• <tr id="yyy80"></tr>
  • <sup id="yyy80"></sup>
  • <tfoot id="yyy80"><noscript id="yyy80"></noscript></tfoot>
  • 99热精品在线国产_美女午夜性视频免费_国产精品国产高清国产av_av欧美777_自拍偷自拍亚洲精品老妇_亚洲熟女精品中文字幕_www日本黄色视频网_国产精品野战在线观看 ?

    Underestimation of Oceanic Warm Cloud Occurrences by the Cloud Profiling Radar Aboard CloudSat

    2015-01-05 02:01:53LIUDongyang劉東陽LIUQi劉奇andZHOULingli周伶俐
    Journal of Meteorological Research 2015年4期
    關(guān)鍵詞:劉奇東陽

    LIU Dongyang (劉東陽),LIU Qi(劉奇),and ZHOU Lingli(周伶俐)

    School of Earth and Space Sciences,University of Science and Technology of China,Hefei 230026

    Underestimation of Oceanic Warm Cloud Occurrences by the Cloud Profiling Radar Aboard CloudSat

    LIU Dongyang (劉東陽),LIU Qi?(劉奇),and ZHOU Lingli(周伶俐)

    School of Earth and Space Sciences,University of Science and Technology of China,Hefei 230026

    The Cloud Profiling Radar(CPR)onboard CloudSat is an active sensor specifically dedicated to cloud detection.Compared to passive remote sensors,CPR plays a unique role in investigating the occurrence of multi-layer clouds and depicting the internal vertical structure of clouds.However,owing to contamination from ground clutter,CPR reflectivity signals are invalid in the lowest 1 km above the surface,leading to numerous missed detections of warm clouds.In this study,by using 1-yr CPR and MODIS(Moderate Resolution Imaging Spectroradiometer)synchronous data,those CPR-missed oceanic warm clouds that are identified as cloudy by MODIS are examined.It is demonstrated that CPR severely underestimates the occurrence of oceanic warm clouds,with a global-average miss rate of about 0.43.Over the tropical and subtropical oceans,the CPR-missed clouds tend to occur in regions with relatively low sea surface temperature.CPR misses almost all warm clouds with cloud tops lower than 1 km,and the miss rate reduces with increasing cloud top.As for clouds with cloud tops higher than 2 km,the negative bias of CPR-captured warm cloud occurrence falls below 3%.The cloud top height of CPR-missed warm clouds ranges from 0.6 to 1.2 km,and these clouds mostly have evidently small optical depths and droplet effective radii.The vertically integrated cloud liquid water content of CPR-missed warm clouds is smaller than 50 g m?2.It is also revealed that CPR misses some warm clouds that have small optical depths or small droplet sizes,besides those limited in the boundary layer below about 1 km due to ground clutter.

    oceanic warm cloud,Cloud Profiling Radar,MODIS,cloud occurrence underestimation

    1.Introduction

    Clouds play a significant role in the energy balance of the earth-atmosphere system.They affect the earth’s radiation budgets by reflecting solar shortwave radiation and absorbing/emitting longwave radiation. Cloud processes also promote vertical exchanges of water between the surface and atmosphere and thus drive the global water cycle.Since clouds develop in atmospheric flow fields and react to those fields through heating and cooling effects by radiation and phase transformation,complex cloud feedbacks have been considered as a major source of uncertainty in climate models(Bony and Dufresne,2005).Barker et al.(1999)found that for clear-sky conditions that are independent of cloud feedbacks,the discrepancy in surface radiation flux among general circulation models(GCMs)is several W m?2,while it could reach 100 W m?2for simulations under cloudy sky conditions.Comparing 10 atmospheric GCMs with satellite measurements,Zhang et al.(2005)found that the simulated seasonal variations of low-level clouds were generally poor compared with high-levelclouds.These findings all indicated that numerical models need further improvement to accurately represent cloud physical properties and their development processes involving cloud feedbacks.

    Warm clouds are defined as those clouds located below the freezing level and consist exclusively of liquid droplets.Since the conditions for warm cloud formation are more attainable compared to middle and high clouds,they tend to occur more frequently in theatmosphere,resulting in significant effects in the global radiation budget.Especially over the ocean, benefiting from the adequate water supply,cumulus,stratocumulus,and stratus clouds occur in very large numbers,and single-layer oceanic stratus clouds cover nearly a third of the area of the global ocean (Charlson et al.,1987).In particular,over the tropical and subtropical oceans,warm clouds have essential effects in the radiation budgets and feedbacks of the earth-atmosphere system,due to their remarkably high reflectivity in contrast to the sea surface (Hartmann and Short,1980).Furthermore,since warm clouds are much closer to the surface,their physical properties are more susceptible to aerosols, which play an essential role in aerosol-cloud interactions.The aerosol indirect effect,which is closely related to warm clouds,is also an important aspect of anthropogenic climate forcing.Some proposed geoengineering methods aimed at mitigating the greenhouse effect are in fact based on interactions between artificial aerosols and warm clouds,which lead to increasing cloud albedo and thus reducing shortwave radiation absorption.Besides,warm clouds and their accompanying weak precipitation processes have very important impacts on the dynamic processes in the atmospheric boundary layer.Hence,accurately describing warm clouds and their related physical and dynamic processes is very meaningful to improve the reliability of climate model projects.

    Satellite remote sensing has provided an excellent opportunity to further understand warm clouds, especially oceanic warm clouds on the global scale. The Earth Observing System(EOS)satellites Aqua and CloudSat are two important members of the ATrain constellation of satellites.At present,the Moderate Resolution Imaging Spectroradiometer(MODIS) aboard Aqua and the Cloud Profiling Radar(CPR) aboard CloudSat,constitute the main data source for global clouds(Weisz et al.,2007).With abundant spectral information for the retrieval of cloud properties ranging from the visible spectrum to the infrared band,MODIS is the representation of passive remote sensing in cloud measurements.CPR is a nadirpointing radar working at the millimeter-wavelength. It thus has higher sensitivity to small cloud droplets than the commonly used centimeter-wavelength radar, which is merely sensitive to precipitation-sized hydrometeors.In addition,CPR can distinguish multilayer clouds from single-layer clouds and obtain cloud vertical structure in its available resolution.In recent years,by employing CPR observations,many studies have been carried out to investigate the vertical structure characteristics of clouds and precipitation. Luo et al.(2009)and Wang et al.(2011b)analyzed cloud amounts,vertical distribution,and their seasonal variation characteristics in the Asian monsoon region.Also focusing on the Asian monsoon region, Wang et al.(2011a)studied the distribution of low clouds and the correlation between seasonalvariations and atmospheric instability in the lower troposphere. Han et al.(2013)conducted a comprehensive study to investigate the characteristics of clouds,precipitation,and the thermodynamic structure of typhoons at different stages in the eastern Pacific Ocean by using a CloudSat tropical cyclone dataset.Yi et al.(2014) reported the seasonal occurrences of deep convective clouds and their relationship with cyclonic activity in the northern Pacific.In addition,Peng et al.(2013) and Yin et al.(2013)investigated the vertical distribution and seasonal variations of precipitating and non-precipitating clouds,single-layer and multilayer clouds,by using vertical profile data form CPR measurements.

    In principle,as an active radar,CPR is superior to passive sensors for detecting cloud vertical structures.For instance,MODIS obtains cloud water content through microphysical information near the top of clouds,while CPR resolves the vertical variation of cloud water content in the whole column.Especially for warm clouds in the lower atmosphere,which are often covered by middle-and high-level clouds, MODIS-retrieved results have potentially large biases because of the single-layer cloud assumption in the MODIS algorithm.However,detections by CPR in the lower atmosphere are inevitably restricted by ground clutter and the signal-to-noise ratio.Thus,there is a severe obstacle for CPR to detect these very low clouds.Mace et al.(2007)found that CPR measurements in the lowest two or three range resolution volumes above the sea surface are contaminated byground clutter,and this disturbance could extend to approximately 1 km.It was estimated that approximately two thirds of warm clouds located below 1 km tend to be undetected due to strong ground clutter that overwhelms the cloud echo signals(Marchand et al.,2008).For warm clouds in the tropical eastern Pacific Ocean,Kuber et al.(2011)found that CPR only detected one third of those identified by MODIS.Furthermore,as revealed by Chan and Comiso(2011),CPR also missed some geometrically thin,low-level clouds in high latitude and polar regions,while MODIS identified these clouds explicitly and provided unambiguous cloud property retrievals, except for optically thin clouds with a cloud optical depth smaller than 0.4.In the present study,we find that situations in which CPR identifies clear sky while MODIS identifies cloudy sky are very common over the oceans in low and middle latitudes.Undoubtedly, the failure of CPR to identify clouds that are located below the ground-clutter zone could cause underestimations of the occurrences of global oceanic warm clouds in CPR datasets.Nevertheless,CPR’s underestimation of warm clouds is stillunclear on the global scale.Knowledge on the properties of these CPR-missed warm clouds is hitherto very limited,which has a direct impact on the reasonable application and correct understanding of CPR measurements.

