Atlantic Ocean was studied using satellite observation data [17]. It was found that the dust plume could extend for several kilometers in altitude, up to 8 km in summer. Seasonal and spatial variations of the vertical distribution of aerosols over China were studied using long-term observations from CALIPSO (Cloud Lidar and Infrared Pathfinder Satellite Observations), ground-based lidar, and the Aerosol Robotics Network (AERONET) [18]. It was found that aerosols are easily captured by the boundary layer when the meteorological conditions are relatively stable. Over 80% of aerosols are trapped within 1.5 km from the ground in winter. The vertical distribution of aerosols over the Yangtze River Delta region was studied using ground-based lidar observations [19]. It was found that 89% of the aerosols were distributed below 2 km. By studying the vertical distribution of aerosols in Beijing between 2005 and 2006, it was found that the vertical distribution of aerosols was strongly correlated with vertical mixing and horizontal transport [20]. When both vertical mixing and horizontal transport are strong, aerosol concentration is low in the vertical direction. When vertical mixing and horizontal transport are weak, frontal inversions are prone to occur, leading to higher surface aerosol concentrations. These studies have led to a primary understanding of the vertical distribution of aerosols.
Several studies have been carried out to investigate the relationship between TI and the vertical distribution of aerosols [21,22,23,24]. One previous study indicated that winter TI was the main cause of most minor pollution events [25]. The features of TI under different weather circulation conditions and the effects of TI on aerosol distribution were discussed [26]. In addition, the statistical characteristics of surface-based inversions (SBI) and elevation inversions (EI) were also investigated separately. It was found that different weather circulation patterns alter the static stability of the atmosphere and the low-level TIs, which affect the diffusion of aerosols in the lower atmosphere. The vertical distribution of aerosols and their variation characteristics during spring haze pollution in Beijing were analyzed [27]. They found that the TI inhibited the diffusion of aerosols, and the vertical distribution of aerosols was influenced by relative humidity. Moreover, the intensity and depth of contamination, as well as the vertical distribution of aerosol particle size, were clearly different under different TI conditions. The vertical distribution of aerosols during haze pollution in Wuhan was studied [28]. It was found that stable atmospheric conditions such as strong temperature inversions and low wind speeds led to a large accumulation of pollutants below 0.5 km. The distribution of inverse temperature has a significant diurnal variation, and the aerosol has an enhanced effect on both daytime and nighttime TIs [29]. The above studies mainly reveal the effects of TI on surface aerosol accumulation. The specific factors and characteristics of TI on the vertical distribution of aerosols are still uncertain. In the present study, the effects of TI on the vertical distribution of aerosols and the main influencing factors will be explored in depth.
In this study, the relationship between the TI and the vertical distribution of aerosols is investigated using long-term Raman lidar observations from January 2010 to September 2015 at the Atmospheric Radiation Measurement (ARM) site in the Southern Great Plains (SGP), USA. The main purpose of this study was to investigate the influence of the TI and its parameters, such as inversion depth and inversion intensity, on the vertical distribution of aerosols. The results can contribute to our understanding of the vertical distribution properties of aerosols and their influencing parameters.

