Long-term in situ measurements of the cloud-precipitation microphysical properties over East Asia

Atmospheric Research 102 (2011) 206–217

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Long-term in situ measurements of the cloud-precipitation microphysical properties over East Asia Jinfang Yin a, b, Donghai Wang b,⁎, Guoqing Zhai a a b

Department of Earth Science, Zhejiang University, Hangzhou, 310027, China State Key Laboratory of Severe Weather (LaSW), Chinese Academy of Meteorological Sciences, Beijing, 100081, China

a r t i c l e

i n f o

Article history: Received 29 October 2010 Received in revised form 8 July 2011 Accepted 11 July 2011 Keywords: Cloud-precipitation Microphysical properties Particles size distribution

a b s t r a c t A database of cloud-precipitation microphysical characteristics is established, using in situ data during 1960–2008. Main features of aerosol, ice nuclei (IN), cloud droplet, fog, ice crystal, snow crystal, and raindrop are presented based on the analyses of the database. In addition, a statistical analysis has been performed. The results show that the overall average aerosol concentration in diameter greater than 0.3 μm is 166.9 cm −3 and the average maximum values of IN concentration can reach 78.9 L −1 at − 20 °C, with an overall average of 22.9 L−1. In addition, cumuliform clouds have higher overall average cloud droplet number concentration (Nc) of 907.7 cm −3, and that of stratiform clouds, is 120.9 cm −3; cumuliform clouds (stratiform clouds) have an average liquid water content (LWC) of 0.875 (0.140) g m−3, with a peak value of 2.000 (0.520) g m−3. The gamma size distributions are shown to be suitable for most of the observed spectra in stratiform clouds. Both the exponential and gamma size distributions are applicable to fit the raindrops originating from stratiform clouds. Good agreement is obtained when the gamma size distribution is applied to fit the raindrops originating from both convective and mixing (stratiform and cumuliform) clouds. The exponential size distributions are suitable for both ice crystal and snow crystal fitting. © 2011 Elsevier B.V. All rights reserved.

1. Introduction Clouds cover almost two thirds of the global surface at any given time, which exerts an enormous influence on weather and climate. Clouds reflect incoming shortwave solar radiation back to the space, which cools the Earth's atmosphere. On the other hand, clouds warm the Earth surface by absorbing and reemitting longwave radiation. Besides, Wang et al. (2010a) employed a two-dimensional (2D) cloud-resolving model to conduct a series of sensitivity experiments and suggested that cloud radiative has strong effects on responses of rainfall to large-scale forcing. Additionally, clouds play an important role in Earth's water cycle as the intermediate media between water vapor that evaporates from the surface and cools the surface and precipitation that heats the atmosphere and returns moisture ⁎ Corresponding author. Tel.: + 86 10 68407136; fax: + 86 10 62176430. E-mail address: [email protected] (D. Wang). 0169-8095/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.atmosres.2011.07.002

back to the surface (e.g., Gao et al., 2006). Latent heating and cooling associated with clouds modify atmospheric circulations, which contribute to surface rainfall rate (Wang et al., 2009a, 2010b), and thunder clouds produce lighting, which all are closely related to these associated processes depend on cloud microphysics (Baker, 1997; Pineda et al., 2011). Thus, good knowledge of the microphysical properties will lead to a better understanding of microphysical processes in clouds, which is fundamental for model prediction of weather and climate. Numerical models are currently widely used for weather and climate predictions and researches (e.g., Wang et al., 2009b; Sokol et al., 2009; Fiori et al., 2011). However, cloudprecipitation processes are still poorly represented in models so far. Cloud processes and related feedbacks have been confirmed to cause the largest uncertainties in the simulations by the Intergovernmental Panel on Climate Change (IPCC) (Houghton et al., 2001). One of the major reasons for the poor representation of cloud-precipitation processes in

