Vertical cloud structures of the boreal summer intraseasonal variability based on CloudSat observations and ERA-interim reanalysis
The boreal summer intraseasonal variability (BSISV), which is characterized by pronounced meridional propagation from the equatorial zone to the Indian Continent, exerts significant modulation of the active/break phases of the south Asian monsoon. This form of variability provides a primary source of subseasonal predictive skill of the Asian summer monsoon. Unfortunately, current general circulation models display large deficiencies in representing this variability. The new cloud observations made available by the CloudSat mission provide an unprecedented opportunity to advance our characterization of the BSISV. In this study, the vertical structures of cloud water content and cloud types associated with the BSISV over the Indian Ocean and subcontinent are analyzed based on CloudSat observations from 2006 to 2008. These cloud structures are also compared to their counterparts as derived from ERA-interim reanalysis. A marked vertical tilting structure in cloud water is illustrated during the northward propagation of the BSISV based on both datasets. Increased cloud liquid water content (LWC) tends to appear to the north of the rainfall maximum, while ice water content (IWC) in the upper troposphere slightly lags the convection. This northward shift of increased LWC, which is in accord with local enhanced moisture as previously documented, may play an important role in the northward propagation of the BSISV. The transition in cloud structures associated with BSISV convection is further demonstrated based on CloudSat, with shallow cumuli at the leading edge, followed by the deep convective clouds, and then upper anvil clouds. Some differences in cloud water structures between CloudSat and ERA-interim are also noted, particularly in the amplitudes of IWC and LWC fields.
KeywordsIntraseasonal variability Cloud water Northward propagation CloudSat
It has been widely acknowledged that the intraseasonal variability (ISV) plays a significant role for tropical climate (see recent reviews by Lau and Waliser 2005; Zhang 2005; Wang 2006). While the eastward propagating Madden–Julian Oscillation (MJO; Madden and Julian 1971, 1994) is found to be a dominant ISV form in boreal winter, the boreal summer ISV (BSISV), with a period of 30–50 days, is characterized by a pronounced northward propagation over the Asian monsoon region (e.g., Yasunari 1979; Hsu and Weng 2001; Lawrence and Webster 2002; Hsu et al. 2004; Jiang et al. 2004; Jiang and Li 2005; Goswami 2005; Wang et al. 2005; Waliser 2006; and many others). The meridional propagation of the BSISV is found to be intimately associated with active and break phases of the Asian summer monsoon (e.g., Sikka and Gadgil 1980; Cadet 1986; Lawrence and Webster 2002). Due to its quasi-periodic occurrence, the BSISV provides a primary source for the predictability of the Asian monsoon on subseasonal time scales, and thus has received significant attention in the climate research community.
A number of theories have been advanced in interpreting this northward propagating BSISV, including the land–atmosphere interaction (Webster 1983), Rossby wave emanation from the eastward propagating equatorial Kelvin–Rossby wave packet (Wang and Xie 1997; Lawrence and Webster 2002), and air–sea interactions (Kemball-Cook and Wang 2001; Fu et al. 2003). Based on analyses of both an atmospheric general circulation model (GCM) simulation and reanalysis dataset, Jiang et al. (2004) identified prominent meridional asymmetric structures associated with the northward propagating BSISV. A positive equivalent barotropic vorticity perturbation and enhanced specific humidity in the lower troposphere are found to lead the convection center by a few degrees. The northward shift of low-level moisture perturbation relative to the BSISV convection, which may suggest a pre-conditioning process for its northward propagation, has also been illustrated by other observational studies (e.g., Hsu et al. 2004; Fu et al. 2006). Jiang et al. (2004) further proposed an “easterly vertical wind shear” mechanism to explain the northward propagation of the BSISV, in which the easterly vertical shear of the zonal mean flow over the Asian monsoon region could play a fundamental role. This mechanism was confirmed by a numerical study based on an idealized model (Drbohlav and Wang 2005). Most recently, this “easterly vertical wind shear” mechanism was further verified by inspecting the meridional propagation of the ISV over the eastern Pacific warm pool region (Jiang and Waliser 2008, 2009).
