The height of the nocturnal boundary layer (hn) is a fundamental parameter for weather and climate prediction. However, because turbulent processes weaken at night, estimating hn remains challenging. In addition, our understanding of its variability is limited, especially due to the predominant use of indirect methods that do not always accurately reflect the physical definition of the boundary layer. In this study we used micrometeorological measurements collected at the Amazon Tall Tower Observatory, in central Amazon. These measurements enable the study of turbulent sensible heat flux (H) profiles from the canopy top up to 300 m above ground, from which hn can be defined. Our analysis focused on the seasonal differences between dry and wet periods for a La Niña year and an El Niño year. Also, we explore how variations in hn affect the vertical distribution of CO and CH4 concentrations. The results revealed significant variations, such as: largest values of hn were observed during the wet season of a year marked by the La Niña phenomenon (∼ 270 m ± 40 m), while smallest values of hn occurred in the dry season associated with El Niño (∼100 m ± 27 m). It was also observed that hn can act as a “barrier” to the entry or exit of air masses with high concentrations of CO and CH4. This study provides important insights into the variability of hn above the Amazon forest, with implications for improving parameterizations in atmospheric models.

This study evaluates the performance of eleven Planetary Boundary Layer (PBL) schemes within the Weather Research and Forecasting (WRF) model over the Central Amazon Basin, focusing on contrasting wet and dry season conditions observed during the GoAmazon2014/5 campaign. High-resolution (1 km) simulations were conducted for representative periods in each season and validated against in situ observations. Model performance was assessed using multiple statistical metrics with the explicit separation of daytime convective and nighttime stable PBL regimes. Results reveal substantial variability among PBL schemes, strongly modulated by the season and diurnal cycle. Overall performance was higher during the wet period, whereas dry period simulations exhibited larger uncertainties, particularly under nocturnal conditions. The Shin–Hong (SH) PBL scheme had the best skill on average to reproduce the observed PBL height (PBLH) during the wet period, while the University of Washington (UW) PBL scheme was the best during the dry period. The Mellor–Yamada–Janjic (MYJ) PBL scheme had the best skill for daytime PBLH in both periods. Spatial analysis demonstrated how PBL schemes impact the PBLH distribution over the Central Amazon Basin, revealing a river-influenced pattern. These findings highlight the strong sensitivity of the Amazon PBL depth to PBL schemes and underscore the importance of appropriate PBL parameterizations and the vertical resolution for tropical applications.

There are many studies on moisture transport and surface fluxes in the Amazon, but only a few simultaneously connect remote oceanic anomalies with local-scale responses in surface processes. This study investigates how large-scale forcings (e.g. anomalies in the Tropical North Atlantic (TNA) and the Equatorial Pacific (associated with ENSO)) interact with surface processes to modulate energy partitioning (EP) and convection in Central Amazonia. Data from in situ (ATTO), reanalysis (ERA5), and satellite (SMAP L4) were used to analyze variations in temperature, humidity, vapor pressure deficit, and energy fluxes on rainy (≥1 mm) and dry days (<1 mm) across two wet seasons (January–March 2019 and 2020) and two dry seasons (July–September 2019 and 2020). During the rainy season, while a late-coupling El Niño weakly disrupted large-scale forcing in JFM-2019, the positive TNA anomaly displaced the convective band northward in JFM-2020, reducing the zonal circulation and vertical motion organization. This hindered regional moisture convergence (+120.7 vs. +152.7 kg m⁻¹ s⁻¹ in JFM 2020 and 2019, respectively) and shifting the dominant control of convective organization toward surface processes, as reflected in the hourly distribution of precipitation and EP. In contrast, La Niña conditions during JAS-2020 did not result in convective intensification despite their typical association with a strengthening Walker Circulation ascending branch: moisture convergence was greater in JAS-2019 (neutral ENSO) (+56.8 kg m⁻¹ s⁻¹) than in JAS-2020 (+39.6 kg m⁻¹ s⁻¹), but this was not the determining factor controlling rainfall that local surface processes, such as soil moisture control over EP, governed convective organization during the dry season.

