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To improve understanding of the convective processes key to the Madden–Julian oscillation (MJO) initiation, the Dynamics of the MJO (DYNAMO) and the Atmospheric Radiation Measurement Program (ARM) MJO Investigation Experiment (AMIE) collected 4 months of observations from three radars—the S-band dual-polarization Doppler radar (S-Pol), the C-band Shared Mobile Atmospheric Research and Teaching Radar (SMART-R), and Ka-band ARM zenith radar (KAZR)—along with radiosonde and comprehensive surface meteorological instruments on Addu Atoll, Maldives, in the tropical Indian Ocean. One DYNAMO/AMIE hypothesis suggests that the evolution of shallow and congestus cloud populations is essential to the initiation of the MJO. This study focuses on evaluating the ability of these three radars to document the full spectrum of cloud populations and to construct a merged cloud–precipitation radar dataset that can be used to test this hypothesis. Comparisons between collocated observations from the three radars show that KAZR provides the only reliable estimate of shallow clouds, while S-Pol/SMART-R can reasonably detect congestus within the 30–50-km range in addition to precipitating deep clouds. On the other hand, KAZR underestimates cloud-top heights due to rainfall attenuation in ~34% of the precipitating clouds, and an empirical method to correct KAZR cloud-top height bias is proposed. Finally, a merged KAZR–S-Pol dataset is produced to provide improved cloud-top height estimates, total hydrometeor microphysics, and radiative heating rate retrievals. With this dataset the full spectrum of tropical convective clouds during DYNAMO/AMIE can be reliably constructed and, together with complimentary radiosonde data, it can be used to study the role of shallow and congestus clouds in the initiation of the MJO.

A first-of-a-kind, extended-term cloud aircraft campaign was conducted to obtain an in situ statistical characterization of continental boundary layer clouds needed to investigate cloud processes and refine retrieval algorithms. Coordinated by the Atmospheric Radiation Measurement (ARM) Aerial Facility (AAF), the Routine AAF Clouds with Low Optical Water Depths (CLOWD) Optical Radiative Observations (RACORO) field campaign operated over the ARM Southern Great Plains (SGP) site from 22 January to 30 June 2009, collecting 260 h of data during 59 research flights. A comprehensive payload aboard the Center for Interdisciplinary Remotely-Piloted Aircraft Studies (CIRPAS) Twin Otter aircraft measured cloud microphysics, solar and thermal radiation, physical aerosol properties, and atmospheric state parameters. Proximity to the SGP’s extensive complement of surface measurements provides ancillary data that support modeling studies and facilitates evaluation of a variety of surface retrieval algorithms. The five-month duration enabled sampling a range of conditions associated with the seasonal transition from winter to summer. Although about twothirds of the flights during which clouds were sampled occurred in May and June, boundary layer cloud fields were sampled under a variety of environmental and aerosol conditions, with about 77% of the cloud flights occurring in cumulus and stratocumulus. Preliminary analyses illustrate use of these data to analyze aerosol–cloud relationships, characterize the horizontal variability of cloud radiative impacts, and evaluate surface-based retrievals. We discuss how an extended-term campaign requires a simplified operating paradigm that is different from that used for typical, short-term, intensive aircraft field programs.