While great progress has been made to gain a better understanding of the BSISV during the past decades, complete interpretation of the BSISV phenomena remains elusive, including the mechanisms for its initiation and temporal/spatial-scale selection, as well as detailed evolution patterns. Moreover, the ability to properly represent this form of variability remains a great challenge to current weather/climate models (e.g., Waliser et al. 2003; Wang 2008; Kim et al. 2008). It is generally agreed that the poor representation of this variability in current models could be largely ascribed to the lack of the interaction between the large-scale circulation and small-scale convective processes, the latter of which are not explicitly resolved in current conventional GCMs, and can only be realized via so-called “parameterization” approaches.
In recent years, the cloud structure information made available based on remote-sensing techniques provides great insights into convective processes coupled with the large-scale circulation. For example, distinct cloud regimes associated with “weather states” were investigated based on International Satellite Cloud Climatology Project (ISCCP) D1 data (e.g., Jakob and Tselioudis 2003; Rossow et al. 2005; Chen and Del Genio 2009; Tromeur and Rossow 2010). In particular, Chen and Del Genio (2009) and Tromeur and Rossow (2010) analyzed the frequency of occurrence of each cloud regime during the evolution of the MJO. Their results confirm the important role of cumulus congestus clouds for the pre-conditioning of MJO deep convection as previously proposed (e.g., Johnson et al. 1999; Kikuchi and Takayabu 2004). While the ISCCP D1 data provide valuable information regarding the coupling between convection and dynamics, the major limitation is the lack of detailed information on cloud vertical structures, which is a common shortcoming of passive sensor measurements.
The recent CloudSat satellite radar mission provides an unprecedented opportunity to explore the three-dimensional cloud structures associated with the large-scale circulation, including that associated with the ISV. In the present study, vertical cloud water structures associated with the northward propagation of the BSISV are examined by utilizing cloud liquid and ice water content fields based on CloudSat estimates. These features are also compared to their counterparts from the European Centre for Medium-Range Weather Forecast (ECMWF) ERA-interim reanalysis. The cloud classification information provided by CloudSat will also be analyzed to understand the cloud water structures. Due to the limited period of CloudSat observations available at the time of this study, we will mainly focus on the analyses for three summer seasons from 2006 to 2008. The organization of this paper is as follows. The datasets employed in this study are described in Sect. 2. In Sect. 3, an objective approach to identify strong northward propagating BSISV events is introduced. In Sect. 4, vertical cloud structures associated with the BSISV are analyzed based on a composite analysis by using both CloudSat observations and ERA-interim reanalysis. Finally, a summary and a discussion are given in Sect. 5.
The primary dataset used for this study is the cloud liquid and ice water contents based on CloudSat estimates since its launch in 2006 as part of the NASA A-Train constellation of satellites (Stephens et al. 2002). The Cloud Profiling Radar (CPR) on the CloudSat satellite is a 94 GHz, nadir-viewing radar measuring backscattered power from clouds and precipitation particles in the atmospheric column within a 1.4 km across-track by 2.5 km along-track radar footprint. Measurements of radar backscatter are converted to a calibrated radar reflectivity factor, which is then used in estimates of cloud and precipitation properties, such as profiles of liquid water content (LWC) and ice water content (IWC). The minimum detectable reflectivity is approximately −30 dBZ.
The cloud LWC and IWC employed in this study are generated by the latest CloudSat retrieval algorithm (version 5.1, contained in release 4 of the CloudSat 2B-CWC-RO data product), which uses an optimal estimation approach to retrieve parameters of the cloud particle size distribution based on measurements of radar reflectivity (Austin et al. 2009). The cloud water estimates are obtained by constraining LWC values only in regions warmer than 0°C, the IWC in regions colder than −20°C, and a linear combination of the two in the intermediate temperature range. The LWC and IWC provided by the CloudSat dataset are interpolated onto grids with 1 × 1° horizontal resolution and 40 vertical levels between 1,025 and 50 hPa.
We also utilize CloudSat determinations of cloud types, including stratus (St), stratocumulus (Sc), cumulus (Cu), nimbostratus (Ns), altocumulus (Ac), altostratus (As), deep convective clouds (Dc), and high cirrus/cirrostratus clouds (Ci). These determinations are based on different rules for hydrometeor vertical and horizontal scales, the maximum effective radar reflectivity factor measured by the CloudSat CPR, and ancillary data including predicted ECMWF temperature profiles (Wang and Sassen 2007; Sassen and Wang 2008).
