Our knowledge is still poor regarding the response of the precipitation vertical
structure to aerosols, partly due to the ignorance of precipitation occurring
at different spatial scales. A total of 6 years of collocated ground-based PM
Clouds and their interactions with aerosols (solid or liquid particles suspended in the atmosphere) have been documented as one of the largest sources of uncertainty for climate (Boucher et al., 2013). Therefore, a better understanding of aerosol–cloud interactions will not only help us to understand and forecast our climate much better, but also enable us to simulate the weather systems more accurately (Seinfeld et al., 2016; Jiang et al., 2017). Despite many challenges and uncertainties, there are increasing observational evidences for the aerosol-induced changes on clouds and precipitation properties (e.g., Koren et al., 2005; Rosenfeld et al., 2008; Li et al., 2011; Guo et al., 2014; Altaratz et al., 2014; Lee et al., 2016; Fan et al., 2016, 2018), as recently reviewed by Tao et al. (2012) and Li et al. (2017). On one hand, by absorbing and scattering solar radiation, aerosols can cool the surface and heat the atmosphere nearby, which leads to a more stabilized lower atmosphere and much suppressed clouds and precipitation (Hansen et al., 1997; Liu et al., 2018). This effect is termed as the aerosol radiative effect. On the other hand, by acting as cloud condensation nuclei (CCN) and ice nuclei (IN) (Andreae et al., 2009), aerosols can initiate clouds with more but smaller cloud droplets and a narrower size distribution (Squires et al., 1958; Twomey et al., 1977), which affects the subsequent cloud microphysical processes, changes the thermodynamic and dynamic conditions, and thus influences precipitation (Koren et al., 2005; Rosenfeld et al., 2008; Fan et al., 2018; Lin et al., 2018). This effect is also termed as aerosol microphysical effects.
Convective invigoration has been suggested in ample studies that both the height (Williams et al., 2002; Andreae et al., 2004; Koren et al., 2005; Jiang et al., 2008; Rosenfeld et al., 2008; Li et al., 2011; van den Heever et al., 2011; Fan et al., 2013) and fraction (Fan et al., 2013; Yan et al., 2014) of deep convective clouds increase with aerosol loading, thereby leading to stronger storms in polluted environments. At the same time, the inhibition of light precipitation by aerosols has also been reported in different regions of the world (Kaufman and Fraser, 1997; Rosenfeld and Lensky, 1998; Rosenfeld and Givati, 2006; Wang et al., 2011; Guo et al., 2014). The invigoration theory was recently generalized by Fan et al. (2018) and can also occur for shallower water clouds under extremely clean conditions, under which ultra-fine mode aerosol particles may be nucleated to release latent heat to fuel cloud development. While we have come a long way in understanding the mechanisms behind various observation-based findings, the impacts of aerosol on precipitation remain a daunting task (Tao et al., 2012). Failure in fully understanding and accounting for these effects may not only undermine our understanding of the earth's climate and its changes (IPCC, 2013), but also impair the accuracy of rainfall forecast by a numerical weather model (Jiang et al., 2017).
The net effects of aerosols on precipitation are strongly influenced and confounded by atmospheric dynamic and thermodynamic conditions, such as updraft strength (Koren et al., 2012; Tao et al., 2012; Guo et al., 2016a), wind shear (Fan et al., 2009), and atmospheric instability (Khain et al., 2004). Consequently, aerosols can indirectly modify the vertical profiles of hydrometeors and cloud phases, which can, in turn, alter the dynamics and thermodynamics of a precipitating cloud system through latent heat release (Heiblum et al., 2012). Also, the relationships between aerosols and precipitation vary significantly on seasonal and spatial scales (Huang et al., 2009a, b). It has been a great challenge to single out the aerosol effects, largely due to various processes influencing precipitation, radiation, and even the state of the atmosphere that is induced by aerosols.
