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This study investigates the diurnal cycles of convective activities and low-level winds with emphasis on their interactions during the East Asian summer monsoon. We categorize atmospheric convection states using satellite-derived cloud regimes and group southerly monsoon flows with different characteristics of diurnal cycles, and found that they are closely related to each other. Over South China, deep convection and associated cirrus anvils produce a strongly negative shortwave cloud radiative effect and induce anomalous cooling in the ABL in the daytime, which reduces the diurnal amplitude of monsoon flows during the night that follows. Conversely, a fair-weather state in the daytime leads to an anomalously warm ABL that enhances the subsequent nighttime acceleration of the monsoon southerlies. The dominant cloud regimes in the daytime over South China are closely related to whether the organized deep convection over the Yun-Gui Plateau (southeast of the Tibetan Plateau) propagates eastward during the preceding night. Such a precursor of clouds/convection governing wind diurnal amplitude indicates a possible predictability of monsoon diurnal cycles over South China. Further analyses show that the cloud regimes and induced monsoon diurnal cycles over South China can regulate the moist convection over the downstream region (central China) through modulating moisture transport and convergence. Therefore, the observational statistics of this study reveal a strong coupling of clouds, radiation, winds, and precipitation at the diurnal time scale over the summer monsoon regions, in which the cloud radiative effects of atmospheric convection can strongly regulate the diurnal variations of low-level winds that further influence precipitation downstream.

This paper is the first attempt to document a simple convection-tracking method based on the IMERG precipitation product to generate an IMERG-based Convection Tracking (IMERG-CT) dataset. Up to now, precipitation datasets have been Eulerian accumulations. Now with IMERG-CT, we can estimate total rainfall based on Lagrangian accumulations, which is a very important step in diagnosing cloud-precipitation process following the evolution of air masses. Convection-tracking algorithms have traditionally been developed on the basis of brightness temperature (Tb) from satellite infrared (IR) retrievals. However, vigorous rainfall can be produced by warm-topped systems in a moist environment; this situation cannot be captured by traditional IR-based tracking but is observed in IMERG-CT. Therefore, an advantage of IMERG-CT is its ability to include the previously missing information of shallow clouds that grow into convective storms, which provides us more-complete life cycle records of convective storms than traditional IR-based tracking does. This study also demonstrates the utility of IMERG-CT through investigating various properties of convective systems in terms of the evolution before and after peak precipitation rate and amount. For example, composite analysis reveals a link between evolution of precipitation and convective development: the signature of stratiform anvils remaining after the storm has produced the maximum rainfall, as average Tb stays almost constant for 5 h after the peak of precipitation. Our study highlights the importance of joint analysis of cloud and precipitation data in time sequence, which helps to elucidate the underlying dynamic processes producing tropical rainfall and its resultant effects on the atmospheric thermodynamics.

Although Tropical Rainfall Measuring Mission (TRMM) and CloudSat/CALIPSO fly in different orbits, they frequently cross each other so that for the period between 2006 and 2010, a total of 15,986 intersect lines occurred within 20 min of each other from 30°S to 30°N, providing a rare opportunity to study tropical cloud and precipitation regimes and their internal vertical structure from near-simultaneous measurements by these active sensors. A k-means cluster analysis of TRMM and CloudSat matchups identifies three tropical cloud and precipitation regimes: the first two regimes correspond to, respectively, organized deep convection with heavy rain and cirrus anvils with moderate rain; the third regime is a convectively suppressed regime that can be further divided into three subregimes, which correspond to, respectively, stratocumulus clouds with drizzle, cirrus overlying low clouds, and nonprecipitating cumulus. Inclusion of CALIPSO data adds to the dynamic range of cloud properties and identifies one more cluster; subcluster analysis further identifies a thin, midlevel cloud regime associated with tropical mountain ranges. The radar-lidar cloud regimes are compared with the International Satellite Cloud Climatology Project (ISCCP) weather states (WSs) for the extended tropics. Focus is placed on the four convectively active WSs, namely, WS1–WS4. ISCCP WS1 and WS2 are found to be counterparts of Regime 1 and Regime 2 in radar-lidar observations, respectively. ISCCP WS3 and WS4, which are mainly isolated convection and broken, detached cirrus, do not have a strong association with any individual radar and lidar regimes, a likely effect of the different sampling strategies between ISCCP and active sensors and patchy cloudiness of these WSs.