ERA-interim reanalysis LWC and IWC are also examined to facilitate comparison to those from the CloudSat observations. The ERA-interim, which is the latest ECMWF reanalysis (Simmons et al. 2006), benefits from several developments of the ECMWF integrated forecasting system, including improved model physics, a new humidity analysis, the use of a 4D-VAR assimilation scheme, a variational bias correction technique, and direct assimilation of early satellite radiance data. The LWC and IWC fields based on ERA-interim reanalysis extend from 1989 to present with 1.5° horizontal resolution and 37 vertical pressure levels. Since CloudSat observations are only available since 2006, we will focus our analysis on the period of 2006–2008 based on both the CloudSat dataset and ERA-interim. Note that CloudSat was not used in the assimilation for the ERA-interim reanalysis.
Rainfall observations based on Tropical Rainfall Measuring Mission (TRMM, version 3B42; Huffman et al. 2007) are employed to identify the BSISV events. TRMM 3B42 is a global precipitation product based on multi-satellite and rain gauge analyses. It provides precipitation estimates gridded on a 3-hourly temporal resolution and a 0.25° spatial resolution in a global belt extending from 50°S to 50°N.
3 An index for the boreal summer intraseasonal variability
Time series of EEOF1 during June–September from 2006 to 2008 are displayed in Fig. 1d–f. The northward propagation BSISV events as illustrated by the Hovmöller diagrams in Fig. 1a–c correspond well to positive peaks in EEOF1, with the timing of each peak in EEOF1 representing enhanced BSISV convective conditions near the equator. We then define strong northward propagating BSISV events by selecting maximum peaks in EEOF1 time series exceeding 0.8. By doing so, 10 relatively strong northward propagation events are identified during the three summers as denoted by dark dots in Fig. 1d–f. The time corresponding to each of these selected peaks is taken as reference “day 0” of each event for the following composite analysis.
4 Vertical cloud structures of the northward propagating BSISV
In this section, we will characterize the cloud structures associated with the BSISV. In particular, we will examine meridional asymmetries in the clouds relative to the BSISV convection center during its northward propagation. Such information would provide insight into the physics responsible for the northward propagation of the BSISV.
4.1 Cloud water
Composite IWC profiles based on the ERA-interim in Fig. 7 also exhibit systematic northward propagation along with the convection. While the upper-level positive (negative) IWC anomalies are largely consistent with enhanced (suppressed) convection as previously discussed in Fig. 5 by CloudSat, a southward shift of the IWC maxima relative to the convection center is also noticeable in Fig. 7, e.g., at days 10 and 15. The ERA-interim composite results exhibit much smoother patterns and more coherent northward propagation than those based on CloudSat, which could be partially due to a greater sample size of ERA-interim.
It is worth mentioning that these aforementioned cloud water features associated with the BSISV based on both CloudSat and ERA-interim datasets are mainly discussed in terms of the enhanced BSISV convection phase (e.g., after day 5 in Figs. 4, 5, 6, 7). Very similar features, including the prominent vertical tilting structure in the LWC relative to the convection center, are also perceptible during the suppressed BSISV period (e.g., between day −5 and 5 in Figs. 4, 5, 6, 7), except with an opposite sign. This further lends confidence to the robustness of these above-mentioned features in cloud water fields associated with the BSISV.
Meanwhile, maximum IWC anomalies are found aloft near 400 hPa, and are largely coincident with the convection center for CloudSat (Fig. 8b) with only a slight southward shift, but situated about 2° south of the convection center for ERA-interim (Fig. 8d). CloudSat IWC amplitudes are also much larger than those of ERA-interim. Maximum CloudSat IWC anomalies are 30 mg m−3 (Fig. 8b), while those based on the reanalysis are only about 12 mg m−3, a factor of two to three times smaller. Other studies have also reported much larger mean IWC magnitudes from CloudSat IWC compared to GCMs (e.g., Li et al. 2007; Waliser et al. 2009; Wu et al. 2009). This could be largely due to the greater CloudSat sensitivity to larger precipitation particles, including snow and graupel, as well as suspended cloud ice. Precipitation particles are not necessarily represented in GCM IWC fields.
4.2 Cloud fractional coverage and cloud classifications
In order to better understand the above cloud water structures of the BSISV, we further examine vertical profiles of cloud fractional coverage from both CloudSat and ERA-interim. Moreover, the contribution from each cloud type to the total anomalous cloud fraction pattern associated with the BSISV is examined with CloudSat cloud classifications.
