Cloud Droplet Distributions Research Articles

Design and experiment of a lightweight cloud particle imager

Abstract Accurate in-situ measurement of cloud microphysical parameters such as particle diameter, number concentration and droplet spectrum distribution are of great significance in cloud physics, climate change, numerical weather forecasting and weather modification. This work describes the design and characterization of a newly developed in situ measurement instrument for cloud and fog named lightweight cloud particle imager (LCPI). The basic measurement principle of LCPI is based on scattering light imaging of particles in a dark field. A ring LED lighting source was designed to improve the image quality. Field measurements were carried out at a high-altitude research station on Lushan Mountain. The measured size distributions showed good agreement with parallel measurements of a cloud droplet spectrometer (fog monitor-120). The field data also showed that LCPI was able to measure the size distribution of cloud droplets from 5 to 300 μm with a high spatial resolution. In addition, another observation experiment carried out in the Tuli River weather station showed that LCPI also had the ability to acquire clear images of ice crystal particles larger than 10 μm. Thus, LCPI provides an opportunity to simultaneously quantify the microphysical structure of different cloud types.

In-situ aircraft observations of aerosol and cloud microphysical characteristics of mixed-phase clouds over the North China Plain

Aerosol-cloud interactions play a vital role in climate change. This study leverages observations from the King-350 aircraft over the North China Plain on November 29, 2019, to examine aerosol and cloud microphysical characteristics of mixed-phase clouds. Through detailed vertical and spectral distributions, we investigate aerosol, cloud droplet, and ice crystal distributions in seeded clouds (SC) and natural precipitating clouds (NPC) within the same cloud system. From the vertical profile, SC and NPC have similar vertical distributions of aerosol and cloud droplets, with over 95 % of aerosols concentrated below 1600 m near the ground. Cloud droplets are more evenly distributed within the two clouds, cloud droplet number concentrations (Nc) in SC were three orders of magnitude higher than in NPC. Ice water content (IWC) and ice crystal number concentration (Ni) show distinct layer preferences—accumulating predominantly in SC's top layer and NPC's middle layer. From spectral distribution, a smaller proportion of cloud droplets (40–50 μm in diameter, the same hereafter) in SC compared to NPC. Rimed ice crystals and globular graupel (1325–1550 μm in diameter) were in SC, while plate and irregular ice crystals (300–450 μm) were in NPC with an order of magnitude higher than in SC. These microphysical differences highlight the complexity of cloud seeding efficacy, which varies based on cloud conditions and microphysical properties. In the first seeding, Ni increased by 1–2 orders of magnitude (125–300 μm) in the high Nc (Nc > 1.11 × 105 L−1) region. Seeding in low Nc (Nc < 1.11 × 105 L−1) regions was hard to be effective, especially in low Nc and low liquid water content (LWC) (LWC < 0.122 g/m3) regions. In the second seeding, ice crystals (125–250 μm) produced by the first seeding enhance the seeding efficiency. The responded regions were more sensitive to subsequent seeding, resulting in stronger reactions or longer duration.

A Similarity Measurement Method of Normal Cloud Models for the Operational Status Perception and Computing of Urban Rail Transit

The similarity measurement method is the key part of the normal cloud model as well as its applications. Too much attention has been paid on geometric and numerical features, leading to the weak interpretability and unreasonable results in the similarity measurement method of the normal cloud models. A bidirectional and weighted similarity measurement (BWSM) method is proposed by the number distribution and membership of cloud droplets on a normal cloud model. First, the cloud droplets are analyzed, and their characteristics of number distribution and membership that belong to different cloud models are obtained. Second, the similarity measurement strategy of the normal cloud models is analyzed and used to propose a BWSM method, which is compared with two commonly used similarity measurement methods of normal cloud models. The influence of expectation, entropy, and hyper entropy on the similarity measurement method of the normal cloud models is analyzed. Finally, the method is applied on the operational status perception and computing of urban rail transit. Results show the method has high rationality, validity, and applicability for perceiving and computing the operational status of urban rail transit. The method improves the interpretability of the similarity measurement of the normal cloud models and the credibility of the research based on the similarity measurement of the normal cloud models.