The three-dimensional (3-D) structures of radar echoes, which are determined by a combination of dynamic, thermodynamic, and cloud microphysical processes, are known as a good way to represent details inside precipitating systems (Zipser and Lutz, 1994; Yuter and Houze, 1995; Min et al., 2009; Chen et al., 2016). Any systematic changes in precipitation vertical structure as aerosol varies may provide new insights into the mechanism underlying the aerosol–cloud–precipitation interaction (Koren et al., 2009; Heiblum et al., 2012; Chen et al., 2017). Indeed, the deployment of the cloud profiling radar onboard CloudSat has led to new insights into the response of clouds to aerosols (e.g., Nakajima et al., 2010; Suzuki et al., 2010; Chen et al., 2016; Peng et al., 2016). To the best of our knowledge, however, few studies have ever used the precipitation radar (PR) to analyze the association of the vertical structure of precipitation with aerosol in China.
Given the dominant effects of atmospheric dynamics on synoptic-scale precipitation systems, only precipitation events occurring on a local scale are examined in detail in the following sections. This consideration is largely due to the point-based nature of ground aerosol measurements and the strong susceptibility of the localized precipitating system to aerosol pollution (Fan et al., 2007; Lee et al., 2012; Guo et al., 2017). The goal of this study is to investigate the influence of aerosols on the vertical structure of different localized precipitation regimes by examining a large amount of collocated measurements from the precipitation radar onboard the Tropical Rainfall Measuring Mission (TRMM) and ground-based in situ aerosol measurements made in the Pearl River Delta (PRD) region of southern China. We will examine differences in the vertical structure of precipitation between clean and polluted atmospheric environments to determine whether they are consistent with some previously proposed mechanisms governing aerosol invigoration or suppression of precipitation.
The rest of this paper proceeds as follows. The study area, datasets, and methods used here are described in Sect. 2. How to discriminate between synoptic-scale and localized precipitating systems, the potential aerosol-induced changes in the vertical structure of different precipitation regimes, and their dependences on meteorological conditions are discussed in Sect. 3. Finally, the main findings of this study are summarized in Sect. 4.
The study area is mainly over the PRD region (bounded by 113
and 115
Specifications from TRMM-PR-retrieved precipitation, China National Environmental Monitoring Center (CNEMC) in situ measured
PM
Spatial distributions of
The datasets used here are listed in Table 1 and are briefly described here. Notably, 6 years (from 1 January 2007 to 31 December 2012, unless noted otherwise) of precipitation measurements from the TRMM PR (version 7, Huffman et al., 2007), combined with collocated aerosol data collected at ground surface, and meteorological data from the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim reanalysis (Dee et al., 2011) are analyzed here. Prior to further explicit observational analyses, the spurious signals likely resulting from measurement uncertainties should be firstly considered, such as the misclassification of rain profiles, abnormal observations, and so on. To minimize such uncertainties, we screen the aerosol and precipitation observational data very carefully, which will be detailed as follows.
The precipitation properties are obtained from the TRMM PR products 2A25 and
3B42 (Huffman et al., 2007). For each rain profile, the information of
the category, attenuation-corrected reflectivity (
Given the difficulties in obtaining large-scale CCN concentration information, we have to resort to any CCN proxy such as satellite-derived aerosol optical depth (AOD) and the aerosol index (AI), or ground-based particulate matter (PM) measurements. Sound correlations have been extensively documented between (i) satellite retrievals of AOD and (ii) cloud and precipitation properties (e.g., Koren et al., 2005, 2012; Huang et al., 2009b). Such correlations, however, are susceptible to various uncertainties arising from cloud contamination and the dependence of AOD on certain atmospheric components like water vapor (e.g., Li et al., 2009; Boucher and Quaas, 2013). Moderate Resolution Imaging Spectroradiometer (MODIS) AOD products are available for less than 30 % of the time over the PRD region (Wang et al., 2015). AI, defined as the product of AOD and the Ångström exponent, has been reported as a better proxy than AOD to quantify CCN concentration due to its ability to weight AOD measurements towards the fine mode (Nakajima et al., 2001). The Ångström exponent is restricted over oceans because of its large uncertainties over land (Levy et al., 2010), so large uncertainties will arise when using AOD or AI as a proxy for CCN (Andreae, 2009). These uncertainties can be reduced by applying the method proposed by Liu and Li (2014). However, the most serious problem in using AOD as a proxy for CCN lies in the fact that AOD is only measurable under cloud-free conditions and is subject to various retrieval errors, as critically reviewed by Li et al. (2009).
