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amount and cloud types (Schiffer and Rossow, 1983; Minnis et al., 1992; Klein and Hartmann, 1993; Rossow and Garder, 1993; Rossow and Schiffer, 1999; Li et al., 2003; Liu et al., 2003; Liu and Fu, 2009). With the progress in instruments, such as the advanced visible and infrared sensor, microwave imager, CloudSat, Cloud-Aerosol Lidar, and Infrared Pathfinder Satellite Observation (CALIPSO), the technology for retrieval of cloud parameters has been improved (Arking and Childs, 1985; Huang and Diak, 1992; Rao et al., 1995; MaKague and Evans, 2002; Stephens et al., 2002; Zhao and Weng, 2002; Winker et al., 2010; Wang et al., 2011), which provides a new opportunity to understand the properties of cloud parameters. In particular, the observations from 36 channels of the Moderate-resolution Imaging Spectroradiometer (MODIS) on Aqua and Terra, and 20 channels of the Medium Resolution Spectral Imager (MERSI) on FY-3 have promoted the studies on cloud parameters. However, most radiometers do not have as many channels as MODIS or MERSI. Usually, they have one visible, one near-infrared, one intermediate infrared, and two thermal infrared channels, such as Visible and Infrared Scanner (VIRS) onboard Tropical Rainfall Measurement Mission (TRMM). Making good use of the observations from these satellites together with the data from other instruments can promote the studies of cloud parameters. Based on this motivation, this paper summarizes the methods for retrieval of cloud parameters, especially the bispectral reflectance (BSR) method, reviews the advances in recent studies on the aerosol indirect effects related to cloud parameters, and analyzes the relationship between the cloud parameters and precipitation properties (intensity, type, and structure). 2. The BSR algorithm The spectrum algorithm for retrieval of cloud parameters involves inherent properties of clouds. It is known that the scale of cloud particles generally varies from 1 to 100 μm, typically 10 μm for liquid cloud and 30 μm for ice cloud. These particles generate different electromagnetic radiation extinction from visible

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bands (0.38–0.78 μm), near infrared bands (0.8–3.0 μm), mid-infrared bands (3–8 μm), to thermal infrared bands (8–14 μm). For shortwave bands (0.4–4.0 μm), especially the visible bands, cloud particles scatter the incoming solar radiation to various degrees depending on the particle size and phase, the incident angle of solar radiation, as well as the structure of cloud, while the absorbing effect of the particles could be neglected. For longwave bands (4–100 μm), especially the thermal infrared bands, the incoming radiation is affected by the absorption/emission of liquid and ice cloud particles, which is mostly determined by the cloud temperature (related to the cloud height). For the thin cloud, the upward radiance above it is also influenced by surface upwelling radiation. During the radiative transfer in cloud particles, reflection and transmission of solar radiation by clouds are determined by the effective radius (Re) and liquid water path (LWP) of cloud particles (Hansen and Travis, 1974). For the simplicity of radiative transfer calculation, Re, a parameter evaluating the size of cloud, is defined as the ratio of the cubic sum to quadratic sum of different cloud particle radii. It has been revealed that cloud optical thickness (τc ) is sensitive to LWP, Re, and the phase of cloud particles, while LWP is determined by cloud number density (i.e., cloud water content) and cloud thickness, and Re is influenced by the size distribution of cloud particles and the phase determined by cloud temperature. The BSR algorithm is the most representative methods for cloud parameter retrieval among numerous algorithms. It is first proposed by Twomey and Seton (1980) to evaluate τc and Re. Nakajima and King (1990) improved the method to retrieve τc and Re from the MODIS observations. Generally, the BSR algorithm takes advantage of the characteristics of negligible absorbing effect in visible bands and distinct absorbing effect in near infrared bands to synchronously retrieve cloud τc and Re. According to the radiative transfer theory, for a plane-parallel (one-dimensional) model, the spectral reflectance Rλ at a given wavelength (λ) in visible/infrared bands is expressed as the function of τc , single-scattering albedo ω (varying with wavelength), asymmetry factor g (determining the scattering phase