Two isolated deep convective clouds (DCCs) that developed in clean‐humid and polluted‐dry air masses, observed during the Tropical Pacific Warm Pool International Cloud Experiment (TWP‐ICE) and U.K. Aerosol and Chemical Transport in Tropical Convection (ACTIVE) campaigns, respectively, are simulated using a three‐dimensional cloud‐resolving model with size‐resolved aerosol and cloud microphysics. We examine the impacts of different homogeneous and immersion freezing parameterizations on the anvil characteristics and the water vapor content (WVC) in the tropical tropopause layer (TTL) for the two DCCs that developed in contrasting environments. The modeled cloud properties such as liquid/ice water path and precipitation generally agree with the available radar and satellite retrievals and in situ aircraft measurements. We find that anvil microphysical properties such as ice number concentration and ice effective radius (rei) are sensitive to the homogeneous freezing parameterizations (HomFPs) under both the clean‐humid and polluted‐dry conditions, while upper level convection and WVC in the TTL air are only sensitive to HomFPs under the polluted‐dry condition. Specifically, the cloud anvil with the Koop et al. (2000) relative humidity dependent scheme has up to 50% and 70% lower ice number than those with other schemes (temperature dependent) for the clean‐humid and polluted‐dry cases, respectively. Consequently, the rei is increased by 20–30 μm in both cases. As a result, extinction coefficient of cloud anvils is reduced by over 30%. Anvil size and evolution are also much affected by HomFPs. Higher immersion‐freezing rate (Bigg, 1953) leads to a stronger convective cloud due to larger latent heat release resulted from much higher freezing rates, with larger ice water path but less precipitation in both humid and dry conditions. Consequently, the domain‐averaged homogeneous freezing rates are enhanced by over 15%. Also, the higher immersion‐freezing rate results in over 1.5 times higher ice number concentrations, much reduced rei in the cloud anvil, and larger anvil size. The moistening effect of deep convection on the TTL clear air is very significant, with increases of a few times relative to the WVC before convection under both humid and dry conditions. Different HomFPs does not make much difference in WVC and upper level convection in the clean‐humid case, but in the polluted‐dry case, the HomFPs with lower nucleation rates predict about 25% lower WVC relative to the HomFPs with higher nucleation rates. Under both humid and dry conditions, the Bigg (1953) immersion freezing predicts about 25% higher WVC relative to the Vali (1975) parameterization, due to stronger transport and the larger anvil area in the domain.

Accurate observations of cloud microphysical properties are needed for evaluating and improving the representation of cloud processes in climate models and better estimate of the Earth radiative budget. However, large differences are found in current cloud products retrieved from ground‐based remote sensing measurements using various retrieval algorithms. Understanding the differences is an important step to address uncertainties in the cloud retrievals. In this study, an in‐depth analysis of nine existing ground‐based cloud retrievals using ARM remote sensing measurements is carried out. We place emphasis on boundary layer overcast clouds and high level ice clouds, which are the focus of many current retrieval development efforts due to their radiative importance and relatively simple structure. Large systematic discrepancies in cloud microphysical properties are found in these two types of clouds among the nine cloud retrieval products, particularly for the cloud liquid and ice particle effective radius. Note that the differences among some retrieval products are even larger than the prescribed uncertainties reported by the retrieval algorithm developers. It is shown that most of these large differences have their roots in the retrieval theoretical bases, assumptions, as well as input and constraint parameters. This study suggests the need to further validate current retrieval theories and assumptions and even the development of new retrieval algorithms with more observations under different cloud regimes. Key Points Large differences exist for cloud properties retrieved from various techniques These differences are highly related to the algorithm details and inputs Cloud retrieval uncertainties need to be further understood and quantified

Deep convective systems (DCSs) consist of intense convective cores (CC), large stratiform rain (SR) regions, and extensive nonprecipitating anvil clouds (AC). This study focuses on the evolution of these three components and the factors that affect system lifetime and AC production. An automated satellite tracking method is used in conjunction with a recently developed multisensor hybrid classification to analyze the evolution of DCS structure in a Lagrangian framework over the central United States. Composite analysis from 4221 tracked DCSs during two warm seasons (May–August, 2010–2011) shows that maximum system size correlates with lifetime, and longer‐lived DCSs have more extensive SR and AC. For short to medium systems (lifetimes <6 h), the lifetime is mainly attributed to the intensity of the initial convection. Systems that last longer than 6 h are associated with up to 50% higher midtropospheric relative humidity and up to 40% stronger middle to upper tropospheric wind shear. Such environments allow continuous growth of detrained hydrometeors by deposition, supporting further development of the SR and AC region, as indicated by the increased staggered timing between stratiform clouds and peak convective intensity, thus prolonging the system lifetime beyond 6 h. Regression analysis shows that the areal coverage of thick AC is strongly correlated with the size of CC, updraft strength, and SR area. Ambient upper tropospheric wind speed and wind shear also play an important role for convective AC production, where for systems with large AC (radius >120 km) they are 24% and 20% higher, respectively, than those with small AC (radius = 20 km). Key Points Initial convective intensity determines lifetime of short‐lived systems Higher relative humidity and wind shear favor longer‐lived convective systems Convective intensity and wind speed/shear contribute to large anvil cloud extent