    Since CloudSat launched and began to release data in 2006,many studies of the macrophysical and microphysical properties and vertical structures of clouds based on CPR datasets have been conducted. It thus becomes increasingly important to clearly understand the accuracy,representation,and shortcomings of these datasets.The present study addresses the aforementioned problems by attempting to quantify the CPR miss rate with respect to oceanic warm clouds and clarify its impacts.The results could help in obtaining more accurate knowledge on the occurrences and properties of global oceanic warm clouds.

    2.Data and methods

    The data analyzed in this study consist of the CPR 2B-GeoProf(Geometric Profile)datasets, MODIS cloud detection,and retrieved cloud properties.The atmospheric vertical temperature profile data from the ECMWF are combined with the cloud top temperature(CTT)from MODIS to estimate the cloud top height(CTH).All datasets used here cover the whole year of 2008.

    2.1 CPR dataset

    CPR is a 94-GHz nadir-looking millimeterwavelength radar aboard CloudSat that was launched in April 2006.CloudSat has an orbit altitude of about 705 km,an orbit inclination angle of 98.2°,and a period of 98.9 min.With a high sensitivity at-30 dBZ, CPR can obtain effective echo signals from small-sized cloud droplets and achieve straightforward cloud identification.CPR acquires 125 data samples for each profile ranging from the sea surface level to 30 km, with a vertical resolution of approximately 240 m. The effective field of view of CPR is approximately an oval,with a footprint of 1.7-km along-track and 1.4-km across-track.The along-track interval between adjacent pixel centers is about 1.1 km.

    The main CPR product used is the operational 2B-GeoProf,which contains a“cloud mask”assigned for each range bin.The cloud mask has a confidence coefficient between 0 and 40,with higher values corresponding to increasing confidence of cloud presence. Marked values of 30 and 40 have low false detection rates at 0.043 and 0.006,respectively,which basically assures the presence of clouds(Marchand et al.,2008).

    One ofthe reasons for CPR failing to detect cloud might be because it cannot obtain effective echoes in the ground-clutter zone.The averaged thickness of the near-surface blind zone of CPR is shown in Fig. 1 on a 2.5×2.5 grid(the same configuration for the following global distributions).Such a thickness is derived from all the clear-sky cases,and defined as the averaged height of the top bin that has much larger echo intensity than the clear-sky background.

    Affected by multiple factors,the thickness of the CPR blind zone has notable spatial variations over the oceans that have relatively smooth geomorphology.Such a spatial distribution is largely related tothe sea surface elevation distribution(Tanelli et al., 2008).Over the global ocean,the averaged blind-zone thickness ranges from 400 to 650 m,corresponding to the detection failure of CPR in the first 1-3 bins from the sea surface.This means that the first entirely unaffected bin in the CPR vertical profile is at least the fourth bin above the sea surface,which is located at about 1 km.Thus,the instances of missed cloud detection are concentrated below 1 km in the CPR observations.In addition,considering the restriction of detection sensitivity,CPR also cannot detect some very thin clouds with rather low cloud water content. These missed-cloud cases due to limited sensitivity are known to be very common for CPR in detecting thin cirrus clouds(Marchand et al.,2008).

    2.2 Collocation of MODIS and CPR measurements

    MODIS is the main sensor aboard the NASA EOS Terra and Aqua satellite platforms,which acquires a global coverage every one or two days with a swath width of 2330 km.MODIS measures the upward radiances from the surface to atmosphere in 36 spectral bands,ranging from 0.4 to 14.2μm.Cloud detection by MODIS is achieved on a scale of 1 km,resulting in the cloud mask product,and for each cloudy pixel a series of cloud property retrievals were derived by using the operational cloud algorithm(Platnick et al.,2003).According to comparison between groundbased radars,airborne radars,and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO),the accuracy of MODIS cloud detection is more than 90%over the low-latitude and midlatitude oceans(Ackerman et al.,2008;Holz et al.,2008). In this study,we regard the MODIS cloud detection results as the true value,using them as the basis for comparison with the CPR cloud detection results.

    The standard pixel-level cloud products of MODIS are archived in the level-2 MODIS Collection 5 standard cloud product(MYD06?L2).The MODIS data required for the present study are only spatiotemporally matched with the CPR track.Therefore,the narrow-swath(about 10 km)MODIS/Aqua subset along the CloudSat track,officially termed as the MODIS/Aqua Clouds 1 and 5 km 5-Min L2 Wide Swath Subset along CloudSat V2(MAC06S0), is used in this study.The subset data provide various cloud parameters from MODIS,e.g.,cloud detection,cloud-top temperature(CTT),cloud optical depth,and cloud water path,which are in 1-or 5-km resolution.For each cloud parameter,the MAC06S0 dataset has either 3 pixels in the 5-km resolution or 11 pixels in the 1-km resolution.The MODIS and CPR pixels were collocated according to the minimum distance matching method.In the A-Train satellite constellation,CloudSat lags the Aqua orbit by approximately 60 s,which ensures the temporal synchronization of these two datasets.This con-figuration ensures a better retrieval validity of cloud parameters,since the parallax effect from MODIS is negligible in the middle part of the across-track scan (Chan and Comiso,2011).Meanwhile,the retrieval of warm cloud properties based on MODIS multi-spectral data carries much less uncertainty than ice and mixedphase clouds.Hence,CPR and MODIS provide the cloud detection and cloud parameters,respectively,for the same pixel,and such a combined dataset forms the basis for this study.

    Fig.1.Averaged thickness of the CPR blind zone caused by ground clutter over the global ocean on a 2.5°×2.5°grid.

    2.3 ECMWF dataset

    The ERA-Interim reanalysis,produced by the ECMWF,is a global atmospheric reanalysis dataset from 1979,continuously updated in real time.In this study,we use the globalsea surface temperature(SST) dataset and the atmospheric temperature profile from the surface to 500 hPa at a spatialresolution of0.75°× 0.75°.It was combined with the CTTs from MODIS to calculate the cloud-top heights(CTHs).Considering the rough horizontal resolution of this dataset,they are collocated to every pixel of the combined satellite data.

    2.4 Isolated warm clouds

    In most analyses of satellite cloud data,the identification ofwarm cloud is simply based on CTT tested at the pixel level.All cloud pixels with CTT greater than 273 K are classified as warm cloud samples.However,many investigations have demonstrated that, over the globalocean and especially the tropicalocean, it is a widespread feature that warm cloud pixels are connected to cold ones.These warm cloud pixels are distinct from true warm clouds,considering their different thermal and dynamic conditions.The abovementioned cloud systems that contain both warm and cold cloud pixels are mostly at the transition stage in the development of deep cloud systems,which have completely different evolution processes compared with isolated warm clouds(Schumacher and Houze,2003;Liu and Zipser,2009).Thus,to a certain extent,analyses of oceanic warm clouds can become contaminated and unrepresentative if all pixels with CTTs greater than 273 K are simply classified into warm clouds.In order to avoid the above problems, we define isolated warm clouds in this study,which are spatially continuous and compose exclusively of warm cloud pixels.Unlike the general definition at the pixel level,the definition of warm clouds here is at the object level.Correspondingly,a single object of warm cloud consists of one or more spatially continuous warm cloud pixels.

    Hence,the isolated warm clouds over the ocean were identified and samples of warm clouds created. Considering that CPR only forms one-dimensional data in the horizontal plane,the continuity filter of warm cloud pixels is solely based on the single dimension.

    For each pixel in the isolated warm cloud determined by MODIS,it is defined as a CPR-captured cloud if CPR detects the cloud and identifies the CTH as below 4.5 km.Conversely,if CPR identifies the MODIS cloud-pixel(MODIS cloud fraction greater than 0.9)as clear sky,such a pixel is defined as CPR-missed cloud.It should be pointed out that the above classification rules out situations of multilayer clouds, because the retrieval of cloud properties from MODIS is very likely unrepresentative in this case.Hence, those cases in which CPR detects the presence of midor high-level cloud but misses the warm cloud in the lower layer are not investigated in this study.

    2.5 Calculation of CTH

    For CPR-captured clouds,the CTH is explicitly defined as the height of the top-down first bin that has a CPR cloud mask greater than or equal to 30.As for CPR-missed clouds,the MODIS CTT retrievals were used to estimate the CTH as follows:

    With a relatively accurate CTT,the determination of the CTH depends mainly on the local vertical temperature structure,although it is very difficult to accurately describe the thermal structure in the lower troposphere,particularly within the atmospheric boundary layer.Therefore,a common method is to combine the CTT,near-surface air temperature and proper lapse rate profile to calculate the CTH(e.g. Sun-Mack et al.,2008;Zuidema et al.,2009;Minnis et al.,2011).Relying on the global boundary layer temperature lapse rate from the Clouds and Earth’s Radiant Energy System(CERES)cloud property re-trievalsystem(CCPRS),Sun-Mack et al.(2014)compared the CTHs derived from the above methods and CALIPSO direct measurements.They found that, over the ocean,the differences in CTH between those derived from the CCPRS-2(CCPRS-4)lapse rate profiles and those derived from CALIPSO were within 0.2(0.1)km,with a standard deviation near 0.6 km. In this study,we also combine the lapse rate from CCPRS,CTT from MODIS,and the atmospheric temperature at 1000 hPa from ERA-Interim,to calculate the CTH.It should be noted that there are still certain deviations in the calculation of CTH,as in the following aspects:(1)The CTT itself may deviate considerably,caused by the presence of an inversion layer near the top of the atmospheric boundary layer and the transmission of the infrared radiation emitted from the sea surface through the optically thin clouds. (2)The default hypothesis in the method is the equality between CTT and ambient air temperature around the top of the cloud.However,in practice,they are generally different,which might cause a deviation of about 0.15 km(Zuidema et al.,2009).(3)The lapse rate from CCPRS only reflects the variable relationship of the atmospheric temperature profile along with latitude,which is still apart from the real lapse rate in local regions.However,these are all random errors and would not form consistent system deviations in the estimation of CTH.