2. Data and Methodology

2.1. Raman Lidar Data

The aerosol extinction coefficients and temperature profiles are provided by the Raman lidar system at the ARM site in the SGP, USA (36°36′N, 97°29′W) [30,31,32]. The topographic images of the SGP and the ARM site in the SGP are shown in Figure 1. The Raman lidar emits short pulses of UV light (355 nm) into the atmosphere, collects backscattered light in the elastic (355 nm) and Raman (408 and 387 nm) channels, converts it into electric signals, and digitizes them for processing [33]. The aerosol extinction coefficient is estimated from the elastic and N2 returns, and temperature estimates are obtained from measurements of the rotational Raman spectrum near the transmit wavelength [34]. The vertical and temporal resolutions of the original aerosol extinction coefficients profiles and temperature profiles are 75 m and 10 s, respectively. The incomplete overlap region of the ARM Raman lidar is 150 m. The detailed specifications of the ARM Raman lidar system can be found in [35]. There, the Raman lidar data are gathered from January 2010 to September 2015. The extinction coefficient and temperature data provided by the Raman lidar used in this paper are S1 level data products from the SGP site. S1 level data products were briefly quality controlled and set to missing for known error values [31]. The aerosol extinction coefficients and temperature profiles are processed as hourly average profiles to reduce the effect of background noise. In addition, the erroneous data and clouds data were excluded from our analysis based on Raman measurements and quality control flags. Since the transport aerosol layers may affect the Raman lidar’s detection capabilities, the days with transport aerosol layers were removed. Notably, cases with aerosol optical depth (AOD) less than 0.1 were also excluded to guarantee a certain amount of aerosol in the atmosphere for subsequent studies. After screening, a total of 2599 h of data were obtained.

2.2. Methodology

2.2.1. Retrieval Method of TI

There are two types of TIs: SBI and EI. SBI is defined as a TI whose base is located at the surface and EI is defined as a TI whose base is above the surface. A TI is considered to have occurred when there is a value greater than zero on the temperature gradient curve. The height of SBI and EI is identified by the change in gradient of the temperature profile [36,37]. Figure 2 show the SBI case at 04:00 local time (LT) on 18 January 2010 and the EI case at 11:00 LT on 22 January 2010. Meanwhile, the profile of temperature and extinction coefficient can be found in Figure S1 in the Supplement. For SBI (Figure 2a), the location where the temperature gradient equals zero is defined as the SBI height, marked by a blue line. The height at which the temperature starts to rise (fall) is defined as the SBI bottom (top) height. The difference in temperature between the SBI bottom and top is referred to as the inversion intensity (ΔT) and the difference in height is referred to as the inversion depth (ΔZ). For EI (Figure 2b), similarly, the height at which the temperature starts to rise (fall) is defined as the EI bottom (top) height. In this case, the blue lines indicate the EI top height (EITH) and the EI bottom height (EIBH). The difference in temperature between the EI bottom and top is referred to as the ΔT and the difference in height is referred to as the ΔZ. When more than one EI is present, the height of the maximum value on the temperature gradient curve is considered as the EI height.
This method was applied to all temperature profiles for the period 2010 to 2015 to calculate the TI parameters. Figure S2 in the Supplement shows the number of observations of total profiles and TIs at different times and seasons. The red and blue bars indicate the total profiles and the TI, respectively. Figure S2 in the Supplement indicates that the number of TIs occurring at each time period is quite adequate and the sample size is sufficient to support the follow-up study.

2.2.2. Retrieval Method of AOD

This Raman lidar system can also provide the profiles of aerosol extinction coefficients. Therefore, the AOD can be obtained by integrating the extinction coefficient along the optical path between r1 and r2 [38]:
A O D = ∫ r 1 r 2 α ( r ) d r
In this study, we are concerned with the effect of TI on the vertical distribution of aerosols. Therefore, we calculated the AODs above and below SBI height, EITH, and EIBH in order to compare them with each other. The extinction coefficient at 150 m was extrapolated to the ground to eliminate the effect of overlap. For the calculation of aerosols below the inversion, r1 was set to 0 m and r2 was set to SBI height, EITH, and EIBH. For the calculation of aerosols above the inversion, r1 was set to SBI height, EITH, and EIBH, and r2 was set to 3 km.