To shed light on coupling between clouds and the large-scale circulation, the associated vertical-meridional circulation based on ERA-interim is also illustrated by vectors in Fig. 9a. Strong upward motion is intimately associated with enhanced convection and clouds, while regions with reduced cloud coverage are characterized by weak downward motion due to the convective overturning flow. Note that the vertical axis of maximum upward motion also displays a southward tilt with height relative to the convection center. The strongest upward vertical velocity is largely collocated with the convection center at lower and mid-levels, while it shifts to the south in the upper troposphere. This vertical configuration in cloud and circulation greatly mirrors the typical evolution of individual mesoscale convective systems (MCSs; Houze 2004). The upper-level clouds to the south of the convection center, which are coupled with the divergent outflow, resemble the anvil clouds spreading outward from deep convection.
It is worth mentioning that due to the sensitivity of the CPR, many warm cumulus clouds and thin cirrus clouds, consisting of relatively small particles, lack sufficiently large hydrometeors to be detected by the millimeter-wave radar, and thus are likely to be underestimated by the CloudSat (e.g., Zhang et al. 2007; Kubar et al. 2010). Also, because of possible CPR surface return contamination, shallow cumulus (e.g., fair weather) may likely be underrepresented by CloudSat (Sassen and Wang 2008). Additionally, it is expected that some mid-level cumulus congestus could be mixed in the altocumulus CloudSat cloud category in Fig. 10e, due to the overlapping definitions of cloud base between 2 and 3 km of altocumulus and cumulus clouds. Meanwhile, altostratus clouds shown in Fig. 10d may include some anvil clouds associated with deep convection, since high clouds are defined by CloudSat with bases higher than 7 km (Wang and Sassen 2007). In the tropics, Houze (1993) defines high clouds with bases above 6 km.
Regardless of these above caveats of CloudSat estimates, the transition in cloud structures associated with BSISV convection, from shallow cumuli, to deep convective clouds, and then upper anvil clouds displays great similarity to those which have been revealed in convectively coupled equatorial waves (CCEWs), easterly waves, and the MJO (Kiladis et al. 2009). This ubiquity of vertical clouds and dynamical profiles from MCSs to planetary scale waves remains a mystery for the climate research community on the manner in which convection is coupled to the large-scale circulation (Mapes et al. 2006; Kiladis et al. 2009).
It is also noteworthy that an analogous composite analysis of ERA-interim cloud fields, but based on 33 strong BSISV events from 1998 to 2008 as identified by the approach described in Sect. 3, yields very similar anomalous cloud structures of the BSISV to those discussed above based on the 10 events during 2006–2008. It thus further lends credibility to the findings in this study.
5 Summary and discussion
While the BSISV exerts significant modulation on the Asian summer monsoon, mechanisms to fully understand its behaviors are still elusive, and predictive skill for this form of variability remains limited. In order to gain insights into the physics governing the BSISV, particularly its northward propagation, we have analyzed vertical structures of cloud water content and cloud types associated with the BSISV over the Indian Ocean and subcontinent by capitalizing on the recent release of 3D cloud observations by the CloudSat mission. These cloud structures of the BSISV based on CloudSat are further compared to their counterparts derived from ERA-interim reanalysis. Of particular interest is that results based on both datasets suggest a marked asymmetric structure in cloud LWC relative to the convection center during the northward propagation of the BSISV. Increased LWC in the lower troposphere is located north of the BSISV rainfall maximum, and therefore leads the convection. This northward displacement of increased LWC, which is in accord with local enhanced moisture as previously documented, could play a fundamental role for the northward propagation of the BSISV. Further analysis based on CloudSat estimates indicates that the enhanced LWC anomalies to the north of the BSISV convection center are largely associated with low-level cumulus/stratocumulus clouds. Differences in the anomalous LWC fields based on CloudSat and ERA-interim are also noted. The maximum LWC anomalies are detected in the lower troposphere based on CloudSat, and in the middle troposphere in the ERA-interim.
Meanwhile, analyses based on both datasets illustrate that enhanced cloud IWC is evident in the upper troposphere corresponding to enhanced BSISV convection. While a clear southward phase shift (~2°) relative to the convection center is evident in the ERA-interim IWC field, the CloudSat IWC field shift is only weakly southward. In addition, CloudSat IWC anomalies are much stronger than the reanalysis. Further analysis based on CloudSat data indicates that the anomalous IWC pattern of the BSISV is largely associated with deep convective clouds.