    3.Results

    3.1 Occurrence frequency characteristics

    Figure 2 shows the global distribution of the occurrence frequency of CPR-missed(Fig.2a)and CPR-captured clouds(Fig.2b)over the whole year of 2008.The oceanic warm clouds are mainly located over subtropical oceans,in 20°-30°north and south of the equator.Especially over the extensive Pacific Ocean,there are clearly more warm clouds located in Northeast and Southeast Pacific(Jensen et al.,2008;Zhang et al.,2010).On the contrary,few occur in the North Pacific storm track and the deep convective regions of the equator,western Pacific,and the intertropical convergence zone.Hence,the basic spatial distribution pattern of global warm clouds is approximately opposite to the high incidence areas of deep convective clouds and precipitation,which is also reflected in the completely different background flow fields for these two typical clouds over the ocean(Liu et al.,2010a).

    As for the difference between Figs.2a and 2b, CPR-missed clouds are primarily present over relatively colder waters in the eastern ocean,usually adjacent to the west of continents.There are more CPR-captured clouds occurring in the middle of the tropical and subtropical ocean.Although high incidence areas of CPR-missed and CPR-captured clouds are very close,the latter is larger and has higher occurrence frequency.In high incidence areas,the occurrence frequency of CPR-captured clouds can reach more than 5%,while CPR-missed clouds are concentrated within 1.6%-3.0%.In the central ocean,CPR-captured clouds have relatively higher occurrence frequency(>1.2%),where few CPR-missed clouds occur(<0.4%).In addition,both CPR-missed and CPR-captured clouds rarely occur over high-latitude oceans above 50°N and 50°S.At the same latitude, compared to CPR-captured clouds,many more CPR-missed clouds are located closer to the continents. Such a distribution pattern shows that CPR tends to miss warm clouds over waters with relatively lower SST.Considering the blind zone of CPR at approximately 1 km above the sea surface,there would be plenty of warm clouds restricted under the atmospheric boundary layer over cold waters,which are unable to break through the boundary layer and reach the effective detection height of CPR.This is the main reason for the high levelof missed detection of oceanic warm clouds.

    In fact,the abovementioned analysis reveals the global distribution of the absolute quantity of CPR-missed clouds.Since the CPR-captured clouds are dominant in most areas and distributions of CPR-captured and CPR-missed clouds overlap in their high incidence areas,defining a miss rate will be beneficial in terms of clarifying the characteristics of CPR missed detections of global oceanic warm clouds.The miss rate in this study is defined as the proportion ofCPR-missed cloud pixels among totalwarm cloud pixels in each region.To some extent,the value of the miss rate represents the proportion of warm clouds restricted under the atmospheric boundary layer,which directly reflects the deviations in CPR detections of warm clouds in various regions.The statistical results show that the average miss rate of CPR for global oceanic warm clouds is approximately 0.43 in the whole of 2008,meaning nearly half of these clouds were missed by CPR over the ocean.Thus,an underestimation of nearly 50%will result in when directly using CPR measurements to estimate the global oceanic warm cloud amount.Meanwhile,it also reflects that, over the global ocean,those warm clouds whose locations are restricted to under the atmospheric boundary layer account for an evidently large proportion of the total warm cloud amount,which should not be neglected in statistical analyses of warm cloud amounts and their microphysical properties.

    Fig.2.Occurrence frequency of(a)CPR-missed and(b)CPR-captured oceanic warm clouds in 2008.

    The distribution of the miss rate over the global ocean is shown in Fig.3,but only the grids with more than 50 warm cloud pixels are displayed,considering the representation of the miss rate calculation.It can be seen that,relative to the lower value of the miss rate within 0.1-0.2 in the central oceans,the miss rate of inshore areas is generally higher,with many reaching more than 0.7.Among these regions,high values along the west coasts of the American and African continents are probably caused by the suppression of convective activities due to the locally lower SST.Low clouds arise frequently and most are unable to penetrate the top of the atmospheric boundary layer.On the contrary,although there are also high miss rates along other coasts,such as the northern Indian Ocean near the Arabian Peninsula and the east coasts of the East Asian continent and North American continent, the absolute quantity of warm clouds is quite small. The indication is that the dynamical conditions inthese regions are not appropriate for warm-cloud formation.Once formed as warm clouds under suitable conditions,they are mostly boundary layer clouds. This is probably due to the large-scale condensation processes generated in these regions by the interaction of distinct sea and land air masses.

    The western Pacific warm pool is the region with the lowest miss rate in the low and middle latitudes. Although warm clouds are not frequent there,they can mostly break through the boundary layer over 1 km, and are identified as CPR-captured clouds.The miss rate near 40°N and 40°S is consistently high,which indicates boundary-layer clouds account for a large portion in these regions.Since the freezing level is lower in high latitudes,there is relatively narrower space for warm-cloud formation.In addition,warm cloud amounts decrease obviously compared with those in the low latitude oceans.All these factors lead to the consistently high value of the miss rate at high latitudes.Figure 4 shows the zonal distribution of the miss rate with warm cloud amounts and zonal average SST,in which data in the high-latitude and polar regions above 60°have been neglected due to corresponding small sample volumes.As shown in Fig.4a, in the high incidence regions of warm clouds over the tropical and subtropicalocean,the cloud-amount contribution from the boundary layer clouds is stable at 40%,slightly higher in the Northern Hemisphere than the Southern Hemisphere,which leads to the miss rate at 0.4 in these regions.This basically determines the global average miss rate.On the whole,the distribution of the miss rate is negatively correlated with the average SST on large scales(Fig.4b).The miss rate remains steady at approximately 0.4 in the tropics.With increasing latitude,warm cloud amounts uniformly decrease,while the miss rate uniformly increases and can reach more than 0.7 up to north and south of 50°of latitude.

    3.2 Altitude characteristics of CPR-missed and CPR-captured clouds

    The CTH reflects the vertical instability and intensity of the vertical movement,and the corresponding cloud temperature directly determines the external longwave radiation.Thus,it is also an important factor affecting cloud radiative forcing.The active detection method of CPR can obtain the CTH and,to some extent,resolve the cloud vertical structure from top to base.Nevertheless,with regard to CPR-missed clouds,CTH was calculated by the above method(in Section 2)with MODIS CTT.With the calculated CTH,the vertical distribution characteristics of CPR-missed clouds are available,which is beneficial for understanding the reason that leads to the missed detection by CPR of these warm clouds over the ocean. Meanwhile,relevant statisticalresults are necessary to complement the investigations of cloud vertical structure characteristics with the CPR datasets.

    Fig.3.Global distribution of the miss rate of oceanic warm clouds(only grids with 50 or more valid pixels are shown).

    Fig.4.Zonal mean distributions of the miss rate(dashed line)with(a)warm cloud amounts and(b)sea surface temperature(solid line)across the global ocean.

    Fig.5.Global distributions of CTHs of(a)CPR-missed and(b)CPR-captured oceanic warm clouds.

    As shown in Fig.5,few CPR-missed clouds have a CTH higher than 1.2 km,while CPR-captured cloudsare mostly higher than 1.5 km.Thus,the difference in the CTH between these two types of cloud samples is obvious,suggesting that there are great deviations in the vertical distribution of occurrence frequency of warm clouds,due to many missed detections by CPR. Most of the CPR-missed clouds are located under the atmospheric boundary layer and have CTH concentrated within 0.6-1.2 km.The distribution pattern revealed in Fig.5a approximately reflects the CTH distribution of the warm clouds below the boundary layer over the global ocean.Based on the aforementioned analysis,in high incidence regions within the Pacific and Atlantic oceans,CPR-missed clouds have relatively higher CTH,which are more likely to break through the boundary layer.On the other side,along the east coasts with a higher miss rate,CPR-missed clouds are restricted to below 0.6 km,certainly limited under the atmospheric boundary layer.Hence, the value of the CPR miss rate is directly associated with the CTH of warm clouds.

    The inshore region ofthe western American continent is the most active area for forming boundary-layer clouds throughout the global ocean.Since boundary layer clouds dominate warm clouds in this region,CPR makes more missed detections and yields a higher miss rate.The CTH of CPR-missed clouds tends to decrease progressively from central oceans to western coasts.By contrast,the CTH distribution of CPR-captured clouds is more uniform(Fig.5b),and CTH slowly decreases from more than 2 km to near 1.5 km from west to east across the ocean,in accordance with the variation of the cloud types with changing SST. As revealed in previous studies,from west to east,the dominant three types of low cloud are trade cumulus,stratocumulus,and cumulus,respectively(Norris, 1998).It is worth noting that,although the warm clouds here are required to have the first effective echo from CPR under 4.5 km and a CTT greater than 273 K,warm clouds are intensively located in lower layers. Only a few of them can reach more than 2.5 km,forming a specific mode of the vertical distribution of the warm clouds(Huang et al.,2012;Gao et al.,2014).In particular,the boundary-layer clouds that CPR fails to detect constitute a major part of low clouds.These clouds feature totally different latent heating and radiative forcing compared to CPR-captured clouds.