The single‐layer mixed‐phase clouds observed during the Atmospheric Radiation Measurement (ARM) program's Mixed‐Phase Arctic Cloud Experiment (MPACE) are simulated with a three‐dimensional cloud‐resolving model, the System for Atmospheric Modeling (SAM), coupled with an explicit bin microphysics scheme and a radar simulator. By implementing an aerosol‐dependent and a temperature‐ and supersaturation‐dependent ice nucleation scheme and treating IN size distribution prognostically, the link between ice crystal and aerosol properties is established to study aerosol indirect effects. Two possible ice enhancement mechanisms, activation of droplet evaporation residues by condensation followed by freezing and droplet evaporation freezing by contact freezing inside out, are scrutinized by extensive comparisons with the in situ and remote sensing measurements. Simulations with either mechanism agree well with the in situ and remote sensing measurements of ice microphysical properties but liquid water content is slightly underpredicted. These two mechanisms give similar cloud properties, although ice nucleation occurs at very different rates and locations. Ice nucleation from activation of evaporation nuclei occurs mostly near cloud top areas, while ice nucleation from the drop freezing during evaporation has no significant location preference. Both ice enhancement mechanisms contribute dramatically to ice formation with ice particle concentration of 10–15 times higher relative to the simulation without either of them. Ice nuclei (IN) recycling from ice sublimation contributes significantly to maintaining concentrations of IN and ice particles in this case, implying an important role to maintain the observed long‐term existence of mixed‐phase clouds. Cloud can be very sensitive to IN initially but become much less sensitive as cloud evolves to a steady mixed‐phase condition.

Cloud radiative effects on surface downwelling fluxes are investigated using datasets from the Atmospheric Radiation Measurement Program (ARM) sites in the tropical western Pacific Ocean (TWP) region. The Nauru Island (Republic of Nauru) and Darwin, Australia, sites show large variability in sky cover, downwelling radiative fluxes, and surface cloud radiative effect (CRE) that is due to El Niño–Southern Oscillation (ENSO) and the Australian monsoon, respectively, whereas the Manus Island (Papua New Guinea) site shows little intraseasonal or interannual variability. At Nauru, the average shortwave (SW) surface CRE varies from −38.2 W m−2during La Niña conditions to −90.6 W m−2during El Niño conditions. The average longwave (LW) CRE ranges from 9.5 to 15.8 W m−2during La Niña and El Niño conditions, respectively. At Manus, the average SW and LW CREs vary by less than 5 and 2 W m−2, respectively, between the ENSO phases. The variability at Darwin is even larger than at Nauru, with average SW (LW) CRE ranging from −27.0 (8.6) W m−2in the dry season to −95.8 (17.0) W m−2in the wet season. Cloud radar measurements of cloud-base and cloud-top heights are used to define cloud types to examine the effect of cloud type on the surface CRE. Clouds with low bases contribute 71%–75% of the surface SW CRE and 66%–74% of the surface LW CRE at the three TWP sites, clouds with midlevel bases contribute 8%–9% of the SW CRE and 12%–14% of theLW CRE, and clouds with high bases contribute 16%–19% of the SW CRE and 15%–21% of the LW CRE.