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models is a lack of knowledge on microphysical processes and internal structures in the clouds. Observations show that cloud droplet number concentration (Nc), which cannot be calculated in a realistic way in most models, occupies a wide range and varies greatly between different types of cloud and in different parts of a cloud (e.g. Heymsfield, 1993; Paul, 2000). Miles et al. (2000) collected in situ data reported in the literature, including the total number concentration, liquid water content (LWC), and distribution parameters in marine and continental clouds observed over the globe. A lot of data on cloud-precipitation microphysical characteristics parameters have been obtained by aircraft- and ground-based instruments during the past five decades over East Asia. There have been, however, few systematic investigations into cloud-precipitation properties over East Asia which is in the Asian monsoon region (eg., You and Liu, 1995; Deng et al., 2009). Since the Asian monsoon is one of the most significant components of the global climate system, models are unlikely to make reliable weather and climate projections if cloud systems in the Asian monsoon region are not reasonably represented in the models. Indeed the role of clouds in weather and climate is fundamental. However, we do know little about it because of complex and varying microphysical processes. The analysis based on detailed measurements should be one of the ways that can improve our understanding of the complicated interactions between the different physical processes and serve as a basis for the development of more accurate microphysical parameterizations in the models, including those of the cloud-scale, meso-scale, synoptic-scale, and global-scale. As a result, we will deeply discuss the in situ measurements in this paper. Since observational programs are costly and complex, a review of past measurements over East Asia would be of great value for characterizing the cloudprecipitation properties in the region. The main objective of the present study is to advance our understanding of cloud-precipitation microphysical properties over East Asia via the analyses. It is also expected that these results will be useful for future parameterizations and provide a basis for retrieval algorithms of remote sensing observations as well as for weather modifications. A general description of the data and method is given in Section 2. The analytical results are shown in Section 3. Conclusions and discussions are presented in Section 4. 2. Data and method A survey of the existing literature on in-situ measurements of cloud-precipitation microphysical properties has been undertaken and then a database established for aerosol, ice nuclei (IN), cloud droplet, fog, ice crystal, snow crystal, and raindrop. From the database, the main properties of aerosol and IN as well as microphysical parameters, including mean concentration of liquid and ice particles, LWC, and functional fit parameters of particle-size distributions are obtained. The time span of in situ probe measurements ranges from early measurements taken by Gu (1962) to recent publications (Jin et al., 2008; Xiang and Niu, 2008). Fig. 1 shows a summary of the geographical coverage of measurement sites and major information about the database is listed in Table 1. Among them the data for aerosols, IN and

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raindrops are obtained by ground-based instruments while the data of cloud droplet, ice crystal and snow crystals were obtained by aircraft inside clouds. This study is concerned mainly with general characteristics of cloud-precipitation, such as range of Nc, form of particle size distribution and ranges of their fitting parameters, etc., by summarizing as much as possible sampled datasets over East Asia rather than with the temporal variability. Although the technology and instruments vary during the time period, some errors associated with instruments have been considered for the data published in original literature. Besides, an important aspect of the current study that allows us to make a review of the results of different investigators is to present the published data on cloud-precipitation in a consistent way. The presentation of data in the literature is typically in terms of integrated quantity, such as mean concentration of liquid and ice particles, content of cloud droplet and rain LWC. It should be noted that there are many times of measurements per project or grouping of years. Thus, the “mean” represents an arithmetically averaged value over all measurements in each project or groupings of years, and “minimum” and “maximum” represent the averaged values of minimum and maximum, respectively. 3. Results 3.1. Aerosol particles Aerosol particles (APs) can influence weather and climate in several ways (e.g., Ackerman et al., 2000; Rosenfeld et al., 2008; Chen et al., 2011). They may act as ice and/or cloud condensation nuclei, and hence modify cloud formation and precipitation development processes. Furthermore, the interaction of APs with solar radiation may change the temperature at the Earth's surface. Another noticeable effect of APs is visibility reduction. The role played by APs in these processes depends on their concentration, size and chemical composition. The World Meteorological Organization (WMO) began to measure AP in 1971, as one of its important projects, which monitors atmospheric pollutions. The measurements have been made since the mid 1970s under a variety of conditions at locations all over East Asia. A variety of devices with capability to measure different size ranges have been used. In the beginning, the measurements were made at the ground and the instruments were capable of measuring particle-size distribution in the range in diameter (D) greater than 0.3 μm. For those measurements, the average concentrations vary from 73.1 to 353.0 cm −3, with an overall average of 166.9 cm −3. Measurements of much smaller sizes have been made since the device of Particle Measuring System (PMS) was introduced from abroad in 1981. The minimum AP diameter that can be effectively estimated with the PMS device is ~ 0.1 μm. As a result, the new samples by PMS contrast sharply with the samples obtained before PMS, in terms of overall average concentration. The average maximum values of AP concentration can be as high as 6850.5 cm −3 for the samples by PMS (Fan et al., 2007). Generally, the AP-size distribution can be described by a Junge power-law function, namely, Nðr Þ =

dN −v = cr ; dlogr

ð1Þ

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Fig. 1. Geographical coverage of cloud-precipitation microphysics measurements sites during 1960–2008 over East Asia.

Table 1 Information about the variables and their field studies. The numbers indicate that field programs have been made. Variables