An extensive data set for in situ microphysical characterization of low-level clouds in a Finnish sub-Arctic site

Abstract. Continuous, semi-long-term, ground-based in situ cloud measurements were conducted during eight Pallas Cloud Experiments (PaCEs) held in autumn between 2004 and 2019. Those campaigns were carried out in the Finnish sub-Arctic region at the Sammaltunturi station (67∘58′24′′ N, 24∘06′58′′ E; 560 m a.m.s.l.), the part of the Pallas Atmosphere–Ecosystem Supersite and Global Atmosphere Watch (GAW) program. Two cloud spectrometer ground setups and a weather station were installed on the roof of the station to measure in situ cloud properties and several meteorological variables. Thus, the obtained data sets include the size distribution of cloud droplets as a measured cloud parameter along with the air temperature, dew point temperature, humidity, pressure, horizontal wind speed and direction, (global solar) sun radiation, and visibility at the station. Additionally, the number concentration, effective diameter, median volume diameter, and liquid water content from each instrument were derived. The presented data sets provide a insight into microphysics of low-level clouds in sub-Arctic conditions over a wide range of temperatures (−25.8 to 8.8 ∘C). The data are available in the Finnish Meteorological Institute (FMI) open data repository for each campaign and each cloud spectrometer ground setup individually: https://doi.org/10.23728/FMI-B2SHARE.988739D21B824C709084E88ED6C6D54B (Doulgeris et al., 2021).

A Practical, Integrated Multi-Criteria Decision-Making Scheme for Choosing Cloud Services in Cloud Systems

Currently, with the rapid development and broad application of cloud computing technology, companies tend to use cloud services to build their applications or business systems. Selecting a trustworthy cloud service is a challenging multi-criteria decision-making (MCDM) problem. Moreover, decision makers are more inclined to use linguistic descriptions to assess the quality of service (QoS) for cloud services due to the limitation of the decision makers' knowledge and the vagueness of criteria information. Therefore, we propose a practical, integrated MCDM scheme for cloud service evaluation and selection of cloud systems, allowing decision makers to compare cloud services based on QoS criteria. First, to more accurately and effectively express the uncertainty of qualitative concepts, the cloud model is used as a conversion tool for qualitative and quantitative information to quantify linguistic terms. Second, given the shortcomings of traditional differentiating measures between cloud models, a more comprehensive distance measurement algorithm using cloud droplet distribution is proposed for the cloud model. The new distance measurement algorithm is applied to the calculation of cloud model similarity and the gray correlation coefficient. The dynamic expertise weights are determined by calculating the similarity between the expert evaluation cloud model and the arithmetic mean cloud model. Then, we propose a technique for order preference by similarity to an ideal solution (TOPSIS) improved by the grey relational analysis (GRA) to calculate the relative closeness of alternatives to the positive and negative ideal solutions and establish a multi-objective optimization model that maximizes the relative closeness of all alternatives to determine the weights of the criteria. Finally, we reconstructed the QoS evaluation criteria for cloud services from both application and service perspectives, and the classical TOPSIS is applied to generate alternative rankings. The practicability and robustness of the scheme were tested through the cloud service selection problem experienced by a real mining company's scheduling platform, which can provide practical references with the theoretical basis for the selection and evaluation of cloud services.