The Tropical Warm Pool–International Cloud Experiment (TWP‐ICE) provided extensive observational data sets designed to initialize, force, and constrain atmospheric model simulations. In this first of a two‐part study, precipitation and cloud structures within nine cloud‐resolving model simulations are compared with scanning radar reflectivity and satellite infrared brightness temperature observations during an active monsoon period from 19 to 25 January 2006. Seven of nine simulations overestimate convective area by 20% or more leading to general overestimation of convective rainfall. This is balanced by underestimation of stratiform rainfall by 5% to 50% despite overestimation of stratiform area by up to 65% because of a preponderance of very low stratiform rain rates in all simulations. All simulations fail to reproduce observed radar reflectivity distributions above the melting level in convective regions and throughout the troposphere in stratiform regions. Observed precipitation‐sized ice reaches higher altitudes than simulated precipitation‐sized ice despite some simulations that predict lower than observed top‐of‐atmosphere infrared brightness temperatures. For the simulations that overestimate radar reflectivity aloft, graupel is the cause with one‐moment microphysics schemes whereas snow is the cause with two‐moment microphysics schemes. Differences in simulated radar reflectivity are more highly correlated with differences in mass mean melted diameter (Dm) than differences in ice water content. Dm is largely dependent on the mass‐dimension relationship and gamma size distribution parameters such as size intercept (N0) and shape parameter (μ). Having variable density, variable N0, or μ greater than zero produces radar reflectivities closest to those observed. Key Points All simulations significantly underestimate stratiform rain rates Simulations fail to reproduce observed radar reflectivity aloft Assumed size distribution parameters cause larger differences in dBZ than IWC

Vertically pointing millimeter-wavelength radar observations of anvil clouds extending from mesoscale convective systems (MCSs) that pass over an Atmospheric Radiation Measurement Program (ARM) field site in Niamey, Niger, are compared to anvil structures generated by the Weather Research and Forecasting (WRF) mesoscale model using six different microphysical schemes. The radar data provide the statistical distribution of the radar reflectivity values as a function of height and anvil thickness. These statistics are compared to the statistics of the modeled anvil cloud reflectivity at all altitudes. Requiring the model to be statistically accurate at all altitudes is a stringent test of the model performance. The typical vertical profile of radiative heating in the anvil clouds is computed from the radar observations. Variability of anvil structures from the different microphysical schemes provides an estimate of the inherent uncertainty in anvil radiative heating profiles. All schemes underestimate the optical thickness of thin anvils and cirrus, resulting in a bias of excessive net anvil heating in all of the simulations.

A 4-yr climatology of midlevel clouds is presented from vertically pointing cloud lidar and radar measurements at the Atmospheric Radiation Measurement Program (ARM) site at Darwin, Australia. Few studies exist of tropical midlevel clouds using a dataset of this length. Seventy percent of clouds with top heights between 4 and 8 km are less than 2 km thick. These thin layer clouds have a peak in cloud-top temperature around the melting level (0°C) and also a second peak around −12.5°C. The diurnal frequency of thin clouds is highest during the night and reaches a minimum around noon, consistent with variation caused by solar heating. Using a 1.5-yr subset of the observations, the authors found that thin clouds have a high probability of containing supercooled liquid water at low temperatures: ∼20% of clouds at −30°C, ∼50% of clouds at −20°C, and ∼65% of clouds at −10°C contain supercooled liquid water. The authors hypothesize that thin midlevel clouds formed at the melting level are formed differently during active and break monsoon periods and test this over three monsoon seasons. A greater frequency of thin midlevel clouds are likely formed by increased condensation following the latent cooling of melting during active monsoon periods when stratiform precipitation is most frequent. This is supported by the high percentage (65%) of midlevel clouds with preceding stratiform precipitation and the high frequency of stable layers slightly warmer than 0°C. In the break monsoon, a distinct peak in the frequency of stable layers at 0°C matches the peak in thin midlevel cloudiness, consistent with detrainment from convection.