The Observations and Modeling of the Green Ocean Amazon 2014–2015 (GoAmazon2014/5) experiment took place around the urban region of Manaus in central Amazonia across 2 years. The urban pollution plume was used to study the susceptibility of gases, aerosols, clouds, and rainfall to human activities in a tropical environment. Many aspects of air quality, weather, terrestrial ecosystems, and climate work differently in the tropics than in the more thoroughly studied temperate regions of Earth. GoAmazon2014/5, a cooperative project of Brazil, Germany, and the United States, employed an unparalleled suite of measurements at nine ground sites and on board two aircraft to investigate the flow of background air into Manaus, the emissions into the air over the city, and the advection of the pollution downwind of the city. Herein, to visualize this train of processes and its effects, observations aboard a low-flying aircraft are presented. Comparative measurements within and adjacent to the plume followed the emissions of biogenic volatile organic carbon compounds (BVOCs) from the tropical forest, their transformations by the atmospheric oxidant cycle, alterations of this cycle by the influence of the pollutants, transformations of the chemical products into aerosol particles, the relationship of these particles to cloud condensation nuclei (CCN) activity, and the differences in cloud properties and rainfall for background compared to polluted conditions. The observations of the GoAmazon2014/5 experiment illustrate how the hydrologic cycle, radiation balance, and carbon recycling may be affected by present-day as well as future economic development and pollution over the Amazonian tropical forest.

CHUVA, meaning “rain” in Portuguese, is the acronym for the Cloud Processes of the Main Precipitation Systems in Brazil: A Contribution to Cloud-Resolving Modeling and to the Global Precipitation Measurement (GPM). The CHUVA project has conducted five field campaigns; the sixth and last campaign will be held in Manaus in 2014. The primary scientific objective of CHUVA is to contribute to the understanding of cloud processes, which represent one of the least understood components of the weather and climate system. The five CHUVA campaigns were designed to investigate specific tropical weather regimes. The first two experiments, in Alcantara and Fortaleza in northeastern Brazil, focused on warm clouds. The third campaign, which was conducted in Belém, was dedicated to tropical squall lines that often form along the sea-breeze front. The fourth campaign was in the Vale do Paraiba of southeastern Brazil, which is a region with intense lightning activity. In addition to contributing to the understanding of cloud process evolution from storms to thunderstorms, this fourth campaign also provided a high-fidelity total lightning proxy dataset for the NOAA Geostationary Operational Environmental Satellite (GOES)-R program. The fifth campaign was carried out in Santa Maria, in southern Brazil, a region of intense hailstorms associated with frequent mesoscale convective complexes. This campaign employed a multimodel high-resolution ensemble experiment. The data collected from contrasting precipitation regimes in tropical continental regions allow the various cloud processes in diverse environments to be compared. Some examples of these previous experiments are presented to illustrate the variability of convection across the tropics.

Abstract. This study provides an overview of precipitation processes and their sensitivities to environmental conditions in the Central Amazon Basin near Manaus during the GoAmazon2014/5 and ACRIDICON-CHUVA experiments. This study takes advantage of the numerous measurement platforms and instrument systems operating during both campaigns to sample cloud structure and environmental conditions during 2014 and 2015; the rainfall variability among seasons, aerosol loading, land surface type, and topography has been carefully characterized using these data. Differences between the wet and dry seasons were examined from a variety of perspectives. The rainfall rates distribution, total amount of rainfall, and raindrop size distribution (the massweighted mean diameter) were quantified over both seasons.The dry season generally exhibited higher rainfall rates than the wet season and included more intense rainfall periods. However, the cumulative rainfall during the wet season was 4 times greater than that during the total dry season rainfall, as shown in the total rainfall accumulation data. The typical size and life cycle of Amazon cloud clusters (observed by satellite) and rain cells (observed by radar) were examined, as were differences in these systems between the seasons. Moreover, monthly mean thermodynamic and dynamic variables were analysed using radiosondes to elucidate the differences in rainfall characteristics during the wet and dry seasons. The sensitivity of rainfall to atmospheric aerosol loading was discussed with regard to mass-weighted mean diameter and rain rate. This topic was evaluated only durPublished by Copernicus Publications on behalf of the European Geosciences Union. L. A. T. Machado et al.: Overview: Precipitation characteristics and sensitivities to environmental conditionsing the wet season due to the insignificant statistics of rainfall events for different aerosol loading ranges and the low frequency of precipitation events during the dry season. The impacts of aerosols on cloud droplet diameter varied based on droplet size. For the wet season, we observed no dependence between land surface type and rain rate. However, during the dry season, urban areas exhibited the largest rainfall rate tail distribution, and deforested regions exhibited the lowest mean rainfall rate. Airplane measurements were taken to characterize and contrast cloud microphysical properties and processes over forested and deforested regions. Vertical motion was not correlated with cloud droplet sizes, but cloud droplet concentration correlated linearly with vertical motion. Clouds over forested areas contained larger droplets than clouds over pastures at all altitudes. Finally, the connections between topography and rain rate were evaluated, with higher rainfall rates identified at higher elevations during the dry season.