Aerosol

Ice nuclei

Cloud droplet

Fog

Ice crystal

Snow crystal

Raindrop

Field program

22

26

62

41

26

18

38

where N is the total number of particles per unit volume in the radius range from r to r + dr and c is an empirical regression parameter. The value of v changes from aerosol to aerosol depending on its composition but is often between 1.17 and 8.24; c has a value between 0.01 and 29.12 cm −3 (Table 2). Many authors (e.g., Zhu, 1982; Chen et al., 1996) have suggested that using piecewise functions to describe APsize distributions can reduce errors greatly. Usually, the sum of a modified gamma distribution and a Junge power-law distribution are used for fitting AP-size distributions. 3.2. Ice nuclei IN are of fundamental importance for precipitation formation in all clouds except for warm tropical maritime clouds. IN comprise a very small but important fraction of the total atmospheric aerosols. Most efforts at weather modification are based on adding numerous artificial IN to clouds in order to increase the concentration of ice crystals. Recent cloudresolving models have suggested that IN can significantly affect clouds by supercooled droplets, that in turn affect radiation (Zeng et al., 2009a) and thus contribute to global warming (Zeng et al., 2009b). Hence, there is a necessity for measurements of IN, determining its concentration and dependency on temperature variability. You and Shi (1964), and You et al. (2002) observed IN concentration in the suburbs of Beijing using the same Bigg mixed-cloud chamber in order to make these observations comparable. They found that the IN concentration increased

Table 2 Fitted parameters of microphysical variable particle size distributions. Variables

Functions

Aerosol

Junge power-law c: cm− 3 v Exponential N0: L− 1

Ice nuclei

Cloud droplet

Gamma

Raindrop

Exponential Gamma

Ice crystal Snow crystal a b c

Exponential

Parameters

b: °C− 1 N0: cm− 3 μm− 1−γ λ: μm− 1 γ a N0: m− 3 mm− 1 λ: mm− 1 a N0: m− 3 mm− 1 λ: mm− 1 γ b N0: m− 3 mm− 1 λ: mm− 1 γ c N0: m− 3 mm− 1 λ: mm− 1 γ N0: m− 3 μm− 1 λ: μm− 1 N0: m− 3 mm− 1 λ: mm− 1

Range 0.01–29.12 1.17–8.24 3.68 × 10− 5– 5.01 0.02–0.51 10− 9–102 0.0019–1.254 0–12 432.9–3036.4 1.7–3.13 91.3–980.9 0.38–3.01 − 3.66–1.38 72.9–551.2 − 0.19–0.8 − 4.06–−2.3 32.4–792.5 1.06–2.04 − 3.0–0.02 82–3217 0.016–0.051 90–2.72 × 104 0.48–5.15

Rains originating from mixing clouds. Rains originating from cumuliform Clouds. Rains originating from stratiform clouds, respectively.

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greatly during the period, from which, at −20 °C, it increased by about 15 times. To find out the relation between ice nuclei and precipitation, a series of measurements have been taken to study IN characteristics since the early 1960s at many locations over East Asia (e.g., You and Shi, 1964; Zhao et al., 1965; You et al., 2002). The statistical results show that the average values of IN concentration range from 3.6 to 78.9 L −1 at temperature −20 °C, with an overall average of 22.9 L −1 (Fig. 2). A generally increasing trend from 1963 to 2003 is evident, especially with a significant increasing during the period 1980–2000. IN activity is able to initiate the ice phase. It is apparent that IN concentrations increase nearly exponentially with decreasing temperature. A convenient approximate expression of this behavior, by Fletcher (1962), is the following:

Table 3 Fitted parameters of ice nuclei-temperature (T) spectra over East Asia. N0 (L− 1) −3

1.06 × 10 6.89 × 10− 2 5.55 × 10− 2 3.79 × 10− 4 3.68 × 10− 5 3.631 × 10− 1 9.0 × 10− 3 3.40 × 10− 2 3.5 × 10-3 2.82 × 10− 2 1.96 × 10− 1 1.5 × 10− 2 8.9 × 10-2 5.01 a

NðT Þ = N0 expð−bΔT Þ;

ð2Þ

where N(T) is the number concentration of IN active at a temperature warmer than temperature T, ΔT is the supercooling in Celsius, and b and N0 are empirical regression parameters. The value of parameter b ranges from 0.4 to 0.8 °C −1; and its reasonable mean value is 0.6 °C −1. N0 is found to be 10 −5 L −1 approximately, whose variation is often quite large, ranging up to several orders of magnitude in some locations. Some empirical parameters, given by the authors of the papers, have been collected. The value of parameter N0 ranges 3.68 × 10−5 to 5.01 L −1; and b is in the range of 0.02–0.51 °C−1 (Table 3). The dependence of IN concentration on temperature

209

b (°C− 1)

T (°C)

Literature

0.42 0.2627 0.2561 0.5110 0.4810 0.1731 0.308 0.395 0.38 0.113 0.022 0.36 0.27 0.11

− 30–−15 − 12–−30 − 30–−15 − 30–−16 − 30–−16 − 30–−15 − 30–−15 − 30–−15 − 30–−15 − 30–−15 − 30–−15 − 30–−15 − 30–−15 − 30–−15

You and Shi (1964) Huang et al. (1986) LCPa You and Liu (1995) You and Liu (1995) Huang et al. (1986) Zhao et al. (2000) You et al. (2002) Li and Huang (2001) Li et al. (2003a) Li et al. (2003a) Li et al. (2003a) Shi et al. (2006) Shi et al. (2006)

LCP: Laboratory of Cloud Physics, Hebei province, China, 1980.

is consistent, always a generally increasing with decreasing temperature. However, the variation of increasing IN-number concentration is often quite large, varying from case to case. 3.3. Cloud droplets Cloud droplet size distribution (CDSD) is one of the most important properties of the microstructure of clouds. CDSD evolution itself reflects the dynamic and thermodynamic characteristics of the cloud system. Thus, knowledge of CDSD is important in the study of cloud physics, cloud development and rain formation. Additionally, at present, Nc cannot be

Fig. 2. Mean ice nuclei concentration. (Each data represents an arithmetically averaged value over all measurements in each project or groupings of years rather than the same value for some years.).