Given the aforementioned considerations, we choose to use the rich dataset
of ground-based PM
Due to the meteorological factors influencing simultaneously aerosol
concentration and precipitation, it will be more feasible if the
investigation of the co-variation of aerosol and precipitation is considered
under similar meteorological conditions. A variety of
meteorological variables will be used here for scaling out the aerosol
effect on precipitation, including vertical velocity (
Statistics describing the three precipitation regimes analyzed in
the study. The critical PM
As mentioned above, three precipitation regimes (i.e., shallow, stratiform,
and convective) are directly derived from the TRMM 2A25 product. We only
consider cases with simultaneously available measurements of both PM
To highlight the aerosol effect on the vertically evolving process of
precipitation, TRMM-PR-observed
The bulk precipitation system parameter called the reflectivity center of
gravity (ZCOG) is used to represent the vertically weighted reflectivity
distribution (Chen et al., 2016). The ZCOG can cancel out any systematic
reflectivity biases throughout the vertical profile, indicates the height
where the great
Generally speaking, synoptic-scale precipitation involves frontal passages
or low-pressure systems, as compared with localized precipitation
characterized by thermal-driven convective clouds fed by the boundary layer
air (aerosol). Our recent study (Guo et al., 2017) indicates that localized
precipitation events are more closely linked to aerosol compared with
synoptic-scale precipitation. In order to make sure that only precipitating
systems more susceptible to the boundary layer aerosol were considered, all
the satellite scenes with synoptic-scale precipitation were excluded. The
discrimination between localized and synoptic-scale precipitation events for
a given day largely relies on the weather composite charts, where the daily
averaged wind field at 850 hPa was overlaid with the geopotential height at
500 hPa. Particularly, the localized precipitation event for a given day was
subjectively determined as follows: (1) there exist favorable atmospheric
conditions for the initiation and development of localized precipitation
events through visual interpretation of the weather composite plot for the
day analyzed, (2) the minimum rainfall greater than 0.1 mm d
Spatial distribution of the wind field at 850 hPa pressure level
(black arrows, vector), superimposed by geopotential height at 500 hPa
pressure level (blue lines) averaged on 26 June 2008
Figure 2 illustrates two typical weather plots, corresponding to synoptic-scale and localized precipitation events. On 26 June 2008, the PRD region lies at the bottom of the weak low pressure at 500 hPa level (Fig. 2a). At 850 hPa level, there is a weak cyclone on the left-forward side of PRD, where a southwestern to northeastern low-level jet stream overpasses at the same time, leading to strong water vapors advected over PRD from the South China Sea. More importantly, the wind shear observed at 850 hPa is most favorable for the formation and evolution of precipitation. Overall, the weather patterns at both 500 and 850 hPa help the onset and development of large-scale convection, so this precipitation event that occurred over PRD can be thought of as a typical synoptic-scale precipitation event. In contrast, PRD is largely controlled by the subtropical high-pressure areas, in combination with the anticyclone systems at low levels on 2 July 2008, as shown in Fig. 2b. This precipitation event can be attributed to localized thermal convection with high confidence. As such, all of the localized precipitation events have been retrieved using these visual assessment methods, which are then used for further aerosol–precipitation interaction below.
In this section, the possible aerosol effect on localized precipitation is
investigated. Precipitation enhancement or inhibition by aerosols is
examined by comparing
Joint occurrence frequency of PM
A few recent studies (Koren et al., 2014; Wang et al., 2015) have shown that
less developed cloud and precipitation are very sensitive to aerosol when
the atmosphere transitions from pristine to slightly polluted conditions.