Low-Level Jets (LLJs) influence the dynamics of the Nocturnal Boundary Layer (NBL) by enhancing mechanical turbulence below the jet nose through vertical wind shear. In this study, we have evaluated whether the jet nose height (hNjet) can serve as a reliable proxy for estimating the NBL height (hN), defined as the level where turbulence diminishes to negligible magnitudes (hNflux). We have used unique high-resolution measurements from sonic anemometers installed at 11 heights above the canopy at the Amazon Tall Tower Observatory (ATTO) in the central Amazon during 2022-2023. Nights with LLJ were selected based on the presence of a wind speed maximum (jet nose), along with a decrease in speed for at least two levels above and below. Given that both the height and intensity of LLJs vary over time, hNjet and hNf lux were determined from mean profiles grouped by jet nose heights: 81-100 m, 151-172 m, and 223-247 m. We have also considered the predominant wind directions associated with LLJs at the ATTO region: N-NE and S-SE. Our results show that, for the same jet nose height, N-NE jets are generally weaker and occur under more turbulent and less stable conditions, with hNjet being comparable to hNf lux. In contrast, S-SE jets are stronger and associated with enhanced stable thermal stratification, which likely suppresses turbulence and results in hNjet values higher than hNf lux. These differences are likely influenced by radiative cooling above the canopy, as S-SE jets occurred under larger net radiative loss. We conclude that the LLJ nose identified from wind speed profiles does not provide a good approximation of the NBL height under strongly stratified conditions, when turbulence above the canopy is significantly suppressed.

Indigenous Lands are critical for protecting Indigenous Peoples and the Amazon. Yet, they are increasingly exposed to socio-environmental frontiers, bringing violence, illegal resource extraction and deforestation. Urgent action is required to prevent irreversible harm to both people and forests, particularly in Brazil’s Vale do Javari Indigenous Land.

The planetary boundary layer (PBL) mediates exchanges of heat, moisture, momentum, and chemical constituents between the surface and the free atmosphere. In the Amazon biome, where land use and land cover (LULC) changes have intensified over recent decades, changes in surface energy partitioning may alter convective boundary layer dynamics. This study provides a spatiotemporal analysis of convective boundary layer height (CBLH) over the Brazilian Amazon using ERA5 data and evaluates its relationship with LULC changes on the CBLH structure, as well as changes in sensible heat flux (H) and latent heat flux (LE) patterns over four decades, as depicted by the Bowen ratio (β). Results show a pronounced increase in CBLH, with values exceeding 1800 m in parts of the southern Amazon and marked increases in the southern and northeastern regions, particularly during the last two decades. Seasonal trend analysis indicates a weak wet-season CBLH trend of 0.72 m yr−1 and a stronger dry-season trend of 5.01 m yr−1. In contrast to the wet season, the dry-season increase is strongly associated with LULC changes and with a shift in energy partitioning toward greater sensible heat flux. These results indicate that land-surface changes are closely linked to Amazonian lower-atmosphere dynamics, with potential implications for regional climate, convection, and the hydrological cycle. We therefore interpret these patterns as spatial associations rather than definitive causal attribution, because no formal attribution model separating LULC forcing from climatic variability was applied.