Abstract.A polarimetric X-band radar has been deployed during one month (April 2011) for a field campaign in Fortaleza, Brazil, together with three additional laser disdrometers. The disdrometers are capable of measuring the raindrop size distributions (DSDs), hence making it possible to forward-model theoretical polarimetric X-band radar observables at the point where the instruments are located. This setup allows to thoroughly test the accuracy of the X-band radar measurements as well as the algorithms that are used to correct the radar data for radome and rain attenuation. For the campaign in Fortaleza it was found that radome attenuation dominantly affects the measurements. With an algorithm that is based on the self-consistency of the polarimetric observables, the radome induced reflectivity offset was estimated. Offset corrected measurements were then further corrected for rain attenuation with two different schemes. The performance of the post-processing steps was analyzed by comparing the data with disdrometer-inferred polarimetric variables that were measured at a distance of 20 km from the radar. Radome attenuation reached values up to 14 dB which was found to be consistent with an empirical radome attenuation vs. rain intensity relation that was previously developed for the same radar type. In contrast to previous work, our results suggest that radome attenuation should be estimated individually for every view direction of the radar in order to obtain homogenous reflectivity fields.

The explicit coupling at meter and second scales of vegetation's responses to the atmospheric-boundary layer dynamics drives a dynamic heterogeneity that influences canopy-top fluxes and cloud formation. Focusing on a representative day during the Amazonian dry season, we investigate the diurnal cycle of energy, moisture and carbon dioxide at the canopy top, and the transition from clear to cloudy conditions. To this end, we compare results from a large-eddy simulation technique, a high-resolution global weather model, and a complete observational data set collected during the GoAmazon14/15 campaign. The overall model-observation comparisons of radiation and canopy-top fluxes, turbulence, and cloud dynamics are very satisfactory, with all the modeled variables lying within the standard deviation of the monthly aggregated observations. Our analysis indicates that the timing of the change in the daylight carbon exchange, from a sink to a source, remains uncertain and is probably related to the stomata closure caused by the increase in vapor pressure deficit during the afternoon. We demonstrate quantitatively that heat and moisture transport from the subcloud layer into the cloud layer are misrepresented by the global model, yielding low values of specific humidity and thermal instability above the cloud base. Finally, the numerical simulations and observational data are adequate settings for benchmarking more comprehensive studies of plant responses, microphysics, and radiation. Plain Language Summary Clouds and forest in the Amazonian rainforest region are closely related. We investigated the final month of the Amazonian dry season in order to study interactions between the rainforest and the overlying atmosphere, placing particular emphasis on studying small spatiotemporal effects, such as that of cloud shading on photosynthesis. We employed three different methods: a cloud-turbulence resolving model, a global weather model, and a complete set of canopy-top and atmospheric observations. We holistically studied these relationships by systematically analyzing the characteristics of incoming solar radiation, evapotranspiration, and cloud cover and thickness. This comparison enabled us to make two relevant findings related to these diurnal carbon and cloud cycles. First, we observed that photosynthesis is offset by the soil carbon dioxide efflux earlier than the two models calculations. With respect to cloud formation and intensification, we showed quantitatively that the inefficiently modeled moisture transport leads to less active shallow convection, which may be insufficient to trigger deep convection. This systematic study paves the way for more comprehensive studies that would include more complex descriptions of microphysics processes and radiation, as well as chemistry and aerosol formation.