While these aforementioned differences in LWC and IWC structures associated with the BSISV could be ascribed to model biases in representing convective processes and aforementioned caveats in the CloudSat retrievals, it is partially due to different IWC and LWC definitions of the model and observations. For example, as previously discussed, differences in cloud water content between ERA-interim and CloudSat estimates need to be reconciled in terms of cloud and precipitation particles. CloudSat is sensitive to both cloud and precipitation sized liquid and ice particles. As a result, retrieved estimates from CloudSat represent more than just suspended cloud liquid and ice water, which is the case for models (e.g., Waliser et al. 2009). In fact, a specific sub-sampling/filtering experiment was conducted based on CloudSat in Waliser et al. (2009), by recalculating the IWC by excluding all the retrievals flagged as either precipitating at the surface or “convective” (including the ‘‘deep convection’’ and ‘‘cumulus’’ cloud classifications). Only about 30% of profiles containing IWC in the tropics are non-convective or not associated with surface precipitation, and this filtered IWC is comparable to IWC from the GCMs. It is indicated by Fig. 10 that such “filtering” for CloudSat IWC could partially account for the difference between CloudSat and models in terms of the phase relationship between IWC and the convection center. However, larger ERA-interim LWC anomalies associated with the BSISV than those of CloudSat are still not understood, and further investigation is needed.
Another plausible explanation for the differences between CloudSat and ERA-interim datasets could be due to data sampling. The daily average cloud water fields employed in this study are based on twice daily sampling at about 1:30 pm and 1:30 am local time when CloudSat samples the Tropics. The daily fields based on the ERA-interim reanalysis contain four outputs per day, at 00, 06, 12, 18 UTC. In order to assess the impact of this sampling issue on the results, we constructed similar vertical LWC and IWC structures associated with the BSISV as in Fig. 8, but by employing only the ERA-interim outputs at 00 and 12 UTC, and 06 and 18 UTC, respectively. For the Indian Ocean BoB sector, the latter case is closer to the overpass times of CloudSat. The results suggest that the differences in the LWC and IWC structures between these ERA-interim output pairs are small (figure not shown) compared to the differences between CloudSat and ERA-interim as shown in Fig. 9. Thus, differences in the diurnal sampling of clouds do not account for the major differences in LWC and IWC structures between CloudSat and the reanalysis.
Regardless of the differences between CloudSat and ERA-interim reanalysis, some prominent features in the cloud structures associated with the BSISV are illustrated by both datasets. In particular, the transition in cloud structures associated with the northward propagating BSISV convection, from shallow cumuli, to deep convective clouds, and then upper anvil clouds, is revealed for the first time. This vertical tilting in cloud structures greatly resembles those that have been previously found in convective systems with a wide range of scales from MCSs to planetary scale waves, which could represent a fundamental coupling process between the clouds and large-scale circulation.
This research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with NASA. We are grateful to Drs. Richard Johnson, Terry Kubar, Eric Fetzer, Brian Kahn, and Parthasarathi Mukhopadhyay for fruitful discussions. We would also wish to thank two official reviewers for their constructive comments and ECMWF for providing the ERA-interim dataset. The first author (XJ) acknowledges support by NSF Climate and Large-Scale Dynamics Program under Award ATM-0934285 and NOAA CPPA Program under Award NA09OAR4310191.
This article is distributed under the terms of the Creative Commons Attribution Noncommercial License which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.