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function), land surface reflectance rs , solar direction angle ξ0 , and observation direction angle ξ, i.e., Rλ = fλ (τc , ω, g, rs , ξ, ξ0 ). Hence, Rλ is a function of τc (1−g) (van de Hulst, 1980). Since the absorbing effect of cloud particles in visible bands is so small that the assumption of ω = 1 is valid, thus the variation of Rλ mainly depends on τc (1 − g). In infrared bands, however, Rλ is influenced by both τc (1 − g) and ω because cloud particles have both absorption effect and scattering effect in these bands. Therefore, the first step of the BSR is to acquire the simulated reflectance at the two bands with different Re and τc by using the radiative transfer model, and then create a lookup table of Re and τc from the reflectance of the two bands. At last, the true Re and τc values are obtained by comparing the observed reflectance with the simulated one. Note that only absorption and scattering of cloud particles to solar radiation are considered in the BSR, while the thermal radiative emission of cloud particles is ignored. If longer wavelength (such as 3.7 μm) is considered in the retrieval, contributions from the thermal radiation of cloud particles, which can be derived through cloud temperature, should be taken into account and deducted from the observations. The reflectance relationship of channels at 0.75 and 2.16 μm for a given τc and Re is simulated by the radiative transfer model (see Fig. 2 in Nakajima and King, 1990). It is shown that the relationship of both channels is not independent when the reflectance is lower than 0.4, contrary to the independent relationship of the two channels at large reflectance (> 0.4, corresponding to cloudy condition), i.e., an orthogonality relationship between Re and τc . These indicate that the reflectance at 0.75 μm increases significantly with the increment of τc , while the reflectance at 2.16 μm is not sensitive to the τc variation. Similarly, with the increment of Re, the reflectance at 2.16 μm decreases gradually against relatively stable reflectance at 0.75 μm. Consequently, τc and Re can be obtained by interpolating the reflectance observed by the two channels. After τc and Re are acquired by the above retrieval method, LWP (in g m−2 or kg m−2 ) is calcu2 lated by the function LWP = ρτc Re, where ρ is liq3

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uid water density (g cm−3 ; Arking and Childs, 1985; Han et al., 1994; Nakajima and Nakajima, 1995). It is worth noting that the capability of solar radiation at visible/infrared bands to penetrate through the cloud layers is so poor that the retrieved τc and Re only represent the cloud parameters near the cloud tops. Figures 1a–c show signals observed by visible, near infrared, and thermal infrared channels of Visible and Infra-Red Radiometer (VIRR) onboard FY-3 over the Tibetan Plateau at 0645 UTC 3 July 2011. In Fig. 1a, a bow cloud band and many small cloud cells to its south with over 0.6 reflectance appeared over the Tibetan Plateau. The bow cloud band has brightness temperatures of 245–260 K at thermal infrared channels while the brightness temperature is lower than 220 K for those cloud cells (Fig. 1c). If information on the near and thermal infrared channels is joined together, it can be speculated that the bow cloud band comprises clouds with mixed ice and water, and those cells are composed of only ice particles because they have higher cloud top altitudes. By using the BSR method, the retrieved Re, τc , and LWP are plotted in Fig. 2, which indicates that the bow cloud band is put together by many cloud blocks with Re exceeding 20 μm. Re for those cloud cells is larger than 35 μm. If we compare the spatial distributions of both Re and LWP, τc exhibits more continuity. All the above results illustrate a good observational ability of VIRR in detecting cloud structures near cloud top. On the basis of the BSR algorithm, triple-channel (such as 1.6, 2.1, and 3.7 μm) and multi-channel algorithms are developed to retrieve the cloud parameters (Stone et al., 1990; Wielicki et al., 1990; Nakajima et al., 1991; King et al., 1992; Ou et al., 1993; Han et al., 1994; Rosenfeld et al., 1994, 2004; Nakajima and Nakajima, 1995; Platnick and Valero, 1995; Masunaga et al., 2002; Platnick et al., 2003; Chen Yingying et al., 2007). Using two near infrared channels (1.6 and 2.1 μm) and a mid-infrared channel (3.7 μm) from MODIS observations, Chen R. et al. (2007) obtained structure information along different height levels near the cloud top and retrieved Re at different cloud tops, i.e., the vertical profile of Re. Based on MODIS observations, Ye et al. (2009) presented a retrieval scheme of τc and

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Fig. 1. Signals detected by (a) visible, (b) infrared, and (c) thermal infrared channels of VIRR onboard FY-3 at 0645 UTC 3 July 2011.