Abstract. The dominant hydrometeor types associated with Brazilian tropical precipitation systems are identified via research X-band dual-polarization radar deployed in the vicinity of the Manaus region (Amazonas) during both the GoAmazon2014/5 and ACRIDICON-CHUVA field experiments. The present study is based on an agglomerative hierarchical clustering (AHC) approach that makes use of dual polarimetric radar observables (reflectivity at horizontal polarization ZH, differential reflectivity ZDR, specific differential-phase KDP, and correlation coefficient ρHV) and temperature data inferred from sounding balloons. The sensitivity of the agglomerative clustering scheme for measuring the intercluster dissimilarities (linkage criterion) is evaluated through the wet-season dataset. Both the weighted and Ward linkages exhibit better abilities to retrieve cloud microphysical species, whereas clustering outputs associated with the centroid linkage are poorly defined. The AHC method is then applied to investigate the microphysical structure of both the wet and dry seasons. The stratiform regions are composed of five hydrometeor classes: drizzle, rain, wet snow, aggregates, and ice crystals, whereas convective echoes are generally associated with light rain, moderate rain, heavy rain, graupel, aggregates, and ice crystals. The main discrepancy between the wet and dry seasons is the presence of both low- and high-density graupel within convective regions, whereas the rainy period exhibits only one type of graupel. Finally, aggregate and ice crystal hydrometeors in the tropics are found to exhibit higher polarimetric values compared to those at midlatitudes.

The GOES-16 mesoscale domain sector (MDS) scans with 1-min intervals are used in this study to analyze a severe thunderstorm case that occurred in southeastern Brazil. The main objective is to evaluate the GOES-16 MDS rapid scans against the operational full-disk scans with lower temporal resolution for nowcasting. Data from a C-band radar, observed sounding, and a ground-based lightning network are also used in the analysis. A group of thunderstorms formed in the afternoon of 29 November 2017 in an environment of moderate convective available potential energy (CAPE) and deep-layer shear. The storms presented supercell characteristics and intense lightning activity with peak rates in excess of 150 flashes per 5 min. The satellite-derived trends with 1-min interval were skillful in detecting thunderstorm intensification, mainly in the developing stage. The decrease in cloud-top 10.35-μm brightness temperature was accompanied by increases in ice mass flux, concentration of small ice particles at cloud top, and storm depth. In the mature stage, there is no evident trend in the satellite-derived parameters that could indicate storm intensification, but the cluster area expands suggesting cloud-top divergence. The 1-min rapid scans indicate greater lead time to severe weather relative to 10- and 15-min-resolution imagery, but also presented numerous false alarms (indication of severe weather but no occurrence) due to oscillations in the satellite-derived parameters. The parameters calculated every 5 min presented better skill than 10 and 15 min and fewer false alarms than 1 min.

This paper presents a new statistical algorithm to estimate rainfall over the Amazon Basin region using the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI). The algorithm relies on empirical relationships derived for different raining-type systems between coincident measurements of surface rainfall rate and 85-GHz polarization-corrected brightness temperature as observed by the precipitation radar (PR) and TMI on board the TRMM satellite. The scheme includes rain/no-rain area delineation (screening) and system-type classification routines for rain retrieval. The algorithm is validated against independent measurements of the TRMM-PR and S-band dual-polarization Doppler radar (S-Pol) surface rainfall data for two different periods. Moreover, the performance of this rainfall estimation technique is evaluated against well-known methods, namely, the TRMM-2A12 [the Goddard profiling algorithm (GPROF)], the Goddard scattering algorithm (GSCAT), and the National Environmental Satellite, Data, and Information Service (NESDIS) algorithms. The proposed algorithm shows a normalized bias of approximately 23% for both PR and S-Pol ground truth datasets and a mean error of 0.244 mm h Ϫ1 (PR) and Ϫ0.157 mm h Ϫ1 (S-Pol). For rain volume estimates using PR as reference, a correlation coefficient of 0.939 and a normalized bias of 0.039 were found. With respect to rainfall distributions and rain area comparisons, the results showed that the formulation proposed is efficient and compatible with the physics and dynamics of the observed systems over the area of interest. The performance of the other algorithms showed that GSCAT presented low normalized bias for rain areas and rain volume [0.346 (PR) and 0.361 (S-Pol)], and GPROF showed rainfall distribution similar to that of the PR and S-Pol but with a bimodal distribution. Last, the five algorithms were evaluated during the TRMM-Large-Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) 1999 field campaign to verify the precipitation characteristics observed during the easterly and westerly Amazon wind flow regimes. The proposed algorithm presented a cumulative rainfall distribution similar to the observations during the easterly regime, but it underestimated for the westerly period for rainfall rates above 5 mm h Ϫ1. NESDIS 1 overestimated for both wind regimes but presented the best westerly representation. NESDIS 2 , GSCAT, and GPROF underestimated in both regimes, but GPROF was closer to the observations during the easterly flow.