Multi-View Polarimetric Scattering Cloud Tomography and Retrieval of Droplet Size

Tomography aims to recover a three-dimensional (3D) density map of a medium or an object. In medical imaging, it is extensively used for diagnostics via X-ray computed tomography (CT). We define and derive a tomography of cloud droplet distributions via passive remote sensing. We use multi-view polarimetric images to fit a 3D polarized radiative transfer (RT) forward model. Our motivation is 3D volumetric probing of vertically-developed convectively-driven clouds that are ill-served by current methods in operational passive remote sensing. Current techniques are based on strictly 1D RT modeling and applied to a single cloudy pixel, where cloud geometry defaults to that of a plane-parallel slab. Incident unpolarized sunlight, once scattered by cloud-droplets, changes its polarization state according to droplet size. Therefore, polarimetric measurements in the rainbow and glory angular regions can be used to infer the droplet size distribution. This work defines and derives a framework for a full 3D tomography of cloud droplets for both their mass concentration in space and their distribution across a range of sizes. This 3D retrieval of key microphysical properties is made tractable by our novel approach that involves a restructuring and differentiation of an open-source polarized 3D RT code to accommodate a special two-step optimization technique. Physically-realistic synthetic clouds are used to demonstrate the methodology with rigorous uncertainty quantification.

A First Look at Cloud Inhomogeneity and Its Effect on Lightning Optical Emission

AbstractOptical emission observed from lightning is the result of in‐cloud scattering, lightning channel geometry, and optical energy of the source discharge. To better understand how scattering affects measured optical emission, we developed a Monte Carlo model using an inhomogeneous scattering profile from a simulated storm using two‐moment bulk microphysics in the Weather Research and Forecasting model, including the concentration and size distribution of cloud droplets and ice particles. Within the Monte Carlo model lightning discharges are simulated as point and line sources. These idealized simulations indicate that for a lightning discharge at 7 km altitude approximately 22% and 13% of light, emitted near and away (respectively) from a reflectivity core will reach cloud top in a thunderstorm. The vertical gradient of the microphysical environment impacts light reaching cloud top regardless of overall particle concentration. Increasing cloud ice modifies the gradient of the microphysical profile and modulates the profile gradient effect.

Using an Arbitrary Moment Predictor to Investigate the Optimal Choice of Prognostic Moments in Bulk Cloud Microphysics Schemes

AbstractMost bulk cloud microphysics schemes predict up to three standard properties of hydrometeor size distributions, namely, the mass mixing ratio, number concentration, and reflectivity factor in order of increasing scheme complexity. However, it is unclear whether this combination of properties is optimal for obtaining the best simulation of clouds and precipitation in models. In this study, a bin microphysics scheme has been modified to act like a bulk microphysics scheme. The new scheme can predict an arbitrary combination of two or three moments of the hydrometeor size distributions. As a first test of the arbitrary moment predictor (AMP), box model simulations of condensation, evaporation, and collision‐coalescence are conducted for a variety of initial cloud droplet distributions and for a variety of configurations of AMP. The performance of AMP is assessed relative to the bin scheme from which it was built. The results show that no double‐ or triple‐moment configuration of AMP can simultaneously minimize the error of all cloud droplet distribution moments. In general, predicting low‐order moments helps to minimize errors in the cloud droplet number concentration, but predicting high‐order moments tends to minimize errors in the cloud mass mixing ratio. The results have implications for which moments should be predicted by bulk microphysics schemes for the cloud droplet category.

Retrieving homogeneous liquid cloud microphysical properties using multiple-field-of-view lidar

Liquid water content (LWC) and cloud droplet effective size (CDES) are important factors affecting atmospheric radiative transfer, and measurement of these parameters in clouds is essential. For homogeneous liquid cloud (constant extinction coefficient) with a gamma size distribution of cloud droplets, we find that LWC and CDES can be retrieved from two parameters obtained from a multiple-field-of-view (MFOV) Lidar: the intercept of the range-corrected Lidar signal (IRCLS) and the slope of the range-corrected Lidar signal (SRCLS) at different sizes of FOV. Monte Carlo simulations reveal that IRCLS at different sizes of FOV varies with both extinction coefficient and CDES while SRCLS varies only with the extinction coefficient, which depends on both LWC and CDES. This means that, after extracting the extinction coefficient using SRCLS, we can easily obtain CDES from IRCLS, and LWC can then be determined using the extinction coefficient. An innovative MFOV Lidar system is constructed to measure the LWC and CDES. A series of experiments is conducted in the northern suburb of Nanjing, China, and the LWC and CDES of homogeneous liquid cloud are obtained. Comparisons among results from the MFOV Lidar, theoretical calculation, and the global precipitation measurement satellite verify our proposed method.