    As analyzed above,in the CTH statistics ofglobal warm clouds using the CPR data only,obvious overestimation would definitely be caused because of the severe level of missed detections of boundary-layer clouds by CPR.In order to clarify the impacts from the exclusion of CPR-missed clouds,the difference in the CTH between the CPR-captured and totalamount of warm clouds was calculated,to demonstrate the extent of the overestimation;the results are presented in Fig.6,in which only those grids with more than 50 pixels of both CPR-missed and CPR-captured clouds are displayed.The CPR-captured clouds tend to consistently overestimate the CTH of total warm clouds over the globalocean,and the biases are diverse across different regions.In the central Pacific,Atlantic,and Indian oceans,where CPR-captured clouds play a dominant role,the difference in the CTH between the CPR-captured clouds and total warm clouds is generally smaller than 0.4 km,with an overestimation of about 20%.Nearer to the continents,CPR-missed clouds become gradually more dominant and the CPR overestimation becomes even more severe,with a difference even larger than 0.8 km.Since boundary-layer clouds with very low CTH are dominant,the overestimation of the CTH can reach more than 50%.

    Since the lowest height of effective detection by CPR is not constant over the global ocean,utilizing the CTH of CPR-missed clouds calculated from MODIS CTT helps to further clarify the miss rate of CPR at different heights,as shown in Table 1(pixels with CTT greater than SST were removed in the calculation).It can be seen that nearly 70%of the CPR-missed clouds are located under the atmospheric boundary layer,with a top at around 1 km,while more than 80%of the CPR-captured clouds are present within 1.0-2.5 km.As for the warm clouds located below 1 km,the miss rate is up to 0.991,implying that CPR misses nearly all the warm clouds there. However,for the warm clouds located within 1.0-1.5 km,the miss rate decreases to 0.443,and it decreases rapidly with increasing CTH.Since the ground clutter can barely reach 1.5 km,the missed warm clouds withCTH higher than 1.5 km probably result from the absence of an effective echo,as is also the case for the missed thin cirrus clouds by CPR.On the whole,the warm clouds under 1 km occupy a large proportion of global oceanic warm clouds,which are almost entirely missed by CPR,therefore leading to the average missing rate of approximately 50%on the global scale. Whereas,for warm clouds higher than 2 km,the detection results from CPR show no evident bias,with a miss rate as low as 0.024.The statistics of cloud parameters for these clouds are highly reliable.

    According to the above results,the distribution of warm clouds is far from uniform over the ocean,with several remarkable regions that have very high occurrences.Although most of them are located over the tropical and subtropical ocean,their background atmospheric and oceanic circulation states are different. In order to avoid distortion of the statistical results, which could result from excessive smoothing ofthe statisticalresults from large scales,five sample areas were selected for further analysis.The locations and sample volumes for these areas are shown in Fig.7.These five chosen areas are extremely active in terms of warmcloud development,contributing more than 95%of the warm cloud amounts over the global ocean.Moreover, both CPR-missed and CPR-captured clouds account for high proportions,with a miss rate ranging between 0.28 and 0.55.

    Fig.6.Global distribution of CTH differences between CPR-captured and total warm clouds.

    Table 1.CPR miss rate of oceanic warm clouds at different heights

    Fig.7.The five selected oceanic sample areas that have sufficient warm-cloud samples.Area A:the northeastern Pacific,Area B:the southeastern Pacific,Area C:the central Indian Ocean,Area D:the north of the Atlantic,and Area E:the southern Atlantic Ocean.

    Fig.8.Frequency distribution of CTH of the(a)CPR-missed,(b)CPR-captured,(c)total warm clouds,and(d)a special comparison in Area A.

    In the five sample areas,the CTH features of the CPR-missed and CPR-captured clouds are analyzed at the pixel rather than grid level.The frequency distribution of CTH in the five sample areas is shown in Fig.8.As shown in Fig.8a,the CTH of CPR-missed clouds is primarily located under 1.6 km,with peaks ranging from 0.5 to 1.3 km in various regions.In the southeastern Pacific(Area B)and the central Indian Ocean in the Southern Hemisphere(Area C),CPR-missed clouds have consistently higher CTH,with a peak above 1 km,and few located below 0.5 km.In contrast,the CTH is consistently lower in the north of the Atlantic(Area D)and in the northeastern Pacific(Area A)in the Northern Hemisphere.Sincethe impacts from seasonal variations were removed in the calculation of the global average,the above deviations reflect the fact that between the Northern and Southern Hemispheres there are essentialdifferences in the development of low clouds within the atmospheric boundary layer(Huang et al.,2012).Figure 8b shows that CPR-captured clouds are truncated at approximately 0.8 km,consistent with the statistical results of the CPR blind-zone thickness(Fig.1).The effective detection echo height of CPR is approximately 0.8 km,reaching at least the fourth bin above the sea surface.As for CPR-captured clouds,which account for more than 50%in the total samples,the CTHs are primarily located within 1.1-2.5 km,with peaks at 1.5-2.0 km,which are approximately 1 km higher than those of CPR-missed clouds.The CPR-captured and CPR-missed clouds possess similar peak frequencies and integral frequencies,and their proportions in total samples are both close to 50%.The above results indicate that most warm clouds are located at 2.5 km above the sea surface,with rather few appearing at higher levels.Considering most of the five sample areas are primarily located in the tropical and subtropical ocean,it is also suggested that extremely few warm clouds can reach up to the freezing level(about 4 km).The CTH frequency distributions of the total warm clouds are presented in Fig.8c.Although the frequency profile retains a unimodal distribution after the addition of the CPR-missed clouds,the distribution pattern changes greatly.In terms of regional variation,the highest peak of about 1.8 km occurs in the central Indian Ocean,higher than the other four sample areas.As mentioned above,there is also a high occurrence frequency of warm clouds in the central Indian Ocean;the universally higher CTH and correspondingly lower CTT would jointly impose unique radiative effects in this particular region.

    CPR-missed clouds have significant impacts on accurately analyzing the vertical distribution of warm clouds.For instance,in Area A(Fig.8d),total warm clouds,which represent the real situation of oceanic warm clouds,have a distribution with lower CTH and smaller peak frequency compared to CPR-captured clouds.The peak height falls below 1.5 km.Consequently,the warm clouds under 1.5 km account for nearly half of all samples,which is obviously higher than the statistical results from CPR-captured clouds alone.Therefore,although CPR has unique advantages in detecting multi-layer clouds and cloud vertical distributions,the restriction imposed by its blind zone near the surface leads to obvious statistical errors in warm cloud amounts and CTH.CPR can only detect clouds to break through the atmospheric boundary layer,which primarily consist of cumulus clouds. Meanwhile,CPR regularly misses those clouds that are trapped within the atmospheric boundary layer, which are mostly stratus and stratocumulus clouds. Hence,it is necessary to combine with other detection methods,to acquire more accurate measurements of global oceanic warm clouds.Similarly,there will be definite deviations in descriptions of the microphysical properties of warm clouds when using CPR alone to measure oceanic warm clouds.

    3.3 Microphysical properties of CPR-missed clouds

    Given that the CPR-missed clouds are mostly located within the atmospheric boundary layer,the microphysical properties of these special clouds cannot be obtained from CPR.Meanwhile,since MODIS is unable to specifically exclude multilayer cloud situations,using MODIS cloud products alone also cannot accurately acquire the properties of CPR-missed clouds.Nevertheless,the combination of these two datasets provides an available opportunity to further investigate CPR-missed clouds.The CPR profiles exclude the potential upper-level clouds,and MODIS retrievals for single-layer cloud provide reliable microphysical properties in the CPR blind zone.

    Figure 9a shows the frequency distribution of cloud droplet radius(CDR)of CPR-missed clouds in the five sample areas.The CDR population peaks within 7-9μm and is primarily concentrated within 7-12μm,which accounts for 47.8%-56.7%.This dominance of low CDR values suggests the ma jority of CPR-missed clouds might be non-precipitating clouds.Approximately 19.7%-26.5%of CPR-missed clouds have a CDR achieving the common size of precipitating particles(about 15μm).The frequency distribution of cloud optical depth(COD)of CPR-missed clouds in the five sample areas is shown in Fig.9b.It is evident that the COD of CPR-missed clouds is primarily concentrated within 1-6 and accounts for 53.0%-67.0%.In addition,the CPR-missed clouds with a COD smaller than 1 account for approximately 5.8%-10.6%,which may contain some thin cirrus clouds that are missed by CPR and misjudged as warm clouds by MODIS.In the central Indian Ocean(Area C),CPR-missed clouds have larger CDR and smaller COD,and correspond to higher CTH,shown in Fig.8,while the opposite is true in the southern Atlantic Ocean(Area E).This indicates that CPR-missed clouds in Area C have more high/thin warm clouds,while in Area E there are more low/thick warm clouds.Dong et al. (2014)and Xi et al.(2014)investigated the microphysical properties of oceanic boundary-layer clouds in the Azores(Area D)with MODIS products and Atmospheric Radiation Measurement Program measurements.Their COD mean value was 13.1,much greater than the COD of CPR-missed clouds(4.8)derived in this study,which is probably due to the different samples employed.