The ocean–atmosphere turbulent heat exchange plays a critical role in the energy and moisture budgets of the Tropical Atlantic Ocean (TAO) and in weather and climate forecasts. However, its estimation strongly depends on the choice of bulk parameterization, as direct in situ measurements are sparse. This study evaluates sensible (Hs) and latent (Hl) heat fluxes derived from three bulk parameterization schemes used operationally in models at the Brazilian Center for Weather Forecast and Climate Studies (CPTEC) of the National Institute for Space Research (INPE), Brazil: the Brazilian Atmospheric Model (BAM), the Modular Ocean Model version 6 (MOM6), and the Weather Research and Forecasting (WRF) model. Using daily in situ observations from seven Prediction and Research Moored Array in the Tropical Atlantic (PIRATA) buoys across the TAO during 1997–2023, we computed monthly mean fluxes and compared them against the Coupled Ocean–atmosphere Response Experiment (COARE) algorithm version 3.0b (COARE 3.0b) reference. COARE version 3.6 (COARE 3.6) and European Centre for Medium-Range Weather Forecast (ECMWF) Reanalysis 5th generation (ERA5) data were included as additional benchmarks. All offline schemes were forced with identical buoy data, isolating differences in internal physical assumptions. Hl is approximately one order of magnitude larger than Hs across all sites, and inter-scheme differences are substantially larger for Hl (±50 W∙m−2) than for Hs (±5 W∙m−2). All schemes reproduce the seasonal cycle linked to the Intertropical Convergence Zone (ITCZ) migration and trade-wind variability, with correlations generally exceeding 0.8 (p < 0.001) for most buoys. However, systematic magnitude biases remain. The Coordinated Ocean Research Experiments (CORE) bulk formulation implemented in MOM6 (MOM6-CORE) shows high temporal correlation (often r ≈ 1.0) but a persistent negative bias for both Hs and Hl (e.g., B1 Hl bias = −24.0 W∙m−2), indicating weaker turbulent exchange relative to COARE 3.0b. BAM overestimates Hs (by 1–3 W∙m−2) and underestimates Hl at most northern and southern sites, while the parametrization of the Yonsei University (YSU) implemented in the WRF model (WRF-YSU) amplifies Hs variability intermittently, particularly at the equator (B4). As expected, COARE 3.6 remains the closest to the reference (differences < 1 W∙m−2 for Hs and <7 W∙m−2 for Hl; r ≈ 0.99). ERA5 captures temporal variability well (r ≈ 0.7–0.9) but systematically overestimates Hl (positive bias up to +47.6 W∙m−2 at B7), implying stronger evaporative cooling. Buoy-specific regimes modulate skill. The choice of bulk formulation thus remains a first-order source of uncertainty in turbulent heat flux estimates over the TAO, with direct implications for mixed-layer heat budgets, SST evolution, and coupled ocean–atmosphere variability. MOM6-CORE provides the most consistent performance relative to the COARE reference and emerges as the most robust option for operational applications at CPTEC/INPE. The findings also provide guidance for improving the representation of ocean–atmosphere turbulent exchanges in MONAN (Model for Ocean-Land-Atmosphere Prediction), the new Brazilian Earth System Model under development for weather and climate prediction.

The dynamics of moisture transport in the Amazon are key to the South American water cycle. Convection-permitting regional climate models (CPRCMs) offer a valuable tool to better understand these processes. This study evaluates integrated water vapor transport (IVT) and precipitation in the Amazon Basin using a CPRCM simulation during austral summer (DJF) and winter (JJA). Simulations at 4.5 km resolution cover most of South America and are validated using ERA5 reanalysis. The CPRCM-ERA experiment (1998–2007) was driven by ERA-Interim data and downscaled to 25 km with an RCM. During DJF, precipitation was overestimated in the central Amazon (+ 1.0 to + 2.0 mm/day) and underestimated in the northeastern and western Amazon (− 2.5 mm/day). The CPRCM-ERA simulation showed a weaker low-level jet (LLJ) and trade winds. IVT analysis indicated a negative bias in moisture transport over the LLJ region, more pronounced in CPRCM-ERA. This model underestimated moisture inflow by 6.1

Understanding cloudiness over hydropower reservoirs is critical for deploying floating photovoltaic (FPV) systems. Using a multi-instrumental approach – including an All-Sky Imager, tethered balloon profiles, IRGASON, GOES-16 and ERA5 datasets, and micrometeorological masts – we evaluated lake-breeze (LB) impacts on cloud cover over Brazil’s Furnas Reservoir. A high-pressure system enhanced LB-driven cloud suppression, with a late onset (10:00 LT) and subsidence persisting until 17:00 LT. The on-reservoir mast recorded 5.3–12.7 Furnas hydropower plant reservoir, southeast Brazil, is essential for both energy security and multiple economic activities. The lake-breeze modulates cloudiness over extensive aquatic systems. Solar irradiance was higher over the reservoir, especially during dry-season afternoons (5.3–12.7

Studies on Convective Boundary Layer Height (CBLH) variability within the Amazon region are scarce. An alternative to obtaining CBLH values is to use reanalysis data, such as ERA5. This study used in situ temperature, humidity, and CO2 vertical profiles collected from 2010 to 2018 by small aircraft carried out within the scope of the CARBAM project (Long-Term Study of the Amazon Carbon Balance) in Amazonia. The flights were performed at 5 different locations in the Amazon (named SAN, ALF, RBA, TEF, and TAB), with significant variations in land use and land cover. Also, they represent different regional atmospheric processes associated. From the profiles, it was possible to estimate the CBLH values and compare/validate them with data provided by the ERA5. The results showed that ERA5 underestimates CBLH values by 5–12