Aerosol–landscape–cloud interaction: signatures of topography effect on cloud droplet formation

Abstract. Long-term in situ measurements of aerosol–cloud interactions are usually performed in measurement stations residing on hills, mountains, or high towers. In such conditions, the surface topography of the surrounding area can affect the measured cloud droplet distributions by increasing turbulence or causing orographic flows and thus the observations might not be representative for a larger scale. The objective of this work is to analyse, how the local topography affects the observations at Puijo measurement station, which is located in the 75 m high Puijo tower, which itself stands on a 150 m high hill. The analysis of the measurement data shows that the observed cloud droplet number concentration mainly depends on the cloud condensation nuclei (CCN) concentration. However, when the wind direction aligns with the direction of the steepest slope of the hill, a clear topography effect is observed. This finding was further analysed by simulating 3-D flow fields around the station and by performing trajectory ensemble modelling of aerosol- and wind-dependent cloud droplet formation. The results showed that in typical conditions, with geostrophic winds of about 10 m s−1, the hill can cause updrafts of up to 1 m s−1 in the air parcels arriving at the station. This is enough to produce in-cloud supersaturations (SSs) higher than typically found at the cloud base of ∼ 0.2 %), and thus additional cloud droplets may form inside the cloud. In the observations, this is seen in the form of a bimodal cloud droplet size distribution. The effect is strongest with high winds across the steepest slope of the hill and with low liquid water contents, and its relative importance quickly decreases as these conditions are relaxed. We therefore conclude that, after careful screening for wind speed and liquid water content, the observations at Puijo measurement station can be considered representative for clouds in a boreal environment.

Meteorological and aerosol effects on marine cloud microphysical properties

AbstractMeteorology and microphysics affect cloud formation, cloud droplet distributions, and shortwave reflectance. The Eastern Pacific Emitted Aerosol Cloud Experiment and the Stratocumulus Observations of Los‐Angeles Emissions Derived Aerosol‐Droplets studies provided measurements in six case studies of cloud thermodynamic properties, initial particle number distribution and composition, and cloud drop distribution. In this study, we use simulations from a chemical and microphysical aerosol‐cloud parcel (ACP) model with explicit kinetic drop activation to reproduce observed cloud droplet distributions of the case studies. Four cases had subadiabatic lapse rates, resulting in fewer activated droplets, lower liquid water content, and higher cloud base height than an adiabatic lapse rate. A weighted ensemble of simulations that reflect measured variation in updraft velocity and cloud base height was used to reproduce observed droplet distributions. Simulations show that organic hygroscopicity in internally mixed cases causes small effects on cloud reflectivity (CR) (&lt;0.01), except for cargo ship and smoke plumes, which increased CR by 0.02 and 0.07, respectively, owing to their high organic mass fraction. Organic hygroscopicity had larger effects on droplet concentrations for cases with higher aerosol concentrations near the critical diameter (namely, polluted cases with a modal peak near 0.1 µm). Differences in simulated droplet spectral widths (k) caused larger differences in CR than organic hygroscopicity in cases with organic mass fractions of 60% or less for the cases shown. Finally, simulations from a numerical parameterization of cloud droplet activation suitable for general circulation models compared well with the ACP model, except under high organic mass fraction.