3. Results and Discussion

3.1. Statistical Properties of TI

Figure 3 shows the diurnal variations of the observation number, frequency of occurrence, ΔT, and ΔZ of SBI and EI during January 2010 and September 2015. The diurnal variation of SBI is significant, with a higher frequency in nighttime and a lower frequency in daytime. This is due to the fact that SBI is driven by the change in the radiation balance at the surface. The enhanced radiative cooling at nighttime leads to a high frequency of SBI. [26]. The diurnal variations of ΔT and ΔZ of SBI are also investigated (Figure 3c,e). Note that the ΔT and ΔZ of SBI during 10:00 to 16:00 LT are not calculated due to the sample number of SBI being too small. It can be seen that the ΔT and ΔZ of SBI also vary obviously with time, being higher at midnight and lower at sunset (17:00–19:00 LT). This is consistent with the change of SBI occurrence frequency, which is due to the presence of a long-wave cooling effect at night. The radiative cooling at the surface reaches its maximum at nighttime, deepening the ΔT and ΔZ of the SBI [37,39]. By contrast, the diurnal variations of frequency, ΔT, and ΔZ of EI are not obvious. The frequency of occurrence of EI ranges from 40% to 60%, and the hourly mean ΔT changes around 2 K. This is due to the fact that EI is often associated with large-scale weather processes, and it lasts for several hours to several days [40]. One previous study indicated that EI is mainly controlled by weather and is weak and shallow in intensity [1]. In addition, it was found that the diurnal trend of ΔT is similar to that of ΔZ (whether EI or SBI). The correlations between ΔT and ΔZ for EI and SBI are shown in Figure S3 in the Supplement. The determination coefficient (R2) between ΔT and ΔZ for SBI and EI is 0.55 and 0.82, respectively. The results indicate that the ΔT and ΔZ are positively correlated under both SBI and EI. One previous study indicated a strong positive correlation between ΔT and ΔZ, which is consistent with our research [41].
The seasonal variation of SBI and EI is illustrated in Figure 4. It can be seen that the number and frequency of SBI and EI occurrence have different seasonal trends (Figure 4a,d). SBI occurs in the largest number and the highest frequency in the summer. Conversely, EI has the lowest frequency in the summer and the highest frequency in the winter. This is due to strong anticyclonic conditions in the summer, leading to cloudless skies and stable weather conditions [42]. It favours the production of SBI at night. During winter, the presence of warm air in the upper layers, combined with the presence of cold air in the lower layers, further predisposes the formation of EI [43]. Moreover, the seasonal trends of ΔT and ΔZ of SBI and EI are very consistent, both being strongest in the winter and weakest in the summer. It was found that SBI and EI generally exhibit strong seasonal and geographic dependencies [44]. SBI and EI are strongest in the winter, which is due to both subsidence inversions and the heating. The presence of cold high-pressure systems in wintertime predispose the air to massive downdrafts, thus enhancing TI [45]. Moreover, one previous study indicated that the TIs are shallowest in summertime and deepest in wintertime, which is consistent with our research [46].

3.2. Case Study

Figure 5 shows a case study on 27 February 2010. In this case, the TI is dominated by SBI during nighttime. At 04:00 LT (Figure 5b), the SBI height is approximately 260 m. The aerosol extinction coefficient profile shows that some aerosols are captured by SBI, but there are still many aerosols above SBI. One previous study indicated that there is a residual layer suspended on the stable boundary layer at night, and most of the aerosols remain in the nocturnal residual layer [47]. At the period of sunrise (07:00–09:00 LT), due to the incomplete heating of the surface and atmosphere, the SBI and EI appear at the same time. From the temperature profile at 8:00 LT (Figure 5c), the SBI height, EIBH, and EITH are 260, 420, and 560 m, respectively. It can be found that most of aerosols are concentrated below EIBH, and almost all of the aerosols can be captured by the top of EI. This phenomenon is more obvious during the daytime (10:00–17:00 LT). During the daytime, the SBI disappears and only EI exists. It can be found that most of the aerosols are concentrated below EITH, and there is a slight fraction of aerosols above EI. The temperature and extinction coefficient profiles at 12:00 LT (Figure 5d) and 17:00 LT (Figure 5e) also prove this. Meanwhile, it can be observed from the extinction coefficient profile that aerosols can be stored between EIBH and EITH (Figure 5c–e). From these results, it can be assumed that the SBI cannot capture all aerosols, while EI can capture almost of them.
To verify this hypothesis, more cases under complex TI conditions are shown in Figure 6. These cases can be divided into two categories. For the first category, the majority of aerosols can be captured by TI, even there is only SBI (Figure 5a). Under some multiple TIs conditions, even if the aerosols are lifted to 1–2 km, the highest EI can also capture most of the aerosols (Figure 6b,c). In contrast, some cases indicate that the TI does not always capture aerosols completely. The SBI, EI, or even multiple EI cannot limit aerosols below the TI (Figure 6d–f). Unfortunately, these phenomena indicate that the assumption in the previous paragraph is not tenable. This creates a basic curiosity to investigate the effect of TI on the vertical distribution of aerosols.