Re for multi-layer clouds. In this scheme, the radiative databases for τc and Re of the multi-layer cloud, water cloud, and ice cloud are established with Santa Barbara DISORT Atmospheric Radiative Transfer Model (SBDART), with consideration of various geometrical conditions, surface types, and atmospheric states. After the identification of clouds, cloud phase recognition, and multilayer cloud detection, τc and Re are retrieved by MODIS channel 1 (0.65 μm) and channel 7 (2.13 μm) data via lookup tables. Meyer and Platnick (2010) provided a new technique of pairing MODIS channels at 1.38 and 1.24 μm to evaluate the above/in-cloud water vapor attenuation and retrieve thin cirrus τc by such corrected attenuation. Nauss and Kokhanovsky (2011) proposed a novel method relying on asymptotic solutions to radiative transfer theory to acquire the information of τc , Re, the liquid and

ice water paths, and so on. The investigations on retrieval algorithms of cloud parameters during nighttime started from the 1990s, which are mainly based on measurements in longwavelength bands (Baum et al., 1994; Kubota, 1994; Strabala et al., 1994; Key and Intrieri, 2000; Baum et al., 2003). In principle, τc , Re, and phase of cloud are obtained by brightness temperature difference between infrared channels, such as one mid-infrared channel (3.7 μm; Ch3) and two thermal infrared channels (10.8 and 12.0 μm; Ch4 and Ch5) in the Advanced Very High Resolution Radiometer (AVHRR), and three thermal infrared channels (8.0, 10.8, and 12.0 μm) in the High-resolution Infrared Radiation Sounder (HIRS). These channels have different behaviors in absorbing and scattering for the same cloud particles. Due to strong scattering effect at near infr-

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Fig. 2. (a) Re, (b) τc , and (c) LWP, retrieved by signals detected by visible and infrared channels of VIRR.

ared and mid-infrared channels (Ch3), the absorptivity and emissivity are far below 1.0. But for Ch4 and Ch5, both absorptivity and emissivity are approximately 1.0, as a result of small single scattering albedo, and then the scattering extinction can be negligible. In the condition of thick clouds with smaller Re, the emissivity at Ch3 is smaller than that at Ch4 so that the brightness temperature difference between Ch3 and Ch4, i.e., BTD34, is negative and decreases with increasing Re. As for thin clouds, i.e., semitransparent clouds, the brightness temperature at Ch3 is higher than that at Ch4 because temperature on land surface is usually greater than that in cloud, which leads to positive values at BTD34. This is why BTD34 is used to identify thin clouds. Generally, the sensitivity of BTD34 is higher than that of BTD45 (the brightness temperature difference between Ch4 and Ch5). But it is convenient to directly use BTD45 in daytime be-

cause there is an additional brightness temperature at Ch3 caused by reflected solar incident radiation (Inoue and Aonashi, 2000). At present, the vertical structure of cloud is obtained from active detection onboard satellites, such as cloud radar on CloudSat and lidar on CALIPSO. If the active detection is combined with spectrum observation, the ability to obtain cloud parameters in the vertical direction will be enhanced. For example, Wu et al. (2009) revealed the cloud occurrence frequency at different altitudes by using combined observations of MISR, AIRS, MODIS, OMI, CALIPSO, and CloudSat. The multi-sensor combination has a good ability to detect multi-layer clouds. Hu et al. (2010) investigated the liquid water content, occurrence, and fraction of super cool water clouds through combining measurements of CALIPSO, IIR (Infrared Imaging Radiometer), and MODIS. Joiner et al. (2010) de-