Retrieving co‐occurring cloud and precipitation properties of warm marine boundary layer clouds with A‐Train data

AbstractIn marine boundary layer (MBL) clouds the formation of precipitation from the cloud droplet distribution in the presence of variable aerosol plays a fundamental role in determining the coupling of these clouds to their environment and ultimately to the climate system. Here the degree to which A‐Train satellite measurements can diagnose simultaneously occurring cloud and precipitation properties in MBL clouds is examined. Beginning with the measurements provided by CloudSat and Moderate Resolution Imaging Spectroradiometer (including a newly available microwave brightness temperature from CloudSat), and a climatology of MBL cloud properties from past field campaigns, an assumption is made that any hydrometeor volume could contain both cloud droplet and precipitation droplet modes. Bayesian optimal estimation is then used to derive atmospheric states by inverting a measurement vector carefully accounting for uncertainties due to instrument noise, forward model error, and assumptions. It is found that in many cases where significant precipitation coexists with cloud, due to forward model error driven by uncertainties in assumptions, the uncertainty in retrieved cloud properties is greater than the variance in the prior climatology. It is often necessary to average several thousand (hundred) precipitating (weakly precipitating) profiles to obtain meaningful information regarding the properties important to microphysical processes. Regardless, if such process level information is deemed necessary for better constraining predictive models of the climate system, measurement systems specifically designed to accomplish such retrievals must be considered for the future.

A novel method of risk assessment based on cloud inference for natural gas pipelines

The safety of natural gas pipelines not only is very important to the economy but also significantly affects social security, considering the flammable property of natural gas. This study proposes a risk assessment method based on cloud inference, with multiple factors such as third-party damage, corrosion damage, misuse of factors, design flaws, biological erosion, aging factors, and so on. First, an assessment index system is established, considering the risk factors that may lead to an accident by using a fault tree analysis (FTA). Second, the index weights and index scores are established via the analytic hierarchy process (AHP) and expert scoring method, respectively. Third, the marking results of experts are transformed into the numerical characteristics of a cloud model by using the backward cloud generator. Finally, the whole assessment cloud droplet distribution of a natural gas pipeline is acquired by using the normal cloud generator and virtual cloud. The risk assessment result of natural gas pipelines using the cloud inference method indicates that the whole risk evaluation level can be clearly shown. Moreover, the cloud inference method can solve the fuzziness and randomness of the quantitative description and qualitative concept transformation in the risk assessment process for natural gas pipelines.

Reply to comment by Yangang Liu et al. on “Cloud droplet spectral width relationship to CCN spectra and vertical velocity”

Reply to comment by Yangang Liu et al. on “Cloud droplet spectral width relationship to CCN spectra and vertical velocity”

Aerosol particles in the Mexican East Pacific. Part II: Numerical simulations of the impact of enhanced CCN on precipitation development

A number of studies have explored the effect of anthropogenic emissions on the development and evolution of precipitation in different types of clouds; however, the magnitude of the effect is still not clear, particularly for the case of deep, mixed-phase clouds. In this study, changes in the parameterization of the autoconversión process were introduced in the Advanced Regional Prediction System (ARPS) model to further evaluate this question. The simulations were initialized with cloud droplet distributions measured from an instrumented C-130 aircraft flying 600-800km offshore in the intertropical convergence zone during the East Pacific Investigations of Climate (EPIC) project. Two contrasting cases were selected, one with and the other without the influence of anthropogenic aerosols. The simulations indicate that the increased cloud condensation nuclei (CCN) concentrations lead to a delay in the formation of rain and to a decrease in precipitation that reaches the surface as a result of the inhibition of the autoconversion of cloud water to rain water and the subsequent delay in the formation of hail. In addition, hail forms at higher levels in the cloud for the case of anthropogenic CCN. The most important process in the production of rain water in both cases is the melting of hail. A decrease in the mass of hail that falls below the freezing level in the polluted case, leads to a decrease in the resulting precipitation at the surface. Changes in the initial concentration of CCN do not appear to influence the storm strength in terms of updrafts and cloud top height, suggesting little sensitivity of the cloud dynamics. A control case simulation using the old microphysics scheme produces much more precipitation than either of the clean and polluted cases. In addition, the clean case with the modified parameterization shows a better agreement to observations than the control case. It is suggested to use the new scheme to simulate deep convective development over tropical maritime regions.