3.3. Effect of TI on the Vertical Distribution of Aerosols

Figure 7 illustrates the AOD above and below SBI height, EITH and EIBH under different seasons. For the SBI, the mean AOD below the SBI height shows a clear seasonal variation, which is highest in winter and lowest in spring. In contrast, the mean AOD above SBI is relatively small in the autumn and winter and larger in the spring and summer. This may be due to the seasonal variations in ΔT and ΔZ of SBI. During winter, the largest ΔZ provides enough space for aerosols, and the largest ΔT can effectively limit the aerosol below the SBI. One previous study also indicated that the effect of SBI on surface aerosol concentrations was strongest in the winter and weakest in the summer [26]. For EI, the seasonal variation of mean AOD below EITH is similar with that bellow EIBH, which is high in the spring and summer and low in the autumn and winter. Moreover, the seasonal variation of mean AOD above EITH and EIBH is also similar, lower in the summer and autumn and higher in the winter and spring. The difference is that the mean AOD below EITH during winter is larger than that above EITH (Figure 7b), while the mean AOD below EIBH is consistent with that above EIBH during winter (Figure 7c). Combined with the seasonal variation in ΔT and ΔZ of EI, this infers that there are some aerosols in the space from the EIBH to EITH. These results show that the vertical distribution of aerosol is closely related to the ΔT and ΔZ of TI.
Figure 8 shows the AOD above and below SBI height, EITH, and EIBH, under different ΔT and ΔZ conditions. For SBI, the mean AOD below SBI height increases with the increase of ΔT and ΔZ. Conversely, the mean AOD above SBI height shows a decreasing trend with the increase of ΔT and ΔZ. This indicates that a strong SBI has an obvious inhibitory effect on surface aerosol. Similarly, the mean AOD below EITH increases with the increase of ΔT and ΔZ, while the mean AOD above EITH decreases with the increase of ΔT and ΔZ. Although the change trend is not very obvious, the EI top can capture more aerosols with the increase of ΔT and ΔZ. Previous studies also indicated that ΔT and ΔZ are the key determinants for the aerosol capture capacity of TI, and aerosol loading below the TI increases with the ΔT and ΔZ of the TI [41,48]. Moreover, it should be noted that the aerosol loading above EITH is relatively large, even when ΔT and ΔZ are larger. This is due to the effect of multiple EIs. When there are multiple EIs, the top of strongest EI is defined as EITH. Some aerosols still appear above this EITH, but are captured by the highest EI (Figure S4 in the Supplement). In addition, the variation of AOD above and below EIBH is opposite to EITH. With an increase of ΔT and ΔZ, the AOD below EIBH shows a downward trend, while the AOD above EIBH shows an upward trend. The large ΔZ of EI represents the large space between EIBH and EITH. This space from the EIBH to EITH stores aerosols, leading to a low AOD below EIBH and a large AOD above EIBH.
To quantify the effects of ΔT and ΔZ on the vertical distribution of aerosols, the correlation of ΔT and ΔZ with AOD in SBI and EI are shown in Figure 9. The asterisks indicate a passing of the significance test (p < 0.05). For SBI, the correlation coefficients between ΔT (ΔZ) and AOD in SBI are 0.39 (0.65). This confirms that the ΔT and ΔZ of SBI have an inhibitory effect on surface aerosol. Moreover, the correlation coefficients between ΔT (ΔZ) with AOD in EI are 0.62 (0.65). The AOD in EI means the integral of the extinction coefficient between EIBH and EITH. These results confirm that the space between EIBH and EITH can store aerosols. Overall, for both SBI and EI, the ΔT and ΔZ are the keys to determining the vertical distribution of aerosols. The lager the ΔT and ΔZ, the more aerosols are suppressed below TI [41,49]. In addition, two points need to be highlighted when discussing the relationship between TI and the vertical distribution of aerosols. One is that, although the AOD below the SBI increases with the increase of ΔT and ΔZ, the aerosol contained in SBI is very limited. The AOD above SBI is larger than that below SBI, even under strong ΔT conditions. Another point is that the TI height needs to be reasonably defined. Choosing the EIBH or EITH as the TI height to count the AOD content above and below TI will lead to completely opposite statistical results.