This paper is the follow on to a previous publication by the authors, which investigated the relationship between the level of neutral buoyancy (LNB) determined from the ambient sounding and the actual outflow levels using mainly CloudSat observations. The goal of the current study is to provide a more complete characterization of LNB, deep convective outflow, and convective core, and the relationship among them, as well as the dependence on environmental parameters and convective system size. A proxy is introduced to estimate convective entrainment, namely, the difference between the LNB (based on the ambient sounding) and the actual outflow height. The principal findings are as follows: (1) Deep convection over the Warm Pool has larger entrainment rates and smaller convective cores than the counterpart over the two tropical land regions (Africa and Amazonia), lending observational support to a long-standing assumption in convection models concerning the negative relationship between the two parameters. (2) The differences in internal vertical structure of convection between the two tropical land regions and the Warm Pool suggest that deep convection over the two tropical land regions contains more intense cores. (3) Deep convective outflow occurs at a higher level when the midtroposphere is more humid and the convective system size is smaller. The convective system size dependence is postulated to be related to convective lifecycle, highlighting the importance of cloud life stage information in interpretation of snapshot measurements by satellite. Finally, implications of the study to global modeling are discussed.

A new, satellite‐based methodology is developed to evaluate convective mass flux and large‐scale total mass flux. To derive the convective mass flux, candidate profiles of in‐cloud vertical velocity are first constructed with a simple plume model under the constraint of ambient sounding and then narrowed down to the solution that matches satellite‐derived cloud top buoyancy. Meanwhile, the large‐scale total mass flux is provided separately from satellite soundings by a method developed previously. All satellite snapshots are sorted into a composite time series that delineates the evolution of a vigorous and organized convective system. Principal findings are the following. First, convective mass flux is modulated primarily by convective cloud cover, with the intensity of individual convection being less variable over time. Second, convective mass flux dominates the total mass flux only during the early hours of the convective evolution; as convective system matures, a residual mass flux builds up in the mass flux balance that is reminiscent of stratiform dynamics. The method developed in this study is expected to be of unique utility for future observational diagnosis of tropical convective dynamics and for evaluation of global climate model cumulus parameterizations in a global sense., Key Points A new strategy is proposed to analyze convective and large‐scale mass fluxes from satellite dataThe systematic evolution of the mass fluxes is captured in association with convective developmentThe findings have the potential to serve as an observational basis to assess GCM parameterizations

We have developed semi-independent methods for determining CH2O scavenging efficiencies (SEs) during strong midlatitude convection over the western, south-central Great Plains, and southeastern regions of the United States during the 2012 Deep Convective Clouds and Chemistry (DC3) Study. The Weather Research and Forecasting model coupled with chemistry (WRF-Chem) was employed to simulate one DC3 case to provide an independent approach of estimating SEs and the opportunity to study CH2O retention in ice when liquid drops freeze. Measurements of CH2O in storm inflow and outflow were acquired on board the NASA DC-8 and the NSF/National Center for Atmospheric Research Gulfstream V (GV) aircraft employing cross-calibrated infrared absorption spectrometers. This study also relied heavily on the nonreactive tracers i-/n-butane and i-/n-pentane measured on both aircraft in determining lateral entrainment rates during convection as well as their ratios to ensure that inflow and outflow air masses did not have different origins. Of the five storm cases studied, the various tracer measurements showed that the inflow and outflow from four storms were coherently related. The combined average of the various approaches from these storms yield remarkably consistent CH2O scavenging efficiency percentages of: 54% ± 3% for 29 May; 54% ± 6% for 6 June; 58% ± 13% for 11 June; and 41 ± 4% for 22 June. The WRF-Chem SE result of 53% for 29 May was achieved only when assuming complete CH2O degassing from ice. Further analysis indicated that proper selection of corresponding inflow and outflow time segments is more important than the particular mixing model employed.