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veloped a relatively simple algorithm to detect multilayer clouds and their vertical structure, using A-train constellation. Delanoë and Hogan (2010) retrieved the ice water content, effective radius, and extinction coefficient of ice clouds, based on the merged data derived from CloudSat, CALIPSO, and MODIS. Comparing four retrieval algorithms for ice cloud properties with data supplied by CloudSat, CALIPSO, and MODIS, Thorwald at al. (2011) concluded that microphysical assumptions in these algorithms need to be refined. Base on high temporal resolution observations (15 min) of Spinning Enhanced Visible and Infrared Instrument (SEVIRI) aboard the Second Generation Meteosat, K¨ uhnlein et al. (2013) proposed a semi-analytical cloud parameter retrieval method. Wang et al. (2013) presented the global distribution of liquid water in snowing clouds using observations of MODIS and CloudSat. All above mentioned studies illustrated the superiority of cloud detection by multiple sensors aboard multiple satellites and the improvement of cloud parameter retrieval methods. 3. Analysis of aerosol indirect effect by using cloud parameters The impact of aerosol on cloud and precipitation is one of the most challenge problems. Among the studies in this aspect, the Indian Ocean Experiment (INDOEX) in the 1990s is most representative, which revealed for the first time the effect of aerosol emission from the urban region of the Indian subcontinent in dry season on downwind clouds and precipitation over the oceanic region. There appeared an obvious gradient in aerosol concentration, more in north and less in south, along the wind direction from the subcontinent southward to the southern Indian Ocean. Notable gradients in liquid water content, cloud droplet size, and other cloud parameters were also observed by airborne instruments and retrieval results (Liu et al., 2001). The interaction between the aerosol and clouds formed the most typical balance in atmospheric radiation in this region (Rhoads et al., 1997). Similar experiments were carried out in East Asia and offshore regions of Northwest Pacific, such as the APEX (Asian

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Atmospheric Particle Environmental Change Studies), ACE-Asia (Asia–Pacific regional Aerosol Characterization Experiments), and TRACE-P (Transport and Chemical Evolution over the Pacific) (Huebert et al., 2003; Jacob et al., 2003; Nakajima et al., 2003; Seinfeld et al., 2004). The results of ACE-Asia have proved that the mixture of dust, black carbon, sulfate, and nitrate in the Asian Pacific area has caused the instability of regional aerosol optical properties, and the clear sky direct radiative forcing in spring in this region far exceeds the global average (Remer and Kaufman, 2006; Yu et al., 2006). The joint aerosol observation experiment conducted by China and the USA in 2004 also revealed the unique role of urban haze aerosol on regional radiative forcing in Asia (Li et al., 2007; Xin et al., 2007). In fact, the region offshore East China is an ideal place to investigate the interaction between aerosols and cloud parameters because westerly brings the aerosols that are emitted from inland to this place. As an example, Fig. 3 displays wind streamlines at 850 hPa on 10 July 2001 in Northeast Asia and the distribution of mean aerosol optical depth issued by MODIS for 8–10 July 2001 over North China, Shandong, and Jiangsu. The air flows southward from the Shandong Peninsula to the Yellow Sea, as shown in Fig. 3. During this period, non-precipitation clouds over the Yellow Sea near south of the Shandong Peninsula were observed by VIRS (Figs. 4a–c). Figure 4d shows the retrieved Re by the BSR algorithm. The brightness temperature of the clouds in regions A and B is about 280 K, and the reflectance at visible bands is greater than 0.5, which indicates that water clouds are popular in this case. The Re in the windward cloud belt (region A) is 10 μm lower than in the leeward side (region B). It may be speculated that the size of cloud particles is reduced in region A due to impact of aerosols emitted from the Shandong Peninsula. The studies about the interaction between aerosols and cloud parameters on the eastern coast of China are ongoing. The effect of aerosols on precipitation remains uncertain from the observational point of view. A few investigations suggested that precipitation is inhibited

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Fig. 3. (a) Wind streamlines at 850 hPa on 10 July 2001 over Northeast Asia and (b) aerosol optical depth averaged from 8 to 10 July 2001.