    It should be pointed out that there are several reasons for CPR missing oceanic warm clouds.For those located below 1 km,ground clutter effects play a dominant role and lead to the average miss rate of CPR exceeding 0.9(Table 1).For warm clouds located within 1.0-2.0 km,the average miss rate is 0.281. Since ground clutter has little impact in this layer, the missed detections by CPR might result from the smaller COD and CDR.The two-dimensional distributions of the COD and CDR of CPR-missed(Fig. 10a)and CPR-captured(Fig.10b)clouds in the five sample areas show that,for CPR-missed clouds,the groups of COD and CDR are primarily concentrated in relatively small values(1<COD<6 and 7<CDR<12μm),and the cloud amounts decrease with increasing COD and CDR.On the contrary,the groups of CPR-captured clouds have more uniform distributions.Their COD is primarily located within 3-9 and reaches up to more than 15.The CDR of these clouds is primarily located within 14-20μm,with some reaching the upper limit of the MODIS COD retrieval. Thus,compared to CPR-missed clouds,CPR-captured clouds have both larger COD and CDR.In addition, most of them are optically thicker oceanic warm clouds with larger CDR and are more likely to be associated with precipitation.It is also indicated that,for clouds with the same COD,the smaller the CDR is(CDR<12μm),the more likely the CPR fails to detect clouds.

    Fig.9.Frequency distributions of(a)CDR and(b)COD of the CPR-missed clouds in the five sample areas.

    Fig.10.Two-dimensional distributions of the sample amounts of the COD/CDR for(a)CPR-missed and(b)CPR-captured clouds in five sample areas,and the miss rate of(c)total warm clouds and(d)warm clouds with CTH higher than 1 km.

    Figure 10c shows distributions of the miss rate in different COD/CDR combinations in the five sample areas.When CDR is smaller than 10μm,CPR shows a severe levelofmissed detection,with a continuously high miss rate of larger than 0.8.With increasing CDR,the miss rate decreases continuously.Since CPR misses almost all warm clouds located below 1 km,due to ground clutter,it is not clear whether these missed detections are in part caused by the microphysical properties of those clouds.Therefore,in order to exclude the impacts from these CPR-missed pixels,we recalculated the distribution of the miss rate after removing the CPR-missed clouds located below 1 km, as shown in Fig.10d.According to the results,when CDR is smaller than 10μm,the impacts from COD are less obvious and the miss rate remains larger than 0.8 in each CODbin.When CDRis larger than 10μm,the impacts from COD gradually emerge,with the miss rate decreasing consistently with COD.In addition, for COD larger than 12,the miss rate remains below0.3 in each CDR bin.Since the COD of oceanic warm clouds is generally small,the CDR might be one of the key factors at play in the missed detections of CPR, especially for those warm clouds with tops higher than 1 km.As for the warm clouds with large COD,those with smaller CDR(<10μm)have millimeter-wave reflectivities that are possibly too weak to be detected by CPR(Liu et al.,2010b).In addition,the statistical results of cloud water path(CWP)of CPR-missed and CPR-captured clouds in the five sample areas(Table 2)indicate that the CWP from CPR-missed clouds is 75 g m?2smaller than that from CPR-captured clouds.This means that the majority of CPR-missed clouds are non-precipitating clouds with quite low water content,while CPR-captured clouds are more likely to generate precipitation.

    The statistical results in the five areas indicate that the CDR and COD of CPR-missed clouds are primarily concentrated within 6-12 and 1-6μm,respectively.The corresponding CWP is concentrated at approximately 40 g m?2.Thus,the suggestion is that the CPR-missed clouds are generally optically thin and weak non-precipitating clouds with very low CWP.In short,besides the interference ofground clutter,low COD and CDR also contribute to the failed detection of many oceanic warm clouds by CPR.

    Table 2.CWP of CPR-missed and CPR-captured clouds in the five areas(A,B,C,D,and E)

    4.Conclusions

    As an important member of the A-Train constellation of satellites,CPR aboard CloudSat is the first space-borne millimeter-wavelength radar.Relative to the passive remote sensing of the International Satellite Cloud Climatology Project for recording global cloud amounts,the cloud detection of CPR in principle represents great progress,playing an indispensable role in investigations of cloud vertical structure globally,and corresponding atmospheric heating.However,CPR is of course not a perfect solution for detecting clouds and precipitation.Furthermore,some technical constraints also lead to some limitations in the application of the CPR datasets.The missed detection of high,thin cirrus clouds due to the sensitivity of CPR has been widely acknowledged,but the impacts on low-cloud detection from ground clutter effects are still unclear.In this study,we have investigated the underestimation of global oceanic warm clouds by CPR.The cloud amounts,CTHs and microphysical properties of CPR-missed clouds have been examined by using the synchronous data from MODIS. The main conclusions can be summarized as follows:

    (1)CPR-missed warm clouds mainly arise over the relatively cold waters of the eastern ocean.The warm-cloud miss rate of CPR is negatively correlated with the zonal average SST.The miss rate in the centralocean is approximately 0.1-0.3 and increases gradually with closer proximity to the continents.The miss rate ofinshore areas exceeds 0.7,while the globalmean CPR miss rate is 0.43.

    (2)For warm clouds at various altitudes,the quantities of CPR missed detections are quite different.Almost all warm clouds located below 1 km are missed by CPR,while the miss rate gradually decreases with increasing CTH.The detection results of CPR for warm clouds higher than 2 km are basically credible,with a negative bias of around 3%.

    (3)The CTH of the CPR-missed clouds is primarily located within 0.6-1.2 km and nearly 70%of the CPR-missed clouds are located under 1 km.Conversely,more than 80%of the CPR-captured clouds are located within 1.0-2.5 km.By using the datasets of MODIS and CPR jointly,almost all oceanic warm clouds occur below 2.5 km.In the upper atmospherethere are barely any warm clouds.

    (4)The CDR and COD of the CPR-missed clouds are generally smaller.The corresponding CWP is lower than 50 g m?2.Most of the CPR-captured clouds are thicker and have CWP larger than 100 g m?2,more likely to form precipitation.It is also found that,in the lower atmosphere,besides ground clutter effects,CPR tends to miss clouds with rather small COD and CDR.

    On the whole,over the oceans,boundary-layer clouds located below 1 km should not be neglected due to their considerable contribution to the total oceanic warm cloud amount.Hence,for oceanic warm clouds,there will be severe statistical deviations in terms of cloud amounts and parameters,when relying on traditional methods that directly remove the CPR profiles within the ground clutter zone.The investigation in this study provides a reference to improve the accuracy in our understanding of quantitative results derived from CPR and,further,the advantage of combining multiple sources of satellite data has been demonstrated.Clearly,combining the information from various active and passive satellite instruments is a crucial approach to achieving advanced applications of satellite remote sensing datasets in the future.

    Acknowledgments.The authors are grateful for constructive suggestions from the two anonymous reviewers.The authors would like to acknowledge the NASA CloudSat project for sharing the CPR and MODIS product data.

    REFERENCES

    Ackerman,S.A.,R.E.Holz,R.Frey,et al.,2008:Cloud detection with MODIS.Part II:Validation.J.Atmos.Oceanic Technol.,25,1073-1086.

    Barker,H.W.,G.L.Stephens,and Q.Fu,1999:The sensitivity of domain-averaged solar fluxes to assumptions about cloud geometry.Quart.J.Roy. Meteor.Soc.,125,2127-2152.

    Bony,S.,and J.-L.Dufresne,2005:Oceanic boundary layer clouds at the heart of tropical cloud feedback uncertainties in climate models.Geophys.Res. Lett.,32,L20806,doi:10.1029/2005GL023851.

    Chan,M.A.,and J.C.Comiso,2011:Cloud features detected by MODIS but not by CloudSat and CALIOP.Geophys.Res.Lett.,38,L24813,doi: 10.1029/2011gl050063.

    Charlson,R.J.,J.E.Lovelock,M.O.Andreae,et al., 1987:Oceanic phytoplankton,atmospheric sulphur, cloud albedo and climate.Nature,326,655-661.

    Dong Xiquan,Xi Baike,A.Kennedy,et al.,2014:A 19-month record of marine aerosol-cloud-radiation properties derived from DOE ARM mobile facility deployment at the Azores.Part I:Cloud fraction and single-layered MBL cloud properties.J.Climate,27,3665-3682.

    Gao Wenhua,Sui Chung-hsiung,and Hu Zhijin,2014:A study of macrophysical and microphysical properties of warm clouds over the Northern Hemisphere using CloudSat/CALIPSO data.J.Geophys.Res.,119, 3268-3280.

    Han Ding,Yan Wei,Ye Jing,et al.,2013:Analyzing cloud,precipitation,and thermal structure characteristics of typhoons in eastern Pacific based on CloudSat satellite data.Chinese J.Atmos.Sci.,37,691-704.(in Chinese)

    Hartmann,D.L.,and D.A.Short,1980:On the use of earth radiation budget statistics for studies of clouds and climate.J.Atmos.Sci.,37,1233-1250.