Cell model of in-cloud scavenging of highly soluble gases

We investigate mass transfer during absorption of highly soluble gases such as HNO3, H2O2 by stagnant cloud droplets in the presence of inert admixtures. Thermophysical properties of the gases and liquids are assumed to be constant. Diffusion interactions between droplets, caused by the overlap of depleted of soluble gas regions around the neighboring droplets, are taken into account in the approximation of a cellular model of a gas–droplet suspension whereby a suspension is viewed as a periodic structure consisting of the identical spherical cells with periodic boundary conditions at the cell boundary. Using this model we determined temporal and spatial dependencies of the concentration of the soluble trace gas in a gaseous phase and in a droplet and calculated the dependence of the scavenging coefficient on time. We found that scavenging coefficient for gas absorption by cloud droplets remains constant and sharply decreases only at the final stage of absorption. In the calculations we employed a Monte Carlo method and assumed gamma size distribution of cloud droplets. It is shown that despite of the comparable values of Henry's law constants for the hydrogen peroxide (H2O2) and the nitric acid (HNO3), the nitric acid is scavenged more effectively by cloud droplets than the hydrogen peroxide due to a major affect of the dissociation reaction on HNO3 scavenging. It is demonstrated that scavenging of highly soluble gases by cloud droplets leads to strong decrease of soluble trace gas concentration in the interstitial air. We obtained also analytical expressions for the “equilibrium values” of concentration of the soluble trace gas in a gaseous phase and for concentration of the dissolved gas in a liquid phase for the case of hydrogen peroxide and nitric acid absorption by cloud droplets.

Worldwide data sets constrain the water vapor uptake coefficient in cloud formation

Cloud droplet formation depends on the condensation of water vapor on ambient aerosols, the rate of which is strongly affected by the kinetics of water uptake as expressed by the condensation (or mass accommodation) coefficient, αc. Estimates of αc for droplet growth from activation of ambient particles vary considerably and represent a critical source of uncertainty in estimates of global cloud droplet distributions and the aerosol indirect forcing of climate. We present an analysis of 10 globally relevant data sets of cloud condensation nuclei to constrain the value of αc for ambient aerosol. We find that rapid activation kinetics (αc > 0.1) is uniformly prevalent. This finding resolves a long-standing issue in cloud physics, as the uncertainty in water vapor accommodation on droplets is considerably less than previously thought.

Retrieval of liquid cloud microphysical properties from spaceborne active and passive sensor data based on optimal estimation theory

In order to meet the urgent requirement for accurate retrieval of liquid cloud microphysical properties, integrating the detecting advantages of active and passive sensors and combining radar reflectivity and optical depth information from CloudSat and Aqua, a new retrieval algorithm of liquid cloud microphysical parameters is proposed according to the optimal estimation theory. By assuming the lognormal size distribution of cloud droplets and establishing functional relationships between measurement and retrieval variables based on forward physical model, with the prior information about spectral distribution parameters, the optimal solutions of spectral parameters are obtained after iteratively calculating, then the microphysical parameters of liquid cloud could be retrieved based on forward physical model, and the uncertainty can be calculated according to error propagation theory. By designing retrieval scheme and using measured case data, the retrieval results are compared with the data published by CloudSat official institutions and those retrieved using empirical algorithms, showing that retrievals of liqiud cloud microphysical parameters based on optimal estimation theory by using combined active and passive sensor data are well consistent with official released data, which makes up for the disadvantages of empirical algorithms that have large error and poor expansibility and gives some important references for retrieval research of liquid cloud microphysical parameters based on domestic spaceborne and airborne W-band millimeter-wave radar data.