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calculated in a realistic way in large-scale and most mesoscale models because it varies over a large range and depends on several factors, such as accurate activated CCN, updraft velocity, and even large-scale conditions. Gu (1962) was the first to discuss the CDSD in cumulus congestus, fair weather cumulus, cumulonimbus, altostratus and nimbostratus over East Asia. Hong and Huang (1965) made a preliminary analysis about bimodal spectra of CDSD. Since then, a great number of measurements have been carried out for the clouds over East Asia. It can be seen from these measurements that the average Nc ranges from 402.8 to 1830.5 cm −3 in cumuliform clouds, with an overall average of 907.7 cm −3, and ranges from 0.04 to 426.4 cm −3 in stratiform clouds, with an overall average of 120.9 cm −3. It is apparent that cumuliform clouds have a larger Nc than stratiform clouds. The variation of Nc in stratiform clouds is often quite large, ranging up to two orders of magnitude in some cases.

However, low concentration of only 0.04 cm −3 was found in June 1987; its onset coinciding with an unusually disastrous fire in Daxin Mountains of Heilongjiang province, China. This may indicate that fire and smoke have a significant effect on cloud microphysical properties. The distribution of liquid water in clouds is closely related to many problems facing the atmospheric science community today. In fact, liquid water might be considered as the most significant substance in the atmosphere, which interacts with both longwave and shortwave radiations in the atmosphere. Liquid water is also important in cloud physics and dynamics, where the amount of liquid water provides information about the extent of condensation, entrainment, and precipitation occurring in clouds. According to the long-term measurement of LWC over Esat Asian, the average LWC for stratiform clouds varies from 0.0002 to 0.520 g m −3, with an overall average of 0.140 g m −3 (Fig. 3). This contrasts sharply with cumuliform

Fig. 3. Cloud droplet liquid water content in statiform clouds. (As Fig. 2 but for cloud droplet liquid water content).

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clouds, whose average LWC ranges from 0.005 to 2.000 g m −3, with an overall average of 0.875 g m −3. CDSD is an important parameter which has a significant impact on microphysical processes and radiative properties of clouds, and cloud droplet spectral change can significantly alter the precipitation intensity (Zhou et al., 2005). CDSD has attracted increasing attention since the 1940s, and great progress has been made in the cloud evolution theory. Several formulas have been used to approximate CDSD, such as exponential, lognormal, weibull, gamma size and modified gamma size distributions (Liu et al., 1995). Deirmendjian (1969) suggested that modified gamma size distribution can be used to fit different particles (rain, hail, cloud, etc.). However, it is inconvenient to use modified gamma size distribution in operation because too many parameters need to be adjusted. In reality, Khrgian–Mazin distribution is widely used in CDSD, which is a reduced form of the modified gamma size distribution. Numerous studies (e.g., Yan and Chen, 1990; Li et al., 2003b; Gonçalves et al., 2008), however, pointed out that Khrgian–Mazin distribution contributes large errors due to the limitation of γ = 2. Accordingly, the gamma size distribution is a logical choice since it can give reasonable approximations to observed spectra. Its general form is: γ

NðDÞ = N0 D expð−λDÞ;

ð3Þ

where γ is a shape parameter. If γ is positive, it implies concave downward in a log-linear coordinate. For a negative γ, it is concave upward. Parameter λ is interpreted as the slope, for the large drop part of the spectrum. N0, in general, has no useful physical meaning and shows a wide range of variation, with dependence to γ. According to the long-term study of CDSD, provided by the authors of the papers, gamma size distributions have shown to be suitable for most observed CDSDs in stratiform clouds over East Asia. However, the empirical parameters are different from cloud to cloud, even in the same cloud at different heights. Parameter γ has a wide range of variation from 0 (reduced the expression to exponential) to 12. N0 ranges from 10 −9 to 10 2 cm −3 μm −1−γ, and λ is in the range of 0.0019–1.254 μm −1. As is shown in Fig. 4, it is apparent that all but one of the CDSD are concave downward and there is a large variation of Nc from cloud to cloud. This indicates that the gamma distribution can be suitable for fitting CDSDs over East Asia, although there are differences among the values of empirical parameter. Generally speaking, the drop size spectra can be divided into two groups. One group has a wide spectral width, with a small change rate, while the other group has a narrow spectral width, with a lager slope. The difference may cause by cloud formation environment. The former group was measured in the northwest in China, and the later group was measured in Shandong province and surrounding areas, which are near East China Sea. The result perhaps indicates that it is unreasonable to represent cloud droplet size distribution with a fixed slope in bulk microphysics schemes. In addition, the lines with circles which show an obvious difference among each other are average cloud droplet spectra at different levels in stratiform clouds. As a result, it is necessary to improve the bulk microphysics schemes by