    Holz,R.E.,S.A.Ackerman,F.W.Nagle,et al., 2008:Global Moderate Resolution Imaging Spectroradiometer(MODIS)cloud detection and height evaluation using CALIOP.J.Geophys.Res.,113, D00A19,doi:10.1029/2008JD009837.

    Huang Yi,S.T.Siems,M.J.Manton,et al.,2012:The structure of low-altitude clouds over the southern Ocean as seen by CloudSat.J.Climate,25,2535-2546.

    Jensen,M.P.,A.M.Vogelmann,W.D.Collins,et al., 2008:Investigation of regional and seasonal variations in marine boundary layer cloud properties from MODIS observations.J.Climate,21,4955-4973.

    Kubar,T.L.,D.E.Waliser,and J.-L.Li,2011:Boundary layer and cloud structure controls on tropical low cloud cover using A-train satellite data and ECMWF analyses.J.Climate,24,194-215.

    Liu,C.,and E.J.Zipser,2009:“Warm rain”in the tropics:Seasonal and regional distribution based on 9 years of TRMM data.J.Climate,22,doi: 10.1175/2008JCLI2641.1,767-779.

    Liu Qi,Fu Yunfei,and Feng Sha,2010a:Geographical patterns of the cloud amount derived from the ISCCP and their correlation with the NCEP reanalysis datasets.Acta Meteor.Sinica,68,689-704.(in Chinese)

    Liu Shuang,G.Heygster,and Zhang Suping,2010:Comparison of CloudSat cloud liquid water paths in Arctic summer using ground-based microwave radiometer.J.Ocean.Univ.China,9,333-342.

    Luo Yali,Zhang Renhe,and Wang Hui,2009:Comparing occurrences and vertical structures of hydrometeors between eastern China and the Indian monsoon region using CloudSat/CALIPSO data.J.Climate,22,1052-1064.

    Mace,G.G.,R.Marchand,and Q.Zhang,et al., 2007:Global hydrometeor occurrence as observed by Cloud Sat:Initial observations from summer 2006.Geophys.Res.Lett.,34,L09808,doi: 10.1029/2006GL029017.

    Marchand,R.,G.G.Mace,T.Ackerman,et al.,2008: Hydrometeor detection using Cloudsat—An earthorbiting 94-GHz cloud radar.J.Atmos.Oceanic Technol.,25,519-533.

    Minnis,P.,S.Sun-Mack,D.F.Young,et al.,2011: CERES Edition-2 cloud property retrievals using TRMM VIRS and Terra and Aqua MODIS data. Part I:Algorithms.IEEE Trans.Geosci.Remote. Sens.,49,4374-4400.

    Norris,J.R.,1998:Low cloud type over the ocean from surface observations.Part II:Geographical and seasonal variations.J.Climate,11,383-403.

    Peng Jie,Zhang Hua,and Shen Xinyong,2013:Analysis of vertical structure of clouds in East Asia with CloudSat data.Chinese J.Atmos.Sci.,27,91-100. (in Chinese)

    Platnick,S.,M.D.King,S.A.Ackerman,et al.,2003: The MODIS cloud products:Algorithms and examples from Terra.IEEE Trans.Geosci.Remote Sens.,41,459-473.

    Schumacher,C.,and R.A.Houze Jr.,2003:The TRMM precipitation radar’s view of shallow,isolated rain. J.Appl.Meteor.,42,1519-1524.

    Sun-Mack,S.,P.Minnis,Y.Chen,et al.,2008:Boundary layer lapse rate in cloudy areas derived using CALIPSO data.Presentation material,CALIPSO Science Team Meeting,Paris,France.March,2008.

    Sun-Mack,S.,P.Minnis,Y Chen,et al.,2014:Regional apparent boundary layer lapse rates determined from CALIPSO and MODIS data for cloud-height determination.J.Appl.Meteor.Climatol.,53, 990-1011.

    Tanelli,S.,S.L.Durden,E.Im,et al.,2008:CloudSat’s cloud profiling radar after two years in orbit:Performance,calibration,and processing.IEEE Trans. Geosci.Remote Sens.,46,3560-3573.

    Wang Hui,Luo Yali,and Zhang Renhe,2011:Analyzing seasonal variation of clouds over the Asian monsoon regions and the Tibetan Plateau region using Cloud-Sat/CALIPSO data.Chinese J.Atmos.Sci.,35, 1117-1131.(in Chinese)

    Wang Shuaihui,Han Zhigang,Yao Zhigang,et al.,2011: An analysis of cloud types and macroscopic characteristics over China and its neighborhood based on the CloudSat data.Acta Meteor.Sinica,69, 883-899.(in Chinese)

    Weisz,E.,Li Jun,W.P.Menzel,et al.,2007:Comparison of AIRS,MODIS,CloudSat,and CALIPSO cloud top height retrievals.Geophys.Res.Lett.,34,L17811,doi:10.1029/2007GL030676.

    Xi Baike,Dong Xiquan,P.Minnis,et al.,2014:Comparison of marine boundary layer cloud properties from CERES-MODIS Edition 4 and DOE ARM AMF measurements at the Azores.J.Geophys.Res.,119,9509-9529.

    Yi Mingjian,Fu Yunfei,Liu Peng,et al.,2014:Deep convective clouds over the northern Pacific and their relationship with oceanic cyclones.Adv.Atmos. Sci.,32,821-830,doi:10.1007/s00376-014-4056-9.

    Yin Jinfang,Wang Donghai,Zhai Guoqing,et al.,2013: Observational characteristics of cloud vertical profiles over the continent of East Asia from the Cloud-Sat data.Acta Meteor.Sinica,27,26-39,doi: 10.1007/s13351-013-0104-0.

    Zhang,G.J.,A.M.Vogelmann,M.P.Jensen,et al., 2010:Relating satellite-observed cloud properties from MODIS to meteorological conditions for marine boundary layer clouds.J.Climate,23,1374-1391.

    Zhang,M.H.,W.Y.Lin,S.A.Klein,et al.,2005:Comparing clouds and their seasonal variations in 10 atmospheric general circulation models with satellite measurements.J.Geophys.Res.,110,D15S02, doi:10.1029/2004JD005021.

    Zuidema,P.,D.Painemal,S.de Szoeke,et al.,2009:Stratocumulus cloud-top height estimates and their climatic implications.J.Climate,22,4652-4666.

    Liu Dongyang,Liu Qi,and Zhou Lingli,2015:Underestimation of oceanic warm cloud occurrences by the Cloud Profiling Radar aboard CloudSat.J.Meteor.Res.,29(4),576-593,

    10.1007/s13 351-015-5027-5.

    Supported by the National Natural Science Foundation of China(41175032).

    ?qliu7@ustc.edu.cn.

    ?The Chinese Meteorological Society and Springer-Verlag Berlin Heidelberg 2015

    February 06,2015;in final form June 16,2015)