4. Conclusions

In this study, the relationship between the TI and the vertical distribution of aerosols is investigated based on Raman lidar observations from January 2010 to September 2015 at the ARM site in the SGP, USA. The diurnal and seasonal variation characteristics of TI are analysed. The effects of TI on the vertical distribution of aerosols are analysed in detail.
The main conclusions of this paper are as follows: first, the diurnal variations of the observation number, frequency of occurrence, ΔT, and ΔZ of SBI are significant. They show a higher frequency during the nighttime and a lower frequency during the daytime. By contrast, these characteristics of EI are not obvious. The number and frequency of SBI and EI occurrences have different seasonal trends. SBI occurs in the largest number and the highest frequency in the summer. On the contrary, EI has the lowest frequency in the summer and the highest frequency in the winter. ΔT and ΔZ of SBI and EI have consistent diurnal and seasonal trends, and they are positively correlated under both SBI and EI. Then, the effects of SBI and EI on the vertical distribution of aerosols are further analysed. It is found that the mean AOD below the SBI height shows a clear seasonal variation, which is highest in the winter and lowest in the spring. This is consistent with the seasonal variation in ΔT and ΔZ of SBI. Meanwhile, the mean AOD below SBI height and EITH increase with the increase of ΔT and ΔZ. For SBI, the correlation coefficients between ΔT (ΔZ) and AOD are 0.39 (0.65). The results indicate that, for both SBI and EI, ΔT and ΔZ are the key factors for determining the vertical distribution of aerosols. The larger the ΔT and ΔZ, the more aerosols are suppressed below TI. In addition, it is found that the average AOD below EITH in wintertime was larger than that above EITH, while the average AOD below EIBH in wintertime was consistent with that above EIBH. With the increase of ΔT and ΔZ, the AOD below EIBH showed a decreasing trend, while the AOD above EIBH showed an increasing trend. Moreover, the correlation coefficients between ΔT (ΔZ) with AOD in EI are 0.62 (0.65). This indicates that the space between EIBH and EITH can store aerosols. The larger the ΔZ, the more aerosols are stored.
This study investigated the effect of SBI and EI on the vertical distribution of aerosols based on Raman lidar observations from January 2010 to September 2015 at the ARM site in the SGP, USA. Regarding SBI, it can suppress more aerosol below the SBI height with an increase of the ΔT and ΔZ of SBI, but the aerosol contained in SBI is very limited. For EI, most of the aerosols are suppressed below EITH rather than EIBH. This is due to the fact that most aerosols can be stored within the TI. These findings contribute to our understanding of the TI and the vertical distribution of aerosols.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/rs14184428/s1, Figure S1: The vertical profile of temperature (black) and extinction coefficient profile (red) ob-served on (a) 18 January 2010 at 04:00 local time and (b) 22 January 2010 at 11:00 local time (b) over Southern Great Plains site. Blue line represents the different layer height; Figure S2: Total profiles (red bars) and the observation number of the temperature inversion (blue bars) under different (a) time and (b) season; Figure S3: Relationship between ΔT and ΔZ for (a) SBI and (b) EI; Figure S4: The vertical profile of extinction coefficient (black) and temperature profile (red) ob-served on 22 December 2010 at 17:00 LT over SGP site. Blue dotted lines represent the EITH.