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predicting (diagnosing) slope γ as a function of the predicted moments. 3.4. Fog droplets Fog research can be dated back to the beginning of the 20th century. As early as 1917, Taylor (1917) carried out a field experiment to study fog formation for the first time. Comprehensive field observations (e.g., Pilieacute, et al., 1975; Roach et al., 1976) and simulation studies (e.g., Fu et al., 2006; Shi et al., 2010) of fog have been conducted ever since. These studies have revealed the basic physics of fog. In the 1960s, several field campaigns have been carried out over East Asia, such as those at the Shuangliu airport, Sichuan province; the sea-fog study in Zhoushan, Zhejiang province; the radiation-fog study in Xishuangbanna, Yunnan province; the city-fog study in Shanghai; and the disaster-fog study in Chongqing City and along the Shanghai-Nanjing expressway. Li (2001) summarized some preliminary results of artificial fog dispersion, physico-chemical characteristics, and genesis and lysis processes during 1958–1997. Based on these measurements (Table 4), it can be found that average fog droplet concentration ranges from 34.9 to 1746.3 cm −3, with an overall average of 279.7 cm −3. The maximum fog LWC can reach 1.180 g cm −3, which was measured over rivers, with an overall average LWC of 0.262 g cm −3 (after excluding the largest value ever measured on a river). Knowledge of fog droplet-size distribution is important for theoretical treatment of problems related to visibility and transmission of radiant energy through fog. It will also lead to a better understanding of the evolution and structure of fog. Thus, fog droplet-size distribution is one of the most important microphysical properties of fog. Generally, the droplet-size distribution is determined by the physical properties of the governing air mass. It is hard to find an empirical formula to describe fog droplet-size distribution. Some measurement results have good agreements with the exponential size distribution, while others have goodness-offits with the modified gamma size distribution. 3.5. Raindrops Raindrops are products of sophisticated macro- and microscale interactions in clouds. Raindrop-size distribution (RSD) provides insight into the cloud physical processes responsible for raindrop growth. RSD is also of interest in numerical simulations of the dynamics of “warm” convective clouds, because the knowledge of RSD is of importance for modeling clouds and large-scale systems. Dating back to the 1960s, many studies have been made to investigate the properties of RSD over East Asia (e.g., Gu, 1962; Xu et al., 1987; Chen and Gu, 1989). However, these studies are based on a single precipitation type. Gong et al. (1997) noted that it is meaningful to investigate RSD by dividing rain into originating from cumuliform clouds, stratiform clouds, and mixed (stratiform and cumuliform) clouds, and analyzed their RSDs in detail, respectively. Several other studies also showed that it is useful to separate precipitation cloud systems into convective clouds and stratiform clouds (e.g., Tokay and Short, 1996; Liu and Lei, 2006).

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103

102

dN/dD (cm-3 µm-1)

101

100

10-1

10-2

10-3

10-4

0

10

20

30

40

50

D (µm) Fig. 4. Gamma fitted cloud droplet-size distributions in stratiform clouds.

Datasets for rain have been separated into raindrops of rain originating from statiform clouds, cumuliform clouds, and mixing clouds, based on the original classification provided by the authors. In all the measurement results, the average raindrop concentration (Rc) for the rain originating from stratiform clouds are 114.5–1663.9 m −3, with an overall average of 526.0 m −3 (Fig. 5). The average Rc of rain originating from cumuliform clouds ranges from 372.1 to 13340.9 m −3, with an overall average of 4918.0 m −3 and that is 3922.6 (range of 205.0 to 8143.0) for the rain originating from mixing clouds. Rains originating from stratiform clouds have lower rain water content (RWC) of 0.357 g m −3 (in the range of 0.028–2.19), compared with the rain originating from cumuliform clouds, 0.596 g m −3 (in the range of 0.083–1.197); and that is 0.390 (range of 0.016 to 0.061) for rains originating from mixing clouds. The Meiyu frontal precipitation in the Yangtze River Valley has its own characteristics, whose RWC is much larger. The average RWC of 2.19 (range of 0.008 to 18.95) and 1.39 (range 0.01 to 12.13) g m −3 as well as the average Rc of 438.0 (range of 4 to 3259) and 579.2 (range of 13 to 579.2) m −3, reported by Bian et al. (1984) and Jiang et al. (1986), respectively. It means that the precipitation in the Meiyu front has a wider spectral width. The southwest strong wind providing an ample supply of moisture and well-developed convective cloud clusters may lead to such a phenomenon. Generally, there are many welldeveloped convective cloud clusters embedding in a wide Meiyu frontal cloudband to form a deep system. Thus, the