Fig. 4. Signals detected by (a) visible, (b) infrared, and (c) thermal infrared channels of VIRS; and (d) retrieved Re.

by aerosols (Albrecht, 1989; Rosenfeld, 1999), while others found that precipitation intensity rises because of the aerosol effect (Koren et al., 2012). Disagreement remains about the aerosol effect on rainfall locations (Lowenthal and Borys, 2000; Givati and Rosenfeld, 2004; Lynn et al., 2007). Due to the differences

in aerosol hygroscopicity, shortwave absorptivity, and aerosol size, aerosols impact cloud parameters in different ways during the process of precipitation. That is why there are many forms of the aerosol effect on precipitation (Paldor, 2008). It is also revealed that more small droplets and richer ice particles exist inside

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stratiform precipitating clouds in the region surrounded by dense mineral and dust aerosols, compared with the case in clean regions (Min et al., 2009). In monsoon regions, the interactions among cloud parameters, aerosols, and monsoon is worth investigating. For example, how would the cloud parameters vary under the effect of aerosols on the monsoon activity? How will the changed cloud parameters impact on the monsoon activity? Preliminary studies have shown that significant differences of cloud parameters such as cloud optical depth, cloud ratio, cloud height, and so on occurred over the central and northeastern India and the equatorial Indian Ocean, due to different aerosol effects on the Indian monsoon bursts and interruption (Kiran et al., 2009). As pointed out by Koren et al. (2012), our knowledge on the influences of aerosols on clouds (especially precipitation) is not even close because such influences vary with many factors such as geographic location, season, and spatial and temporal scales. Furthermore, it is an antinomy to make simultaneous observations of cloud and aerosol. In cloudy sky, satellite spectrum is unable to penetrate cloud to obtain the aerosol information below the cloud, meaning great challenges to study the cloud–aerosol interactions in cloudy conditions. However, long-term observations from the multiple sensors aboard satellites have the advantage of wide coverage and coherent measurement standards. If they are combined with other meteorological data such as groundbased observations and reanalysis data, it is possible for us to acquire useful information on cloud–aerosol interactions. 4. Relationship between cloud parameters and precipitation In the process from formation of clouds to occurrence of precipitation, knowledge on the actual linkage between precipitation and cloud parameters is limited because of the complicated microphysics inside the cloud. Under the ideal hypothesis of cloud physics, Ronsenfeld et al. (1989) suggested that the features of cumulus cloud and the formation of precipitation can be identified by the relationship between

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infrared bright temperature (T ) and Re (Rosenfeld and Lensky, 1989; Rosenfeld et al., 1994; Lensky and Rosenfeld, 1997, 2003; Woodley et al., 2000). This method provides an approach for us to study the relationship between cloud parameters and precipitation. Restricted by many factors, the hypothesis mentioned above needs to be further tested by more experiments as well as observations. Nowadays, the available information of cloud parameters can only be obtained by finite flight observations (Woodley et al., 2003; Rosenfeld et al., 2006; Zhou et al., 2010), and study of the cloud microphysical processes depends more on the simulations by numerical models (Hu et al., 1983; Xiao et al., 1988; Reisin et al., 1996; Guo, 1999; Lei et al., 2008). To better understand the relationship between cloud parameters and precipitation, it is necessary to retrieve the cloud parameters together with the rain intensity and type at the same time. However, the conventional ground-based observations cannot provide all these properties. The combined observations by ground-based precipitation radar and cloud radar may be helpful to overcome this difficulty for a limited area. Using the almost simultaneous measurements from the precipitation radar (PR) and VIRS onboard TRMM, joined with the cloud parameter retrieval method mentioned above, may be a good way to reveal the relationship between cloud parameters and precipitation over a larger region. For this purpose, the author merged the visible/infrared signals measured by VIRS (standard product 1B01) with precipitation profiles derived from PR (standard product 2A25), and obtained a new dataset of precipitation profiles (with a horizontal resolution of 4.5 km) in parallel with signals of visible/infrared in 5 channels (Fu et al., 2011). Then, Re and τc of cloud particles were retrieved by the BSR method, based on this dataset. As a result, Fig. 5 shows the spatial distribution of rain rate, Re, τc , and LWP in January 1988 in the tropics and subtropics. A good correlation between cloud parameters and precipitation is revealed, because PR can distinguish effectively the precipitating cloud from the non-precipitating one. Figure 5 also clearly shows the location of rain band in equator and to its south,

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Fig. 5. Spatial distributions of (a) rain rate, (b) Re, (c) τc , and (d) LWP in January 1998. The data are derived by merging the precipitation radar (PR) and the visible/infrared scanner (VIRS) together with use of the BSR algorithm.