A unique type of cloud in the tropical lower stratosphere, which we call “stratospheric cirrus”, is described in this study. Stratospheric cirrus clouds are generally detached from overshooting deep convection and are much smaller than subvisual cirrus often observed near the tropical tropopause. We analyzed two cases of stratospheric cirrus in the tropical and subtropical lower stratosphere captured by the space-borne lidar, Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP). Both cases occurred 2–3 hours after the most active phase of the nearby convective cloud clusters. Case 1 has a double-layer structure above the cold point height (CPH); the CPH and two cloud top heights are, respectively, 17.8, 18.9, and 19.9 km. Case 2 has a single cloud layer where CPH and the cloud top height are, respectively, 16.5 and 18.7 km. The mode radius and ice water content of the stratospheric cirrus clouds are estimated to be 4–10 μm and 0.2–0.8 mg/m 3 based on the radar-lidar method and consideration of the cloud particle terminal velocity. Comparisons with previous numerical model simulation studies suggest that the double-layer stratospheric cirrus clouds are likely from an overshooting plume, pushed up into the stratosphere in an overshoot when warm stratospheric air is inhomogeneously mixed with cold overshooting air. The single-layer stratospheric cirrus cloud is associated with some non-negligible wind shear, so it could be a jumping cirrus cloud, although we cannot rule out the possibility that it came from an overshooting plume because of the similarity in cloud characteristics and morphology between the two cases. Guided by the case studies, an automatic algorithm was developed to select stratospheric cirrus clouds for global survey and statistical analysis. A total of four years of CALIPSO and space-borne cloud radar (CloudSat) data were analyzed. Statistical analysis suggests that stratospheric cirrus clouds occur on the order of 3.0 × 10 3 times a year between 30 °S and 30 °N. Many of the stratospheric cirrus clouds are found in the pre-monsoon season in the South and Southeast Asia, where convection is deep and intense.

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Cloud-top temperature (CTT) is an important parameter for convective clouds and is usually different from the 11-μm brightness temperature due to non-blackbody effects. This paper presents an algorithm for estimating convective CTT by using simultaneous passive [Moderate Resolution Imaging Spectroradiometer (MODIS)] and active [CloudSat + Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO)] measurements of clouds to correct for the non-blackbody effect. To do this, a weighting function of the MODIS 11-μm band is explicitly calculated by feeding cloud hydrometer profiles from CloudSat and CALIPSO retrievals and temperature and humidity profiles based on ECMWF analyses into a radiation transfer model. Among 16 837 tropical deep convective clouds observed by CloudSat in 2008, the averaged effective emission level (EEL) of the 11-μm channel is located at optical depth ~0.72, with a standard deviation of 0.3. The distance between the EEL and cloud-top height determined by CloudSat is shown to be related to a parameter called cloud-top fuzziness (CTF), defined as the vertical separation between −30 and 10 dBZ of CloudSat radar reflectivity. On the basis of these findings a relationship is then developed between the CTF and the difference between MODIS 11-μm brightness temperature and physical CTT, the latter being the non-blackbody correction of CTT. Correction of the non-blackbody effect of CTT is applied to analyze convective cloud-top buoyancy. With this correction, about 70% of the convective cores observed by CloudSat in the height range of 6–10 km have positive buoyancy near cloud top, meaning clouds are still growing vertically, although their final fate cannot be determined by snapshot observations.

We examined the differences in the cloud top heights (CTHs) detected by the CloudSat radar and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) lidar. Theoretical estimates have shown that CloudSat has higher sensitivity than CALIPSO does when large particles exist. In such case it might be possible that CloudSat-determined CTHs are larger than CALIPSO-determined CTHs. We compared the global distribution of CTHs detected by CloudSat and CALIPSO (version 3, V3) using our cloud mask schemes after carefully selecting data during September–November 2006. The global mean fraction of clouds where CloudSat-determined CTHs were larger than CALIPSO-determined CTHs turned out to be unexpectedly large. The fractions were 26% and 39% at low level and midlevel, and the corresponding CTH differences were 0.56 km and 0.86 km, respectively. On the western coasts of continents, these clouds occurred within temperature inversions. Accounting for the differences in sensitivity to particle size between CloudSat and CALIPSO, the existence of such clouds indicates that the cloud tops consist of large particles with small number concentration. The discovery of such clouds was revealed by our joint analysis of CloudSat and CALIPSO. When the standard vertical feature mask (VFM) V3 was used, these clouds were also found but the fractions were less pronounced. The differences were partly attributed to the overestimation of cloud fraction in the VFM V3, although the degree of misidentification in V3 was reduced compared with that of V2.