mixed-phase cloud process with ice phase (ice, snow, graupel) coexisted with abundant liquid phase (cloud water and rain) plays the most important role in the formation and development of heavy convective rainfall since, during such a process, cloud droplet (raindrop) size increases continuously by a series of microphysical processes, such as the coalescence with smaller drops as it falls. Various empirical equations have been advanced to describe RSD. Probably the most widely used RSD model in the last several decades was the two-parameter exponential size distribution based on observations, proposed by Marshall and Palmer (1948). Its general form is: NðDÞ = N0 expð−λDÞ;

ð4Þ

where D is the drop diameter (or equivalent diameter for non-spherical large drops); N(D) is the drop concentration per diameter interval; N0 is a constant (=8000 m −3 mm −1), and λ (=4.1I −0.21) is the slope parameter that depends on rainfall intensity I (mm hr −1). For this analytical form, the RSD log-linear plot should be a straight line with intercept N0 and negative slope λ. Actually, the “constant” N0 has been found not constant at all, and depends on I. Thus, the functional dependence of λ on I may vary. Many authors have preferred the two-parameter exponential size distribution, which does not restrict N0 to any fixed value. Waldvogel (1974) suggested that a sudden increase or decrease in the value of N0

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Table 4 Fog droplet concentration (Con) and liquid water content (LWC). Literature

Lasting time

Wu et al. (2007)

1962.5 1962.7–8 1981.1–4 1984.4–5.19 1968–1969 1968–1969 1986.12–1987.2 1986.12–1987.2 1986.12–1987.2 1970.12–1971.1 1985.12–1986.1 1981.1 1968–1969 1968–1969 1997.11 1988–1991 1989.1.7 1991.1.29 1989.1 1989.12–1990.1 1996.12 Mean Min Max

Yang et al. (1989) Li et al. (1992)

Huang et al. (2000) Bao et al. (1995)

Li et al. (1999)

Con (cm− 3)

D (μm)

LWC (g m− 3)

Literature

Lasting time

Con. (cm− 3)

D (μm)

LWC (g m− 3)

359.3 453.5 395.5 37.1 41.9 34.9 153 94.8 48.5 256.4 417.4 115.7 83.2 45.2 236.6 198

12.1 15.2 11.7 18.4 19.7 28.6 6.8 13.1 15.4 10.3 8.3 12.3 22.9 22.9 8 5.3 9.5 9.7 5 4.2 6.9 10.2 1.4 28.6

0.4 0.86 0.66 0.11 0.36 0.74 0.08 0.25 0.21 0.17 0.5 0.43 0.32 0.46 0.124 0.18 1.18 0.24 0.26 0.07 0.163 0.262 0.005 1.180

Li et al. (1999) Wu et al. (2007)

1997.1.21 1998.12–1999.1 1999.1 1999.1 2001.1 2001.3 1987.11 1997.11 1990 Fall, 1978 1950–1960 1988.1 1987.11 1987.11. 1988,1989,1991 1993.6–1993.7 2001.12 2001.12 2006.12 2007.3

268.6 47 170 79 191 202 115 222 267 174.1 100–500

4.8 13.3 7.5 11.1 8.2 7.2 12.2 8.1 7.6

0.04 0.125 0.148 0.123 0.155 0.115 0.24 0.11 0.25 0.153

117–159 46–201 26–1178 82.5 1746.3 983 345.7 56.3

12.3 11.7 7.2 2.3 3.8 4.5 3.6 5.2

149.9 173 606 941.4 279.7 34.9 1746.3

can be interpreted as a transition from one type of rainfall to another, although their rainfall intensities are the same. The exponential size distribution has been shown by many authors to be suitable for application to rain originating from stratiform clouds. It fits some observations reasonably well and provides a simple expression (with two-parameter) for the stratiform-cloud rainfall parameterization. Such assumption has been used by many cloud modelers (e.g., Lin et al., 1983; Reisner et al., 1998). Table 2 also shows the statistics of empirical parameters of exponential size distributions over East Asia. Parameter N0 ranges from 432.9 to 3036.4 m−3 mm−1,

Deng et al. (2007)

Wang and Wei (1981) He et al. (2003)

Xu et al. (1994) He et al. (2003) Pu et al. (2008) Qu et al. (2008)

1.4 0.67 0.68 0.16 0.043 0.21 0.097 0.05 0.005

–

with an overall average of 1366.4 m −3 mm−1; λ is in the range of 1.7–3.13 mm−1, with an overall average of 2.82 mm−1. The two-parameter exponential size distribution is limited, however, by its inherent assumption that RSD is exponential. Numerous authors (e.g., Takeuchi, 1978; Ulbrich, 1981; Willis, 1984) have proposed the use of gamma size distribution like Eq. (3) instead. The ranges of gamma size distribution parameters for different RSD types are also shown in Table 2. The parameter N0 ranges from 32.4 to 792.5 m −3 mm−1−γ, with an overall average of 313.3 m −3 mm−1−γ, and the averages of λ and γ are

Fig. 5. Mean raindrop concentrations of rains originating from stratiform clouds. (As Fig. 2 but for raindrop concentration).