i.e., the ITCZ precipitation in winter. The mean intensity of the rain band varies within 3–10 mm h−1 , with the maximum exceeding 10 mm h−1 . Outside the ITCZ, the mean rain intensity is small except for Africa and South America, which are southward away from the equator. The mean rain rate over East China is about 1 mm h−1 . The mean Re retrieved by the visible and infrared channels of VIRS varies between 10 and 50 μm, and the mean Re corresponding to larger precipitation in the ITCZ can be more than 15 μm. At 36◦ N south of East China, the mean Re is usually smaller than 15 μm while it is greater than 15 μm in Africa and South America (regions southward away from the equator). The mean τc is larger than 50 and 90 in the ITCZ and in equatorial western Pacific, respectively. Although the mean rain intensity and Re at 36◦ N south of East China are small, the mean τc in this region is very large. This may be related to the high aerosol content in this region, which needs to be further analyzed. Because LWP is proportional to the product of Re and τc , its spatial distribution is similar to that of Re and τc , with more details omitted here. To reveal the relationship between cloud parameters and the precipitation structure, a frontal cyclone observed by PR and VIRS in the Jianghuai region at 1417 BT (Beijing Time) 22 June 2003 is taken as an example (Zheng et al., 2004). Figure 6 shows the rain rate derived from PR, the visible reflectance and ther-

mal infrared radiative temperature observed by VIRS, the Re and LWP retrieved by the BSR algorithm. The precipitation intensity near the cyclone center and the cold front reaches over 40 mm h−1 , while in the warm front it is less than 8 mm h−1 . Corresponding to large rain rate in the center, lower thermal infrared temperature (mostly lower than 220 K) appears there, i.e., the cloud top is high or the cloud is thick around the center. The retrieved Re and LWP distributions reveal that some differences exist in cloud particle size and cloud water content between the cyclone center and the frontal area. The probability distribution function (PDF) of Re and LWP near the cloud top for the convective and stratiform precipitation in the frontal cyclone is presented in Figs. 7a and 7b, respectively. It is indicated that Re varies mainly between 15 and 25 μm while the peak of LWP for convective precipitation is 200 g m−2 , higher than that for stratifrom precipitation (Fig. 7b). It is believed that the liquid water content in convective precipitating cloud is larger than that in stratiform cloud for this frontal cyclone. Ideally, accurate descriptions of vertical distribution of cloud parameters and their relationship with the intensity and type of rain will be helpful for precipitation parameterization in numerical models. However, it is difficult for us to obtain these descriptions based on the existing technology. Therefore, it may be a shortcut to examine the relationship between cloud

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parameters and precipitation vertical structures by analyzing the precipitation profiles associated with different Re and LWP values near the cloud tops. In

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light of PDF for Re and LWP in Fig. 7, we present the mean rain rate profiles for convective and stratiform precipitation with different Re and LWP values

Fig. 6. (a) Rain rate, (b) brightness temperature at 10.8 μm, (c) Re, and (d) LWP in the Jianhuai region (from orbit 31925).

Fig. 7. Probability distributions of (a) Re and (b) LWP in a frontal area.

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in Fig. 8. For convective precipitation, the mean profile under high Re and LWP displays the deepest precipitating clouds and the highest surface rain rate. In contrast, the mean profile for low Re and LWP shows relative shallower precipitating clouds and lower surface rain rate. However, this relationship is not robust for the stratifrom precipitation. The Re and LWP near the top of stratiform precipitating clouds can only indicate the intensity of surface rain rate but not the thickness of cloud. For statistical significance, large samples of both convective and stratiform precipitation cases are examined in the following three regions, and the results are plotted in Fig. 9. The three regions include Jianghuai (23◦ –34◦ N, 117◦ –119◦ E), South China (25◦ –29◦ N, 116◦ –119◦ E), and the warm pool (WP) area of the western Pacific (0◦ –2◦ N, 140◦ –150◦ E). Summer cases from 1998 to 2007 are considered. In view of the differences in the microphysical processes between water cloud (temperature higher than 268 K at 10.8-μm channel, usually named as shallow precipitating cloud) and non-water cloud (ice cloud and