Author Contributions

Conceptualization, H.L. and B.L.; methodology, B.L.; software, H.L.; validation, S.J., W.W., X.M. and R.F.; formal analysis, J.F.; resources, W.G. and Y.Z.; data curation, Y.M. and X.M.; writing-original draft preparation, H.L.; writing-review and editing, B.L.; visualization, H.L. and B.L.; supervision, W.G.; project administration, Y.M.; funding acquisition, B.L. Investigation, H.L., S.J., W.W. and R.F. All authors have read and agreed to the published version of the manuscript.

Funding

This work was financially supported by the National Natural Science Foundation of China under Grant 42001291 and 62002208, the Project through the China Postdoctoral Science Foundation under Grant 2020M682485, and the Natural Science Foundation of Shandong Province under Grant ZR2020MA082. The Key Research and Development Project of Hubei Province (2022BCA057, 2021BCA216).

Data Availability Statement

The data used in this work were made available by the Atmospheric Radiation Measurement (ARM) Program sponsored by the U.S. Department of Energy (DOE). No new data were created or analyzed in this study. Data sharing is not applicable to this article.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Study area of (a) Southern Great Plains, USA; and (b) Atmospheric Radiation Measurement site in the Southern Great Plains, USA.
Figure 1. Study area of (a) Southern Great Plains, USA; and (b) Atmospheric Radiation Measurement site in the Southern Great Plains, USA.
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Figure 2. The vertical profile of temperature (black) and temperature gradient profile (red) observed on (a) 18 January 2010 at 04:00 local time and (b) 22 January 2010 at 11:00 local time (b) over the Southern Great Plains site. The blue lines represent the different height layers.
Figure 2. The vertical profile of temperature (black) and temperature gradient profile (red) observed on (a) 18 January 2010 at 04:00 local time and (b) 22 January 2010 at 11:00 local time (b) over the Southern Great Plains site. The blue lines represent the different height layers.
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Figure 3. The diurnal variation of (a) observation number, (c) inversion intensity, and (e) inversion depth for SBI. The diurnal variation of (b) observation number, (d) inversion intensity, and (f) inversion depth for elevated temperature inversion. The black line represents the frequency of occurrence.
Figure 3. The diurnal variation of (a) observation number, (c) inversion intensity, and (e) inversion depth for SBI. The diurnal variation of (b) observation number, (d) inversion intensity, and (f) inversion depth for elevated temperature inversion. The black line represents the frequency of occurrence.
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Figure 4. The seasonal variation of (a) observation number, (b) inversion intensity, and (c) inversion depth for SBI. The seasonal variation of (d) observation number, (e) inversion intensity, and (f) inversion depth for elevated temperature inversion. The black line represents the frequency of occurrence.
Figure 4. The seasonal variation of (a) observation number, (b) inversion intensity, and (c) inversion depth for SBI. The seasonal variation of (d) observation number, (e) inversion intensity, and (f) inversion depth for elevated temperature inversion. The black line represents the frequency of occurrence.
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Figure 5. Case study on 27 February 2020: (a) time-height cross-section of extinction coefficient. Red star, red positive and inverted triangles represent surface-based inversion height, elevated temperature inversion bottom height and elevated temperature inversion top height, respectively. The vertical profile of extinction coefficient (black) and temperature profile (red) observed at (b) 04:00, (c) 08:00, (d) 12:00, and (e) 17:00 local time over the Southern Great Plains site. The blue dotted lines represent the different height layers.
Figure 5. Case study on 27 February 2020: (a) time-height cross-section of extinction coefficient. Red star, red positive and inverted triangles represent surface-based inversion height, elevated temperature inversion bottom height and elevated temperature inversion top height, respectively. The vertical profile of extinction coefficient (black) and temperature profile (red) observed at (b) 04:00, (c) 08:00, (d) 12:00, and (e) 17:00 local time over the Southern Great Plains site. The blue dotted lines represent the different height layers.