Investigation of the action of hygroscopic particles on a warm convective cloud from the results of numerical simulation

The results of a numerical simulation of the action of hygroscopic particles on a warm convective cloud with the purpose of obtaining additional precipitation are presented. The one-dimensional numerical model considered in this work describes the evolution of the cloud medium in the central part of an axisymmetric convective cloud at the specified height-variable velocity of the upward air flow which forms the cloud. Our model comprehensively describes microphysical processes in the cloud medium with the use of the kinetic equation for the size distribution of cloud droplets. This model makes it possible to obtain the spatiotemporal pattern of the cloud formation and development and to analyze regular features in the cloud evolution under the action of hygroscopic reagents. The cloud characteristics calculated with the use of this model correspond to the cloud parameters observed in natural conditions of the atmosphere. The process of precipitation stimulation by hygroscopic particles in convective clouds with vertical thicknesses of 2.5–4.5 km was analyzed on the basis of the results of numerical calculations. The results of calculations of the dependence of the intensity and total amount of precipitation on the vertical cloud thickness and parameters of particles introduced into the cloud are presented. It is shown that hygroscopic particles with root-mean-cube radii of 1–1.5 μm are the most effective for acting on warm convective clouds with the purpose of obtaining additional precipitation. In this case, the required reagent expenditure is 100–200 kg/km2. The conditions necessary for obtaining the maximal positive effect of the action are elucidated.

Progress in Understanding Land‐Surface‐Atmosphere Coupling from LBA Research

LBA research has deepened our understanding of the role of soil water storage, clouds and aerosols in land‐atmosphere coupling. We show how the reformulation of cloud forcing in terms of an effective cloud albedo per unit area of surface gives a useful measure of the role of clouds in the surface energy budget over the Amazon. We show that the diurnal temperature range has a quasi‐linear relation to the daily mean longwave cooling; and to effective cloud albedo because of the tight coupling between the near‐surface climate, the boundary layer and the cloud field. The coupling of surface and atmospheric processes is critical to the seasonal cycle: deep forest rooting systems make water available throughout the year, whereas in the dry season the shortwave cloud forcing is reduced by regional scale subsidence, so that more light is available for photosynthesis. At sites with an annual precipitation above 1900 mm and a dry season length less than 4 months, evaporation rates increased in the dry season, coincident with increased radiation. In contrast, ecosystems with precipitation less than 1700 mm and a longer dry season showed clear evidence of reduced evaporation in the dry season coming from water stress. In all these sites, the seasonal variation of the effective cloud albedo is a major factor in determining the surface available energy. Dry season fires add substantial aerosol to the atmosphere. Aerosol scattering and absorption both reduce the total downward surface radiative flux, but increase the diffuse/direct flux ratio, which increases photosynthetic efficiency. Convective plumes produced by fires enhance the vertical transport of aerosols over the Amazon, and effectively inject smoke aerosol and gases directly into the middle troposphere with substantial impacts on mid‐tropospheric dispersion. In the rainy season in Rondônia, convection in low‐level westerly flows with low aerosol content resembles oceanic convection with precipitation from warm rain processes and little electrification. But in the same region in the dry season, widespread fires produce a high aerosol loading with high numbers of cloud condensation nuclei from biomass burning; and convection is then dominated by ice‐phase processes giving deep clouds with frequent lightning and convective tops in the lower stratosphere. Recent studies based on measurements of CCN and cloud droplet distributions have successfully modeled this wide range of convective regimes, and shown the fundamental link between cloud droplet spectra, convective structure and precipitation. The regional scale circulation responds to precipitation and aerosol forcing, as well as the memory provided by soil moisture. The field observations from LBA have been essential to identifying the interactions of critical processes, and for the development and evaluation of our models of the coupled system.