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mixed cloud of ice and water, named as deep precipitating cloud here), precipitating clouds of convective and stratiform cases are classified into two sub-types, water cloud and non-water cloud. For water cloud, Re of convective and stratiform precipitation decreases with increasing rain rate in the three regions, especially in the WP area. This may result from the limited vertical extent in water cloud, which suppresses the cloud particle growth. The mechanism of this kind of precipitation still needs to be further studied. For deep precipitating cloud, Re of convective precipitation in the WP area seems not varing with increasing precipitation intensity. This may be caused by the process of droplet increase such as coagulation mainly happens in the lower and middle of the cloud layer, while particles near the cloud top remain almost stable. Re of stratiform precipitation decreases with increasing rain rate in the WP region, possibly due to the weak updraft inside the oceanic stratiform clouds. Ordinarily, oceanic stratiform precipitation is generated from dissipating stage of the convective life

Fig. 8. Mean profiles of (a, c) convective and (b, d) stratiform clouds under different Re and LWP values in a frontal cyclone.

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Fig. 9. The relationship between Re and rain rate for (a, b) water cloud and (c, d) non-water cloud in summer over the Jianghuai (JN), Huanan (HN), and the warm pool (WP) area of western Pacific, respectively. (a, c) Convective precipitation and (b, d) stratiform precipitation.

cycle. The weakening updraft makes large droplets move down quickly with the earth’s gravity. Consequently, the surface rain rate increases while Re near the cloud top decreases. In Jianghuai region and South China, Re of convective precipitation increases with increasing surface rain rate, which may be caused by the stronger updraft bringing larger droplets in the middle or lower cloud layer up to the near-top layer. In the circumstances of deep stratiform precipitating cloud in both regions, Re in South China also increases with increasing surface rain rate. Relative large Re near the cloud top may be caused by the strong updraft forced by mountainous topography there. A unique relationship between Re and surface rainfall intensity for stratifrom precipitation occurs in the Jianghuai region. As surface rain rate is less than 2 mm h−1 , Re becomes smaller with increasing rain rate; afterward, it becomes larger slowly with increasing rain rate. The mechanism of this is still unclear. Moreover, Fig. 9 also shows that for the same surface

rain rate, Re of convective and stratifrom precipitation in the WP area is 3–5 m larger than that over inland areas, the minimum Re of the deep and shallow convection and shallow stratiform precipitation appears in the Jianghuai region. Whether the difference of Re between land and ocean is caused by the aerosol indirect effect remains to be further studied. In terms of the relationship between LWP and surface rain intensity, as shown in Fig. 10, the larger the surface rain intensity is, the enhanced LWP the clouds have, which is reasonable in physics. This relationship prevails in precipitating clouds with water phase or non-water phase. But there are still regional differences. For the same surface rain intensity, the LWP in water clouds in Jianghuai and South China is 0.2–0.4 kg m−2 higher than that in the WP area. In deep precipitating clouds, increased LWP is also needed in clouds over Jianghuai, especially for the convective precipitation, at the same surface rain rate. The underneath mechanism needs to be studied in de-

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tail. Due to the inverse relationship between the radiative temperatures at the thermal infrared channel 10.8 μm and at the cloud top height, temperatures at this channel can be used to represent the height of cloud top. Figure 11 shows the variation of LWP with the temperature at this channel for convective

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and stratiform precipitation, which may be regarded as the LWP distribution with the height of cloud top, i.e., the LWP varies with different thicknesses of the precipitating cloud. In Jianghuai and South China, the LWP increases with temperature decreasing from 280 to 220 K (i.e., rise of cloud top) at the 10.8-μm channel. When the temperature is below 220 K, the

Fig. 10. As in Fig. 9, but for the relationship between LWP and rain rate.

Fig. 11. The relationship between LWP and brightness temperature at 10.8 μm in summer over Jianghuai (JN), Huanan (HN), and the warm pool (WP) area of western Pacific, respectively. (a) Convective precipitation and (b) stratiform precipitation.