    猜你喜歡
    劉奇東陽
    古韻東陽
    人大代表約見制度的東陽實(shí)踐
    The differences of the regulations of equity—based crowdfunding in China and USA
    商情(2017年14期)2017-06-09 21:14:55
    莆田東陽:兩朝“進(jìn)士村”的前世今生
    東陽:大樹底下長出草
    科學(xué)小衛(wèi)士系列(六)
    科學(xué)小衛(wèi)士(三)
    東陽10家電鍍企業(yè)被整合入園
    科學(xué)小衛(wèi)士系列(二)幸福的流浪貓
    魔法故事系列(十二)
    最近中文字幕2019免费版| 少妇人妻 视频| 国产男人的电影天堂91| 久久久久九九精品影院| 午夜精品国产一区二区电影 | 国产色爽女视频免费观看| 尤物成人国产欧美一区二区三区| 亚洲在久久综合| 只有这里有精品99| a级毛片免费高清观看在线播放| 欧美日韩国产mv在线观看视频 | av在线亚洲专区| 国产永久视频网站| 国产高清国产精品国产三级 | 欧美变态另类bdsm刘玥| 欧美精品一区二区大全| 国产av不卡久久| 午夜亚洲福利在线播放| 国产一级毛片在线| 亚洲色图av天堂| 精华霜和精华液先用哪个| 国产成人精品婷婷| 好男人视频免费观看在线| 国产一级毛片在线| 黄色配什么色好看| 欧美日韩精品成人综合77777| 免费大片18禁| 欧美日韩亚洲高清精品| www.av在线官网国产| 只有这里有精品99| 女人十人毛片免费观看3o分钟| 国产69精品久久久久777片| kizo精华| 国产 一区精品| 美女xxoo啪啪120秒动态图| 狂野欧美白嫩少妇大欣赏| 亚洲丝袜综合中文字幕| 蜜臀久久99精品久久宅男| 91久久精品国产一区二区三区| 国产亚洲精品久久久com| 国产美女午夜福利| 午夜精品国产一区二区电影 | 午夜福利高清视频| 综合色av麻豆| 女人被狂操c到高潮| 亚洲无线观看免费| 精品国产乱码久久久久久小说| 国产黄片视频在线免费观看| 丝瓜视频免费看黄片| 成人免费观看视频高清| 欧美bdsm另类| 免费观看性生交大片5| 91精品国产九色| 特级一级黄色大片| 日韩不卡一区二区三区视频在线| 一区二区三区乱码不卡18| 国产日韩欧美亚洲二区| 色网站视频免费| 国产精品秋霞免费鲁丝片| 欧美zozozo另类| 内地一区二区视频在线| 在线a可以看的网站| 亚洲av成人精品一二三区| 性插视频无遮挡在线免费观看| 久久久久久久国产电影| 日本免费在线观看一区| 国产男女内射视频| 美女高潮的动态| 内射极品少妇av片p| 少妇人妻一区二区三区视频| 毛片一级片免费看久久久久| 男女那种视频在线观看| 男的添女的下面高潮视频| 精品久久久久久久久av| 免费看av在线观看网站| 一个人看的www免费观看视频| 一区二区av电影网| 婷婷色综合www| 99热这里只有是精品50| 亚洲av中文av极速乱| 舔av片在线| 成年免费大片在线观看| 国产毛片a区久久久久| 美女主播在线视频| 国产免费视频播放在线视频| 天堂网av新在线| 天堂中文最新版在线下载 | 亚洲不卡免费看| 老司机影院毛片| 久久精品久久久久久久性| 99re6热这里在线精品视频| 黄片wwwwww| 免费观看无遮挡的男女| av在线老鸭窝| 亚洲精品久久久久久婷婷小说| 偷拍熟女少妇极品色| 狂野欧美白嫩少妇大欣赏| 免费看不卡的av| 街头女战士在线观看网站| 99九九线精品视频在线观看视频| 成年av动漫网址| 国产高清不卡午夜福利| 欧美国产精品一级二级三级 | 日韩亚洲欧美综合| 日日摸夜夜添夜夜添av毛片| 搡女人真爽免费视频火全软件| 97热精品久久久久久| 国产成人精品一,二区| 国产免费一级a男人的天堂| 精品久久国产蜜桃| 午夜激情福利司机影院| 一个人看的www免费观看视频| 色吧在线观看| 久久久久性生活片| h日本视频在线播放| 国产精品一及| 麻豆精品久久久久久蜜桃| 欧美+日韩+精品| 精品国产三级普通话版| 水蜜桃什么品种好| 国产成人精品福利久久| 国产成年人精品一区二区| 简卡轻食公司| 成人高潮视频无遮挡免费网站| 精品久久久久久电影网| 国产黄色视频一区二区在线观看| www.av在线官网国产| 高清日韩中文字幕在线| 国产黄色视频一区二区在线观看| 欧美+日韩+精品| 一区二区av电影网| 久久久久国产精品人妻一区二区| 亚洲成色77777| 在线观看三级黄色| 成人二区视频| 人人妻人人澡人人爽人人夜夜| 亚洲av免费在线观看| 国产精品熟女久久久久浪| 18+在线观看网站| 国产亚洲5aaaaa淫片| 久久精品久久精品一区二区三区| 丝袜喷水一区| 亚洲激情五月婷婷啪啪| 亚洲,一卡二卡三卡| 欧美成人午夜免费资源| 国产女主播在线喷水免费视频网站| 综合色av麻豆| 涩涩av久久男人的天堂| av播播在线观看一区| 亚洲欧美日韩卡通动漫| 联通29元200g的流量卡| 国内揄拍国产精品人妻在线| 国产精品久久久久久精品电影小说 | 一个人看的www免费观看视频| 亚洲精品色激情综合| 51国产日韩欧美| 国产一区二区三区综合在线观看 | 99久国产av精品国产电影| 欧美变态另类bdsm刘玥| 日日撸夜夜添| 三级国产精品片| 成人亚洲精品一区在线观看 | 天美传媒精品一区二区| tube8黄色片| 91午夜精品亚洲一区二区三区| 免费观看性生交大片5| 在线观看人妻少妇| 2021天堂中文幕一二区在线观| 天天躁日日操中文字幕| 欧美变态另类bdsm刘玥| 亚洲怡红院男人天堂| 禁无遮挡网站| 看免费成人av毛片| 久久久久久国产a免费观看| 亚洲精品456在线播放app| 国产老妇女一区| 亚洲欧洲日产国产| 亚洲天堂国产精品一区在线| 国产精品无大码| 日韩免费高清中文字幕av| 亚洲人成网站在线观看播放| 亚洲伊人久久精品综合| 久久精品国产亚洲网站| 国产成人精品久久久久久| 偷拍熟女少妇极品色| 中文字幕人妻熟人妻熟丝袜美| 久久精品人妻少妇| 国模一区二区三区四区视频| 内射极品少妇av片p| 一本色道久久久久久精品综合| 99热全是精品| 在线看a的网站| 综合色丁香网| 97在线人人人人妻| av国产免费在线观看| 禁无遮挡网站| 成人亚洲精品一区在线观看 | 69av精品久久久久久| 成人一区二区视频在线观看| 99热网站在线观看| 蜜桃亚洲精品一区二区三区| 日韩av在线免费看完整版不卡| 久久精品久久精品一区二区三区| 另类亚洲欧美激情| 色吧在线观看| 精品99又大又爽又粗少妇毛片| 我的老师免费观看完整版| 日本爱情动作片www.在线观看| 亚洲在线观看片| 街头女战士在线观看网站| 免费观看的影片在线观看| 中文资源天堂在线| 又粗又硬又长又爽又黄的视频| 蜜桃亚洲精品一区二区三区| 神马国产精品三级电影在线观看| 18禁裸乳无遮挡免费网站照片| 亚洲精华国产精华液的使用体验| 国产欧美另类精品又又久久亚洲欧美| 蜜桃久久精品国产亚洲av| 久久精品人妻少妇| 国产精品嫩草影院av在线观看| 高清视频免费观看一区二区| 精品久久国产蜜桃| av黄色大香蕉| 精华霜和精华液先用哪个| 亚洲国产色片| 伦理电影大哥的女人| 大码成人一级视频| 极品少妇高潮喷水抽搐| 看非洲黑人一级黄片| 国产精品久久久久久精品电影小说 | 亚洲av二区三区四区| 亚洲精品国产成人久久av| 尤物成人国产欧美一区二区三区| av在线天堂中文字幕| 最近最新中文字幕大全电影3| 亚洲精品,欧美精品| 午夜福利视频1000在线观看| 波野结衣二区三区在线| 青青草视频在线视频观看| av在线app专区| 亚洲精品,欧美精品| 99久国产av精品国产电影| 九草在线视频观看| 舔av片在线| 日本一本二区三区精品| 少妇人妻久久综合中文| 香蕉精品网在线| 国产乱人视频| 日韩欧美精品免费久久| 亚洲精华国产精华液的使用体验| 九色成人免费人妻av| 别揉我奶头 嗯啊视频| freevideosex欧美| 国产成人aa在线观看| 国产av码专区亚洲av| 在线观看国产h片| 中文在线观看免费www的网站| 亚洲自拍偷在线| 久久久久网色| 成人亚洲欧美一区二区av| av在线天堂中文字幕| 精品一区二区三卡| 最近最新中文字幕免费大全7| 免费看av在线观看网站| 午夜福利高清视频| 国产老妇伦熟女老妇高清| 三级经典国产精品| 18禁在线播放成人免费| 97超碰精品成人国产| 在线观看三级黄色| 国产一区有黄有色的免费视频| 黄片无遮挡物在线观看| 亚洲精品成人av观看孕妇| 成年女人看的毛片在线观看| 91精品伊人久久大香线蕉| 日日摸夜夜添夜夜添av毛片| 国产av不卡久久| 尤物成人国产欧美一区二区三区| 一区二区av电影网| 狠狠精品人妻久久久久久综合| 亚洲精品自拍成人| 亚洲欧美一区二区三区国产| 纵有疾风起免费观看全集完整版| 一级毛片我不卡| 亚洲国产日韩一区二区| 嫩草影院新地址| 午夜视频国产福利| 精品一区二区免费观看| 99久久人妻综合| 91aial.com中文字幕在线观看| 国产精品99久久99久久久不卡 | 最近中文字幕高清免费大全6| av专区在线播放| 精华霜和精华液先用哪个| 天天躁日日操中文字幕| 黄色配什么色好看| 久久6这里有精品| 在线播放无遮挡| 亚洲内射少妇av| 真实男女啪啪啪动态图| 高清日韩中文字幕在线| 亚洲天堂国产精品一区在线| 亚洲av.av天堂| 如何舔出高潮| 国产一区二区在线观看日韩| 一级av片app| 神马国产精品三级电影在线观看| 一级黄片播放器| 国产精品一区二区性色av| 一区二区三区乱码不卡18| 看十八女毛片水多多多| 少妇猛男粗大的猛烈进出视频 | 婷婷色综合www| 亚洲av二区三区四区| 国产高清有码在线观看视频| 日本熟妇午夜| 久久99热这里只频精品6学生| kizo精华| 99九九线精品视频在线观看视频| 日日摸夜夜添夜夜添av毛片| 一级二级三级毛片免费看| a级一级毛片免费在线观看| 免费不卡的大黄色大毛片视频在线观看| 99热这里只有精品一区| 又大又黄又爽视频免费| 亚洲精品视频女| 国产片特级美女逼逼视频| 大陆偷拍与自拍| av在线播放精品| 人人妻人人爽人人添夜夜欢视频 | 久久99蜜桃精品久久| 免费黄色在线免费观看| 日韩人妻高清精品专区| 国产黄色视频一区二区在线观看| 亚洲一区二区三区欧美精品 | 99热国产这里只有精品6| 成人二区视频| av国产免费在线观看| 干丝袜人妻中文字幕| 亚洲av国产av综合av卡| 久久精品国产亚洲网站| 久久99蜜桃精品久久| 国产精品国产三级国产专区5o| 日韩一本色道免费dvd| 中文乱码字字幕精品一区二区三区| 人体艺术视频欧美日本| 国产亚洲精品久久久com| 在线天堂最新版资源| 精品久久久精品久久久| 国产精品久久久久久久电影| 国产亚洲av嫩草精品影院| 成人亚洲欧美一区二区av| 大片免费播放器 马上看| 伦理电影大哥的女人| 日日摸夜夜添夜夜爱| 久久鲁丝午夜福利片| 少妇裸体淫交视频免费看高清| 97超碰精品成人国产| 91久久精品国产一区二区三区| 80岁老熟妇乱子伦牲交| 久久久精品欧美日韩精品| 赤兔流量卡办理| 国国产精品蜜臀av免费| kizo精华| 精品少妇久久久久久888优播| 色5月婷婷丁香| 国产综合精华液| 亚洲性久久影院| 青青草视频在线视频观看| 伦精品一区二区三区| 深爱激情五月婷婷| 内射极品少妇av片p| 久久久久久久精品精品| 久久久久国产网址| 日本爱情动作片www.在线观看| 日韩国内少妇激情av| av国产精品久久久久影院| 亚洲最大成人中文| 久久人人爽av亚洲精品天堂 | 3wmmmm亚洲av在线观看| 熟妇人妻不卡中文字幕| 日日撸夜夜添| 日韩视频在线欧美| 夫妻午夜视频| av在线亚洲专区| 午夜亚洲福利在线播放| 看十八女毛片水多多多| 婷婷色综合www| 国产免费福利视频在线观看| 最近2019中文字幕mv第一页| 一区二区三区精品91| 欧美3d第一页| 久久精品国产自在天天线| 国产91av在线免费观看| 最近最新中文字幕免费大全7| 久久久久久久亚洲中文字幕| 精品久久久久久久久av| 欧美日本视频| 国精品久久久久久国模美| 三级经典国产精品| 国产亚洲av嫩草精品影院| 麻豆久久精品国产亚洲av| 午夜爱爱视频在线播放| 国产精品福利在线免费观看| 久久久久久久国产电影| 最近最新中文字幕免费大全7| 国产成人a区在线观看| 国产黄a三级三级三级人| 成人午夜精彩视频在线观看| 久久久精品欧美日韩精品| 中国国产av一级| 内射极品少妇av片p| 久久6这里有精品| av福利片在线观看| 五月天丁香电影| 久久久亚洲精品成人影院| 99热全是精品| 3wmmmm亚洲av在线观看| 中文在线观看免费www的网站| 国产精品人妻久久久影院| 免费人成在线观看视频色| 丰满乱子伦码专区| 内射极品少妇av片p| 少妇猛男粗大的猛烈进出视频 | 久久久久国产精品人妻一区二区| 欧美日韩在线观看h| 99热这里只有精品一区| 国产欧美另类精品又又久久亚洲欧美| 午夜视频国产福利| 69人妻影院| 夫妻性生交免费视频一级片| 成年人午夜在线观看视频| 国产爱豆传媒在线观看| 美女高潮的动态| 少妇丰满av| 中文字幕人妻熟人妻熟丝袜美| 国产亚洲91精品色在线| 另类亚洲欧美激情| 欧美zozozo另类| 人体艺术视频欧美日本| 亚洲欧美一区二区三区国产| 亚洲精品日韩av片在线观看| 中文字幕人妻熟人妻熟丝袜美| 国产成人精品婷婷| 91狼人影院| 91久久精品国产一区二区成人| 国产黄片美女视频| 老司机影院成人| 亚洲一区二区三区欧美精品 | 国产成年人精品一区二区| 久久这里有精品视频免费| 免费大片18禁| 大陆偷拍与自拍| 在线免费十八禁| 蜜臀久久99精品久久宅男| 中文字幕制服av| 高清日韩中文字幕在线| 赤兔流量卡办理| 亚洲精品久久午夜乱码| 色网站视频免费| 国产精品久久久久久精品电影小说 | 亚洲成人中文字幕在线播放| videos熟女内射| 一级a做视频免费观看| 欧美+日韩+精品| 一区二区三区免费毛片| 精品人妻偷拍中文字幕| 一级av片app| 亚洲综合色惰| 另类亚洲欧美激情| 少妇猛男粗大的猛烈进出视频 | 欧美丝袜亚洲另类| 亚洲熟女精品中文字幕| videos熟女内射| 久久久久久久午夜电影| 欧美激情久久久久久爽电影| 少妇人妻一区二区三区视频| 亚洲成人一二三区av| 精品国产三级普通话版| 日韩电影二区| 最近中文字幕高清免费大全6| 精品国产露脸久久av麻豆| 最近2019中文字幕mv第一页| 26uuu在线亚洲综合色| 国产色爽女视频免费观看| 91久久精品国产一区二区三区| 九草在线视频观看| 五月天丁香电影| 国产午夜精品久久久久久一区二区三区| 在线亚洲精品国产二区图片欧美 | 人人妻人人看人人澡| 日日摸夜夜添夜夜爱| 日本黄大片高清| 国产视频内射| 国产精品一及| 欧美日本视频| 性色avwww在线观看| 亚洲精品视频女| 欧美高清性xxxxhd video| 五月玫瑰六月丁香| 麻豆成人av视频| 一本久久精品| 国产精品偷伦视频观看了| 亚洲精品一区蜜桃| 激情五月婷婷亚洲| 三级经典国产精品| 欧美xxxx性猛交bbbb| eeuss影院久久| 99热这里只有是精品50| 综合色丁香网| 亚洲欧美精品自产自拍| 免费看日本二区| 国产精品国产三级国产av玫瑰| 日韩在线高清观看一区二区三区| videossex国产| 男人舔奶头视频| 别揉我奶头 嗯啊视频| 国产亚洲午夜精品一区二区久久 | 久久久色成人| 国产精品成人在线| av在线老鸭窝| 色视频在线一区二区三区| 成年女人看的毛片在线观看| 又爽又黄无遮挡网站| 99久久中文字幕三级久久日本| kizo精华| 亚洲综合精品二区| 日本与韩国留学比较| 老女人水多毛片| 有码 亚洲区| 女人久久www免费人成看片| 国产人妻一区二区三区在| 久久精品久久久久久噜噜老黄| 可以在线观看毛片的网站| 日日摸夜夜添夜夜爱| av网站免费在线观看视频| 国产欧美日韩一区二区三区在线 | 国产一区二区三区综合在线观看 | 国产午夜精品一二区理论片| 男男h啪啪无遮挡| 亚洲av欧美aⅴ国产| 午夜精品国产一区二区电影 | 国产老妇伦熟女老妇高清| 欧美老熟妇乱子伦牲交| 在线天堂最新版资源| 在现免费观看毛片| 五月开心婷婷网| 美女cb高潮喷水在线观看| 身体一侧抽搐| 美女cb高潮喷水在线观看| 国内精品美女久久久久久| 三级国产精品片| 国产精品av视频在线免费观看| 男人添女人高潮全过程视频| 国产精品精品国产色婷婷| 亚洲欧美日韩东京热| 777米奇影视久久| 亚洲丝袜综合中文字幕| 久久综合国产亚洲精品| 亚洲经典国产精华液单| 成年人午夜在线观看视频| 亚洲精品自拍成人| 久久久久久久大尺度免费视频| 日日啪夜夜撸| 麻豆乱淫一区二区| 日韩伦理黄色片| 久久久久久久久久久丰满| 精品一区二区免费观看| 丝袜喷水一区| 亚洲国产欧美人成| 亚洲精品一二三| 久久人人爽av亚洲精品天堂 | 国产高清三级在线| 亚州av有码| 黄色欧美视频在线观看| 国产成人精品婷婷| 久热这里只有精品99| 欧美国产精品一级二级三级 | av国产精品久久久久影院| 黄色日韩在线| 男插女下体视频免费在线播放| 欧美日韩一区二区视频在线观看视频在线 | 欧美少妇被猛烈插入视频| 少妇的逼水好多| 久久人人爽人人爽人人片va| 久久精品熟女亚洲av麻豆精品| 麻豆国产97在线/欧美| 尾随美女入室| 国产黄片视频在线免费观看| 伦理电影大哥的女人| 亚洲国产最新在线播放| 美女脱内裤让男人舔精品视频| 毛片女人毛片| 免费观看av网站的网址| 看黄色毛片网站| 国产 一区精品| 亚洲精品乱码久久久v下载方式| 少妇人妻久久综合中文| 一级爰片在线观看| 一边亲一边摸免费视频| 国产午夜精品一二区理论片| 99九九线精品视频在线观看视频| 伊人久久精品亚洲午夜| 欧美成人一区二区免费高清观看| 精品国产一区二区三区久久久樱花 | 白带黄色成豆腐渣| 亚洲婷婷狠狠爱综合网| 在线看a的网站| 日韩一区二区三区影片| 哪个播放器可以免费观看大片| 丝瓜视频免费看黄片| 亚洲av免费在线观看| 国产在线男女| 青青草视频在线视频观看|