This study presents a novel approach to estimating the intensity of hurricanes using temperature profiles from Global Positioning System radio occultation (GPSRO) measurements. Previous research has shown that the temperature difference between the ocean surface and the eyewall outflow region defines hurricanes' thermodynamic efficiency, which is directly proportional to the storm's intensity. Outflow temperatures in the eyewall region of 27 hurricanes in 2004–2011 were obtained from GPSRO observations. These observations, along with ocean surface temperatures from NASA Modern Era-Retrospective Analysis for Research and Applications, made it possible to estimate hurricane intensities using a simplified hurricane model. Our preliminary results are quantitatively consistent with best-track values from the National Hurricane Center within 9.4%. As a by-product of our study, we present for the first time GPSRO vertical temperature profiles in the vicinity of the eyewall region of hurricanes, which we compared with collocated temperature profiles from the European Centre for Medium-Range Weather Forecasts Reanalysis Interim (ERA-Interim). Some of the GPSRO data sets reveal a double tropopause in the vicinity of the eyewall—a characteristic that we do not see in ERA-Interim. We conclude that GPSRO observations can be of supplementary assistance in augmenting existing data sets used in hurricane intensity estimation. GPSROs' cloud-penetrating capability and high vertical resolution can be useful in providing soundings in the area close to the eyewall region of hurricanes revealing detailed information about their thermal structure, potentially advancing our current knowledge of their dynamics, evolution, and physics.

This paper describes a novel use of A-Train observations to estimate vertical velocities for actively growing convective plumes and to relate them to cloud internal vertical structure. Convective vertical velocity is derived from time-delayed (1–2 min) IR measurements from MODIS and IIR. Convective vertical velocities are found to be clustered around 2–4 m/s but the distributions are positively skewed with long tails extending to larger values. Land convection during the 13:30 overpasses has higher vertical velocities than those during the 1:30 overpasses; oceanic convection shows the opposite, albeit smaller, contrast. Our results also show that convection with larger vertical velocity tends to transport larger precipitation-size particle and/or greater amount of water substance to higher altitude and produces heavier rainfall. Finally, we discuss the implications of this study for the designs of future space-borne missions that focus on fast-evolving processes such as those related to clouds and precipitation.

[1] Tropical overshooting deep convection (ODC) plays an important role in affecting the heat and constituent budgets of the upper troposphere and lower stratosphere. This study investigates the properties and behaviors of such intense deep convection using a combination of CloudSat observations and geostationary satellite data. Our study approaches the subject from two unique perspectives: first, W-band cloud profiling radar (CPR) observations from CloudSat are used, which add to our knowledge of the internal vertical structure of tropical ODC; second, each snapshot observation from CloudSat is cast into the time evolution of the convective systems through joint analysis of geostationary satellite data, which provides a lifecycle view of tropical ODC. Climatology of tropical ODC based on CloudSat data is first presented and compared with previous works. Various parameters from CloudSat observations pertaining to cloud vertical extent, convective intensity, and convective environment are analyzed. Although results broadly agree with previous studies, we show that CloudSat CPR is capable of capturing both small cloud particles and large precipitation-size particles, thus presenting a more complete depiction of the internal vertical structure of tropical ODC. Geostationary satellite observations are analyzed in conjunction with CloudSat data to identify the life stage of the convective systems (CSs) in which ODC is embedded. ODC associated with the growing, mature, and dissipating stage of the CSs represents, respectively, 66.2%, 33.4%, and 0.4% of the total population. Convective intensity of the ODC is found to be stronger during the growing stage than the mature stage.