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LWP remains unchanged. In the WP area of the western Pacific, the LWP increases with the rising cloud top. The above analysis indicates the differences of cloud water content in vertical direction with the cloud top height between land and ocean. Furthermore, as the temperature of cloud top is higher than 220 K, the maximum and minimum LWPs occur in the Jianghuai and WP area, respectively, and moderate LWP in South China, for the same cloud top height. These results suggest that cloud water content varies in different regions, which directly impacts the surface rain intensity. The more detailed studies in this aspect are still undergoing. It is well known that the first PR together with VIRS and other instruments are onboard the TRMM satellite. The effective way is to merge the observations measured by these instruments with the cloud parameters retrieved from the algorithms based on spectrum observations. Then, a new dataset containing rain types and precipitation profiles corresponding to their cloud parameters will be set up, which can overcome nonsynchronous shortages of precipitation properties and cloud parameters in the past. This dataset will help us to solve the problems such as the difference of cloud parameters between precipitating and non-precipitating clouds, the relationship between rain intensity and cloud parameters, and so on. This is the author’s primitive motivation to write this article. More detailed studies are on going in rugged ways. 5. Conclusions Using observations of visible and infrared channels to retrieve cloud parameters is and will still be a dominant approach to understand natures of the cloud system, since these channels remain to be the main working way in spectrum instruments onboard geostationary and polar orbit satellites, especially with consideration of the advantage of high temporal resolution in geostationary satellites. Consequently, taking full advantage of the data retrieved from visible and infrared measurements, developing the cloud parameter retrieval methods, and integrating the data

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supplied by other instruments such as precipitation radar and cloud radar, have great significance to the studying of cloud properties, their radiative effects, aerosol indirect effects, and so on. These are helpful to improve numerical models and enhance their abilities in weather forecast and climate prediction. In this paper, we have reviewed the principle of the BSR retrieval algorithm for cloud parameters, and displayed the BSR-retrieved cloud parameters for a case observed by VIRR. The domestic and foreign advances in studies of aerosol indirect effects from the perspective of cloud parameter analysis in recent years are summarized. Moreover, a case is used as an example to present the impact of inland aerosol on cloud parameters in offshore China. The results show that the cloud particle effective radius is reduced by 10 μm due to the aerosol influence. Moreover, the relationship between cloud parameters and rain intensity, type, and structure is introduced. The PR and VIRS data are merged, together with the cloud parameters retrieved by the BSR. The results indicate that Re of precipitating clouds in the tropics and subtropics varies from 10 to 50 μm, and τc of heavy precipitating cloud is larger than 50. It is also found that the average precipitation intensity and Re in precipitating clouds are small with large τc (> 90) at 36◦ N south of East China. This may be related to the higher content of aerosols in the region. The case analysis shows that cloud effective radius and liquid water path near the cloud tops are good indicators of the thickness and intensity of convective clouds. The Re of water cloud reduces with the rain rate increasing in the Jianghuai area, South China, and the warm pool area of western Pacific. In the warm pool area, as rain rate increases, Re of deep convection seems unchanged while Re of stratiform precipitation reduces slightly. Re of deep convection becomes larger with the rain rate increasing in the Jianghuai area and South China. As for the stratiform precipitation over Jianghuai, the relationship between Re and rain rate is complicated. These mechanisms still need to be investigated. Because the space borne spectroradiometer is unable to capture the information inside the cloud, the retrieval algorithm of cloud parameters based on

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merged data, which contain the measurements of space borne spectroradiometer, passive micorwave radiometer/imager, precipitation radar, cloud radar, and laser radar, needs to be further developed. With such an advanced algorithm, not only the cloud parameters near the cloud tops but also the profiles of cloud parameters inside the cloud can be obtained, which is essential to retrieving precipitation intensity and related latent heat release. This is a challenging research direction for the next 10 or more years. Fortunately, the performance of the multiple sensors aboard FY series satellites has been greatly improved (Yang et al., 2012; Zhang et al., 2012a, b). Taking full advantages of these satellite data will enhance our ability in satellite data application. China is located in the typical monsoon region where the clouds have remarkable regional differences, and variations of the cloud system are controlled by the monsoon activities. To select suitable locations representing typical weather systems and to establish ground-based observation stations (super stations) with comprehensive instruments are the necessary approach to observe cloud structures and other cloud properties, and validate the retrieved cloud parameters, based on the remote sensing by multiple sensors aboard the satellites. Meanwhile, to build airborne systems including spectroradiometer and active and passive microwave instruments, is also necessary for verification of the retrieval results, which will signify a nation’s capability in development of the atmospheric science.

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