Therefore, more focus is on the initial stage of atmospheric pollution,
and then on seeing how the occurrence frequency covaries with aerosol and
The vertical structure of precipitation (in the form of radar reflectivity)
to some extent represents the convective intensity and precipitation
microphysics of a precipitation system (Zipser and Lutz, 1994; Yuan et al.,
2011). Due to the intrinsic dependence of
Figure 5 shows the differences in vertical profiles of the frequency of
occurrence (OF) of
The differences of normalized contoured frequency by altitude
diagram (
As expected, convective precipitation is more vertically developed than
shallow and stratiform precipitation regimes. For shallow precipitation
(Fig. 5a), the
NCFAD showing the differences in the occurrence frequency for
detected convective precipitation echoes (polluted minus clean) for
Convective precipitation has a totally different
Given the relatively huge intensity of convective precipitation and its
severe socioeconomic impact, further analyses are performed for the convective
precipitation regime by separately considering three different precipitation
intensities associated with convective precipitation (light, moderate, and
heavy convections defined in Sect. 2.3.1). Figure 6 shows the
Occurrence frequencies (OF) of top height that the 30 dBZ radar echo
can reach of
Statistics describing the mean top height that 30 dBZ radar echoes
can reach under polluted and clean conditions for different precipitation
regimes. The numbers in italics indicate that the differences between
polluted and clean conditions are statistically significant at the 95 %
confidence level according to the two-tailed Student's
The enhancement of 30 dBZ reflectivity above the freezing level is often
associated with larger ice particles and more supercooled liquid water
contents (Zipser, 1994). Therefore, another way of ascribing internal
The results shown in Figs. 4, 5, and 7 along with Table 3 all point to a possible invigoration (suppression) effect for convection (stratiform) precipitation regimes, which may be partly due to the aerosol radiative, microphysical, or combined effect on the vertical development of various precipitation systems (Liu et al., 2018). But at this stage, such influence cannot be attributed to aerosols alone. Therefore, further analyses on the dependence of aerosol–precipitation interactions on meteorology will be performed in the following section.
The aerosols and precipitating systems are reported to be simultaneously
influenced by the meteorology, which is also dubbed as a buffered system due
to the complex feedback between them (Stevens and Feingold, 2009).
Therefore, the aerosol microphysical effects may not entirely account for
the systematically
The differences of rain top height (
Figure 8 shows the difference of RTHs and ZCOGs between clean and polluted conditions
as functions of
A closer look at Fig. 8 reveals that stratiform and convective regimes have larger differences in terms of RTH and ZCOG, as compared with shallow precipitation. In addition, the differences in RTH can be easily detected for both stratiform and convective precipitation regimes, unlike the observed differences in ZCOG under polluted and clean conditions. No obvious positive difference can be observed in shallow precipitation, except for a subtle elevated RTH and ZCOG observed under high CAPE conditions.
When the atmosphere becomes thermodynamically stable (positive
Most of the previous observational studies analyze the impact of aerosol on the
bulk properties of cloud and precipitation based on the cloud or
precipitation properties chiefly from passive sensors, along with
meteorological data. This study establishes some contemporaneous
relationships between radar echo and aerosol over the Pearl River Delta
(PRD) region using TRMM precipitation radar (PR) reflectivity (
Concerning the mean joint frequency of occurrence for each PM
Due to the fundamental role of convective precipitation in the hydrological
cycle, the aerosol microphysical effect on convective precipitation has been
further examined with regard to convective precipitation intensity (i.e.,
light, moderate, and heavy convective precipitation). As expected, the
The results presented here provide some sound but not unequivocal evidence of the possible impact of aerosol on the vertical structures of three different precipitation regimes, due to the inherent aerosol–meteorology–precipitation dilemma. The relationships between changes in TRMM PR reflectivity and aerosol perturbations are statistically significant and generally consistent with the existing theories, but they may be subject to different interpretations concerning the underlying physical processes. Confirming or negating any causes with confidence would require a much more detailed knowledge of the cloud processes than the satellite observation used here and should be further aided by model simulations of aerosol–cloud–precipitation interactions.
The reanalysis data were from ECMWF (European Centre for
Medium-Range Weather Forecasts), which is available at
The supplement related to this article is available online at:
JG, HL and ZL proposed the essential research idea. JG and HL performed the analysis and drafted the manuscript. ZL, DR and JHJ provided useful comments. All the authors contributed to the interpretation and discussion of results and the revision of the manuscript.
The authors declare that they have no conflict of interest.
The authors would like to acknowledge NASA for making the TRMM precipitation radar satellite datasets publicly accessible, as well as the NASA-sponsored Jet Propulsion Laboratory, California Institute of Technology, for support. This study was supported by the Ministry of Science and Technology of China (grant 2017YFC1501401), the National Natural Science Foundation of China (grants 91544217, 41771399 and 41471301), the Chinese Academy of Meteorological Sciences (grant 2017Z005), and the US National Science Foundation (AGS1534670) and Department of Energy (DE-SC0018996). Edited by: Jianping Huang Reviewed by: two anonymous referees