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Figure 6. The vertical profile of extinction coefficient (black) and temperature profile (red) observed on (a) 8 February 2010 at 02:00 local time, (b) on 12 April 2010 at 23:00 local time, (c) on 17 February 2010 at 11:00 local time, (d) on 27 February 2010 at 05:00 local time, (e) on 13 February 2010 at 13:00 local time, and (f) on 22 December 2010 at 01:00 local time over the Southern Great Plains site. Blue dotted lines represent the different layer height.
Figure 6. The vertical profile of extinction coefficient (black) and temperature profile (red) observed on (a) 8 February 2010 at 02:00 local time, (b) on 12 April 2010 at 23:00 local time, (c) on 17 February 2010 at 11:00 local time, (d) on 27 February 2010 at 05:00 local time, (e) on 13 February 2010 at 13:00 local time, and (f) on 22 December 2010 at 01:00 local time over the Southern Great Plains site. Blue dotted lines represent the different layer height.
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Figure 7. The Aerosol optical depth above and below (a) surface-based inversion height, (b) elevated temperature inversion top height, and (c) elevated temperature inversion bottom height under different seasons. The blue and orange boxes represent the aerosol optical depth below and above temperature inversion, respectively.
Figure 7. The Aerosol optical depth above and below (a) surface-based inversion height, (b) elevated temperature inversion top height, and (c) elevated temperature inversion bottom height under different seasons. The blue and orange boxes represent the aerosol optical depth below and above temperature inversion, respectively.
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Figure 8. The Aerosol optical depth above and below (a) surface-based inversion height, (c) elevated temperature inversion top height, and (e) elevated temperature inversion bottom height under different inversion intensity conditions. The Aerosol optical depth above and below (b) surface-based inversion height, (d) elevated temperature inversion top height, and (f) elevated temperature inversion bottom height under different inversion depth conditions. The blue and orange boxes represent the aerosol optical depth below and above temperature inversion, respectively.
Figure 8. The Aerosol optical depth above and below (a) surface-based inversion height, (c) elevated temperature inversion top height, and (e) elevated temperature inversion bottom height under different inversion intensity conditions. The Aerosol optical depth above and below (b) surface-based inversion height, (d) elevated temperature inversion top height, and (f) elevated temperature inversion bottom height under different inversion depth conditions. The blue and orange boxes represent the aerosol optical depth below and above temperature inversion, respectively.
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Figure 9. The correlation of (a) ΔT with AOD in SBI, (b) ΔZ with AOD in SBI, (c) ΔT with AOD in EI, (d) ΔZ with AOD in EI. The gray, black line and color bar indicate the linear fitting curve, error bars and the data density, respectively. The asterisks indicate that it passed the significance test (p < 0.05).
Figure 9. The correlation of (a) ΔT with AOD in SBI, (b) ΔZ with AOD in SBI, (c) ΔT with AOD in EI, (d) ΔZ with AOD in EI. The gray, black line and color bar indicate the linear fitting curve, error bars and the data density, respectively. The asterisks indicate that it passed the significance test (p < 0.05).
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Li, H.; Liu, B.; Ma, X.; Ma, Y.; Jin, S.; Fan, R.; Wang, W.; Fang, J.; Zhao, Y.; Gong, W. The Influence of Temperature Inversion on the Vertical Distribution of Aerosols. Remote Sens. 2022, 14, 4428. https://doi.org/10.3390/rs14184428

AMA Style

Li H, Liu B, Ma X, Ma Y, Jin S, Fan R, Wang W, Fang J, Zhao Y, Gong W. The Influence of Temperature Inversion on the Vertical Distribution of Aerosols. Remote Sensing. 2022; 14(18):4428. https://doi.org/10.3390/rs14184428

Chicago/Turabian Style

Li, Hui, Boming Liu, Xin Ma, Yingying Ma, Shikuan Jin, Ruonan Fan, Weiyan Wang, Jing Fang, Yuefeng Zhao, and Wei Gong. 2022. "The Influence of Temperature Inversion on the Vertical Distribution of Aerosols" Remote Sensing 14, no. 18: 4428. https://doi.org/10.3390/rs14184428

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