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1.72 (ranging from 1.06 to 2.04) mm−1 and −1.75 (ranging from −3.0 to 0.02), respectively, for the rain originating from stratiform clouds. Parameters N0, λ and γ are 269.1, 0.18 and −3.16 and 313.3, 1.72 and −1.75 for the rains originating from cumuliform and mixing clouds, respectively. Negative γ appears frequent, although there are remarkable differences among the RSDs of rain originating from each type of cloud. As shown in Fig. 6, the RSD of rain originating from mixed clouds shows a wider range of variation compared to the RSDs of rain originating from convective and stratiform clouds. Both exponential and gamma size distributions have been used to fit RSDs of rain originating from different types of cloud. The statistical results show that goodness-of-fits for rains of stratiform origin are the exponential size distribution (15 papers come to the same conclusion) and the gamma size distribution (9 papers come to the same conclusion), respectively. Good agreement has been obtained when gamma size distribution was applied to fit the RDSs of rains originating from both convective and mixing clouds, though not all the time. 3.6. Ice crystals The fact that ice particles play an important role in precipitation formation is firmly established, because ice presence in a cloud is required for rain formation by the wellknown Bergeron process. If ice crystals and supercooled droplets coexist in a cloud, the ice particles will grow preferentially, which can lead to the formation of precipitation particles. Wang et al. (2010c) suggested that the radiative processes of ice clouds directly impact radiation in heat budget and the microphysical processes of ice clouds directly affect latent heat and net condensation through deposition processes, which may eventually change surface rainfall. Since the 1960s, researchers have paid more attention to ice-crystal concentrations and their size distribution over East

A

B

Asia. Sun and You (1965) made measurements of ice crystals and snow crystals in stratiform clouds for the first time in Jilin province, China. Later, measurements of ice-crystal microphysical properties have been made using a variety of instruments mounted on aircraft over East Asia. However, most of these measurements were made in stratiform clouds. The results show that ice-crystal concentration ranges from 0.01 to 78.4 L −1, with an overall average of 18.8 L −1. Ice-crystal-size distribution has been analyzed in various field campaigns. Several mathematical expressions for the size distribution have been suggested that describe observations reasonably well. Heymsfield and Platt (1984) presented a power-law for the size distribution. Ryan (2000) suggested an exponential form to fit ice-crystal-size distribution. As for the bimodal and multimodal cases, it is hard to find a simple function to describe the size distribution. Mitchell (1994) showed a bimodal spectrum, which can be described by the sum of two gamma distributions. Platt (1997) presented an alternative model characterized by a power law and a simple gamma size distribution often termed the exponential size distribution. Exponential size distribution has shown to be suitable for most observed spectra over East Asia. The regression empirical parameter N0 ranges from 82 to 3217 m−3 μm−1, and λ, from 0.016 to 0.051 μm−1 (see Table 2 for further details). 3.7. Snow crystals According to the long-term measurement of snow crystal concentration in East Asia, the results show that snow crystal concentration varies from 0.08 to 9.67 L −1, with an overall average of 1.93 L −1. Studies have indicated that the size distribution of snow crystals can be described by the exponential size distribution (e.g., Gunn and Marshall, 1958; Sekhon and Srivastava, 1970). Several researchers have discussed snow crystal-size distributions in clouds over East Asia (e.g., Sun and You, 1965; Chen, 1987; Xiang and Niu, 2008). The exponential size distribution is well adapted to

C

Fig. 6. Gamma fitted raindrop-size distributions. Thick lines with circles are the overall average raindrop-size distributions corresponding to the rains originating from each cloud type. (A) Rains originating from stratiform clouds; (B) Rains originating from cumuliform clouds and (C) rains originating from mixing clouds, respectively.