[1] Water vapor measurements from Aura Microwave Limb Sounder (MLS, above 300 hPa) and Aqua Atmospheric Infrared Sounder (AIRS, below 300 hPa) are analyzed to study the variations of moisture during the 2006–2007 and 2009–2010 El Ninos. The 2006–2007 El Nino is an East Pacific (EP) El Nino, while the 2009–2010 El Nino is a Central Pacific (CP) El Nino or El Nino Modoki. Results show that these two types of El Nino events produce different patterns of water vapor anomalies over the tropical ocean, approximately resembling the cloud anomalies shown in Su and Jiang (2013). Regression of water vapor anomalies onto the Nino-3.4 SST for the A-Train period shows a clear “upper tropospheric amplification” of the fractional water vapor change, i.e., the ratio of the change in specific humidity to the layer-averaged specific humidity. Furthermore, tropical water vapor anomalies in different circulation regimes are examined. It is shown that the variations of water vapor during the 2006–2007 El Nino are mainly controlled by the thermodynamic component, whereas both dynamic and thermodynamic components control the water vapor anomalies during the 2009–2010 El Nino. GFDL AM2.1 model simulations of water vapor and cloud anomalies for the two El Ninos are compared with the satellite observations. In general, the model approximately reproduces the water vapor anomalies on both zonal and meridional planes but it produces too strong a cloud response in the mid- and lower troposphere. The model fails to capture the dynamic component of water vapor anomalies, particularly over the Indian Ocean.

[1] Parallax is associated with an apparent shift of the position of an object when viewed from different angles. For satellite observations, especially observations with clouds, it affects collocation of measurements from different platforms. In this study, we investigate how the parallax problem affects the collocation of CloudSat and Moderate Resolution Imaging Spectroradiometer (MODIS) observations of tropical convective clouds by examining the impact of parallax correction on statistics of convective cloud properties such as cloud top temperature (CTT) and buoyancy. Previous studies circumvented the parallax problem by imposing a “flat-top” condition on the selection of convective clouds, but it inadvertently biases the statistics toward convections at mature or dissipating stages when convective plumes cease to grow but flatten out to form cirrus anvils. The main findings of this study are the following: (1) Parallax correction reduces CTT of convective clouds; the magnitude of the reduction increases with cloud top height (CTH). (2) Parallax correction also reduces the spread of CTT estimates, making it more closely clustered around the corresponding CTH. (3) The fraction of convection with positive buoyancy decreases after the parallax correction. All these changes that are due to parallax correction are most pronounced for convections above 10–12 km, highlighting the importance of parallax correction in satellite-based study of deep convection. With parallax correction applied, we further examine the contrast in convective cloud buoyancy between land and ocean and day and night and the dependence on convective cloud size; results are consistent with our general understanding of tropical convection.

[1] This study describes a satellite-based method to estimate simultaneously convective buoyancy (B) and entrainment rate (λ). The measurement requirements are cloud-top height (CTH), cloud-top temperature (CTT), cloud profiling information (from radar and lidar), and ambient sounding. Initial results of the new method applied to A-Train data are presented. It is observed that tropical oceanic convection above the boundary layer fall into two groups: deep convection (DC) and cumulus congestus (Cg). DC tend to have negative buoyancy near cloud top and λ < 10%/km. Cg are further divided into two groups due to the snapshot view of the A-Train: one has positive buoyancy and λ ≤ 10%/km and the other has negative buoyancy and λ reaching up to 50%/km. Uncertainty analysis is conducted showing that CTT and CTH are the primary source of errors, but they do not affect our conclusions qualitatively. Brief comparisons with previous studies indicate the results of this study are broadly consistent with these earlier studies. Although most of the initial results are expected, this study represents the first time, to our knowledge, that satellite data are used to estimate convective buoyancy and entrainment rate. This new, space-borne method presents an opportunity for a number of follow-up investigations. For example, it serves as a bridge to connect A-Train observations (and follow-up missions) to GCM cumulus parameterizations.