- Austin RT, Heymsfield AJ, Stephens GL (2009) Retrieval of ice cloud microphysical parameters using the CloudSat millimeter-wave radar and temperature. J Geophys Res 114. doi: 10.1029/2008JD010049
- Cadet DL (1986) Fluctuations of precipitable water over the Indian-Ocean during the 1979 summer monsoon. Tellus Ser A Dyn Meteorol Oceanogr 38:170–177Google Scholar
- Fu XH, Wang B, Tao L (2006) Satellite data reveal the 3-D moisture structure of Tropical Intraseasonal Oscillation and its coupling with underlying ocean. Geophys Res Lett 33:L03705. doi: 10.1029/2005GL025074
- Goswami BN (2005) South Asian monsoon. In: Lau WKM, Waliser DE (eds) Intraseasonal variability in the atmosphere–ocean climate system. Springer, Heidelberg, pp 389–424Google Scholar
- Houze RA (1993) Cloud dynamics. Academic Press, San DiegoGoogle Scholar
- Houze RA (2004) Mesoscale convective systems. Rev Geophys 42. doi: 10.1029/2004RG000150
- Jakob C, Tselioudis G (2003) Objective identification of cloud regimes in the tropical western Pacific. Geophys Res Lett 30. doi: 10.1029/2003GL018367
- Jiang X, Waliser DE (2008) Northward propagation of the subseasonal variability over the eastern Pacific warm pool. Geophys Res Lett 35. doi: 10.1029/2008GL033723
- Kikuchi K, Takayabu YN (2004) The development of organized convection associated with the MJO during TOGA COARE IOP: trimodal characteristics. Geophys Res Lett 31:L10101. doi: 10.1029/2004GL019601
- Kubar TL, Waliser D, Li J-L (2010) Boundary layer and cloud structure controls on tropical low cloud cover using A-Train satellite data and ECMWF analysis. J Atmos Sci (submitted)Google Scholar
- Lau WK-M, Waliser DE (2005) Intraseasonal variability in the atmosphere–ocean climate system, Springer, Heidelberg, Germany, 474 ppGoogle Scholar
- Simmons A, Uppala S, Dee D, Kobayashi S (2006) ERA-interim: New ECMWF reanalysis products from 1989 onwards, ECMWF, Shinfield Park, Reading, Berkshire RG2 9AX, UKGoogle Scholar
- Stephens GL, Vane DG, Boain RJ, Mace GG, Sassen K, Wang ZE, Illingworth AJ, O’Connor EJ, Rossow WB, Durden SL, Miller SD, Austin RT, Benedetti A, Mitrescu C, Team CS (2002) The CloudSat mission and the a-train—a new dimension of space-based observations of clouds and precipitation. Bull Am Meteorol Soc 83:1771–1790. doi: 10.1175/BAMS-83-12-1771 CrossRefGoogle Scholar
- Tromeur E, Rossow WB (2010) Interaction of tropical deep convection with the large-scale circulation in the MJO. J Clim (in press)Google Scholar
- Waliser DE (2006) Intraseasonal variations. In: Wang B (ed) The Asian monsoon. Springer, Heidelberg, p 787Google Scholar
- Waliser DE, Jin K, Kang IS, Stern WF, Schubert SD, Wu MLC, Lau KM, Lee MI, Krishnamurthy V, Kitoh A, Meehl GA, Galin VY, Satyan V, Mandke SK, Wu G, Liu Y, Park CK (2003) AGCM simulations of intraseasonal variability associated with the Asian summer monsoon. Clim Dyn 21:423–446. doi: 10.1007/s00382-003-0337-1 CrossRefGoogle Scholar
- Waliser D, Li J-L, Woods CP, Austin R, Bacmeister JT, Chern JD, Del Genio A, Jiang J, Kuang Z, Meng H, Minnis P, Platnick S, Rossow WB, Stephens G, Sun-Mack S, Tao W-K, Tompkins AM, Vane DG, Walker C, Wu D (2009) Cloud ice: a climate model challenge with signs and expectations of progress. J Geophys Res 114. doi: 10.1029/2008JD010015
- Wang B (2006) The Asian monsoon. Springer, HeidelbergGoogle Scholar
- Wang B (2008) Thrusts and prospects on understanding and predicting Asian monsoon climate. Acta Meteorol Sin 22:383–403Google Scholar
- Wang Z, Sassen K (2007) Level 2 cloud scenario classification product process description and interface control document, version 5.0, 50 pp. Available at http://www.cloudsat.cira.colostate.edu
- Wu DL, Austin RT, Deng M, Durden SL, Heymsfield AJ, Jiang JH, Lambert A, Li JL, Livesey NJ, McFarquhar GM, Pittman JV, Stephens GL, Tanelli S, Vane DG, Waliser DE (2009) Comparisons of global cloud ice from MLS, CloudSat, and correlative data sets. J Geophys Res 114:D00a24. doi: 10.1029/2008jd009946
- Yasunari T (1979) Cloundiness fluctuations associated with the northern hemisphere summer monsoon. J Meteorol Soc Jpn 57:227–242Google Scholar