J. Yin et al. / Atmospheric Research 102 (2011) 206–217

represent observed spectra at several locations over East Asia. Table 5 shows the statistics of the fitting parameters of snow crystal-size distribution. Parameter N0 is in a wide range of 90 to 2.72 × 10 4 m −3 mm −1, and λ is in the range of 0.48 to 5.12 mm −1. 4. Conclusions and discussions A database of cloud-precipitation microphysical characteristics has been established, using the in situ data published in literature. The main features of aerosol, IN, cloud droplet, fog, ice crystal, snow crystal, and raindrop are obtained using based on the database. A statistical analysis has been carried out to give a summary of particle concentrations and LWC contents (Table 6) and, the following conclusions might be drawn: (1) The average aerosol concentrations in diameter greater than 0.3 μm ranges from 73.1 to 353.0 cm −3, with an overall average of 166.9 cm −3. Generally, the AP-size distribution can be described by a Junge power-law function. Using piecewise functions to fit AP-size distribution can reduce the errors greatly. (2) The average maximum values of IN concentration can reach 78.9 L −1 at −20 °C, with an overall average of 22.9 L −1. The number of IN concentration increases nearly exponentially with decreasing temperature. However, the variation of increasing IN number is quite large from case to case. (3) Cumuliform clouds have higher average Nc (907.7 cm−3) compared with stratiform clouds (120.9 cm−3). There is a large variation of Nc, suggesting that models with a fixed value of Nc may not capture the actual cloud microphysics in a particular region. The average LWC for stratiform clouds varies from 0.0002 to 0.520 g m−3, with an overall average of 0.140 g m−3. This contrasts sharply with cumuliform clouds, whose average LWC ranges from 0.005 to 2.000 g m −3, with an overall average of 0.875 g m−3. The gamma size distribution has shown to be suitable for most of the observed spectra in stratiform clouds. However, the shape parameter γ has a wide range of variation from 0 (reduced the expression to exponential) to 12. Table 5 Exponential function fitted parameters of snow crystals size distributions. Time

N0 (m− 3 mm− 1)

λ (mm− 1)

Literature

Spring, 1982

370 90 110 150 2720 2170 1860 4430 1620 480 2883 440 9850 2460

0.992 0.48 0.51 0.63 3.78 4.22 5.12 1.99 3.40 3.69 2.90 2.681 1.192 1.537

Chen (1987)

1982

1978–1990 1989–1992 1989

You et al. (1989)

215

Table 6 Summary of aerosol, IN and cloud-precipitation microphysical properties. The mean values and ranges are listed, including ice nuclei (IN), aerosol (D N 0.3 μm), ice crystal, snow crystal, cloud droplet concentration in stratiform (SNc) and in cumuliform (CNc), fog, raindrop concentration of rain origin form statiform clouds (SRc) and cumuliform clouds (CRc), liquid water content in stratiform clouds (SLWC) and cumuliform clouds (CLWC), rain water content of rain originating from statiform clouds (SRWC) and from cumuliform clouds (CRWC), and fog liquid water content (Fog LWC). (Each data is a geometric mean of many measurements per project or grouping years rather than always the same value for some years). Variables

Mean

Max

Min

IN (L− 1) Aerosol (cm− 3) Ice crystal (L− 1) Snow crystal (L− 1) SNc (cm− 3) CNc (cm− 3) Fog (cm− 3) SRc (m− 3) CRc (m− 3) SLWC (g m− 3) CLWC (g m− 3) SRWC (g m− 3) CRWC (g m− 3) Fog LWC (g m− 3)

22.9 166.9 18.8 1.93 120.9 907.7 279.7 526.0 4918.0 0.140 0.875 0.357 0.596 0.262

78.9 73.1 78.4 9.67 426.6 1830.5 1746.3 1663.9 13340.9 0.520 2.000 2.19 0.083 1.18

3.6 353.0 0.01 0.08 0.04 402.8 34.9 114.5 372.1 0.0002 0.005 0.028 1.197 0.005

(4) The average fog droplet concentration ranges from 34.9 to 1746.3 cm −3, with an overall average of 279.7 cm −3. Fog LWC is in the range of 0.005– 1.180 g m −3, with an overall average of 0.262 g cm −3. (5) Both the exponential and gamma size distributions have been used to fit the RSD of rain originating from stratiform clouds. Good agreement has been obtained when the gamma size distribution was applied to fit observed spectra of rains originating from convective and mixing clouds. (6) The exponential size distribution has goodness-of-fits with ice crystals and snow crystal size distributions. The new database established in the study provides a basis to promote our understanding of cloud-precipitation microphysical properties. It can be used for tuning parameterizations in models, for evaluating retrieval techniques using remotelysensed observations, and for weather modifications. Finally, several recommendations are proposed, which will provide guidance for future applications: (1) Systematic errors may come from the uncertainty of instruments used during this period. (2) Concerning the bimodal and multimodal spectra, we need to understand their physical meaning and formation mechanisms. (3) The cloud profile information is of importance in models. Since direct measurements of the vertical structure of cloud have, until now, been limited, new techniques, such as Cloudsat products (Stephens et al., 2002), should be introduced and further developed. Acknowledgments

a

NRG Xiang and Niu (2008) Wang, 1997 Feng, 1993

a NRG: Northern Research Group of artificial precipitation test in stratiform clouds, 1991.

This study is jointly supported by the R&D Special Fund for Public Welfare Industry (meteorology) by the Ministry of Finance and the Ministry of Science and Technology (grant Nos. GYHY200806007 and GYHY201006014), the National Natural Science Foundation (grant Nos. 40875022 and

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