This study investigates the use of a Random Forest (RF), an artificial intelligence (AI) model, to estimate the planetary boundary layer height (PBLH) over Central Amazonia from climatic elements data collected during the GoAmazon experiment, held in 2014 and 2015, as it is a key metric for air quality, weather forecasting, and climate modeling. The novelty of this study lies in estimating PBLH using only surface-based meteorological observations. This approach is validated against remote sensing measurements (e.g., LIDAR, ceilometer, and wind profilers), which are seldom available in the Amazon region. The dataset includes various meteorological features, though substantial missing data for the latent heat flux (LE) and net radiation (Rn) measurements posed challenges. We addressed these gaps through different data-cleaning strategies, such as feature exclusion, row removal, and imputation techniques, assessing their impact on model performance using the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and r2 metrics. The best-performing strategy achieved an RMSE of 375.9 m. In addition to the RF model, we benchmarked its performance against Linear Regression, Support Vector Regression, LightGBM, XGBoost, and a Deep Neural Network. While all models showed moderate correlation with observed PBLH, the RF model outperformed all others with statistically significant differences confirmed by paired t-tests. SHAP (SHapley Additive exPlanations) values were used to enhance model interpretability, revealing hour of the day, air temperature, and relative humidity as the most influential predictors for PBLH, underscoring their critical role in atmospheric dynamics in Central Amazonia. Despite these optimizations, the model underestimates the PBLH values—by an average of 197 m, particularly in the spring and early summer austral seasons when atmospheric conditions are more variable. These findings emphasize the importance of robust data preprocessing and higtextight the potential of ML models for improving PBLH estimation in data-scarce tropical environments.

The investigation of atmospheric gases in the central Amazon, with emphasis on tropospheric ozone (O3), is fundamental to understanding impacts of climate change and anthropogenic activities. While previous studies associated downdrafts with near-surface O3-enhancement, cases of O3-decrease are still less explored. This study was conducted during the field mission from GoAmazon 2014/15 project. Experimental data and vertical O3 profiles from ERA5 reanalysis were used. 73 downdraft events were identified, of which around 51% were associated with near-surface O3-enhancement and the rest with decrease or minor variations. Numerical simulations were performed using the WRF-Chem model to investigate the three-dimensional dynamics of O3 during downdraft events. Two representative cases in O3 concentrations were selected to assess the model's performance. The study revealed that the variability of near-surface O3 is driven by the O3 concentration in the middle troposphere as well as the wind direction patterns that precede the downdrafts. Our analysis shows that downdrafts carry air masses that have higher or lower concentrations of O3 than those near the surface. It was observed that wind direction, O3 concentration in the middle troposphere, and O3 concentration near the surface are identified as key factors that define whether the occurrence of a downdraft will result in an increase or decrease in surface O3. Enhancement events were associated with an increase of 11 ppbv, while decrease events showed reductions of 10 ppbv. These results may contribute to better parameterizations of some chemical processes in atmospheric models.

All-sky images (ASI) are widely used for sky monitoring, particularly in solar energy generation applications. Alignment issues and interferences demand a detection process of problematic images. With high sampling frequencies (1-2 images per minute), automating this process is crucial for managing large datasets and enabling integration into automatic systems, which is the objective of this work. For this purpose, a robust ensemble model, using the ApproxHull and Radial Basis Function (RBF) neural networks combined with Multi Objective Genetic Algorithms (MOGA) tools, was developed to compute the cloud cover fraction of each image. By computing the deviation between this result and the one obtained by the equipment, and by assessing if it lies within prediction bounds obtained in the design phase, an automatic method for detecting anomalies in All-sky images was obtained. ASI data collected during the Green Ocean (GoAmazon) Experiment 2014/5 was employed. The proposed approach obtained a Probability of Interval Coverage (PICP) similar to the user-specified level of confidence for several sets within what was classified as a “good” dataset, while being able to detect anomalies found within a “bad” dataset.

We investigate the components of radiation and energy balance and heat storage flux in an urban area of Central Amazonia, during the wet and dry seasons of 2022. Detailed radiation and turbulent energy fluxes measurements were conducted using a 30-meter micrometeorological tower. The analyses include an assessment of the energy balance closure, incorporating the urban canopy heat storage term. The main findings were: (i) A footprint analysis showed that during the wet season, the primary energy flux sources were from the impermeable surfaces, while in the dry season, in addition to impermeable surfaces, green areas also influenced the fluxes; (ii) Incoming shortwave radiation was significantly higher during the dry season; (iii) Albedo was higher in dry season compared to the wet season; (iv) Latent heat flux showed low sensitivity to seasonal variability, compared to sensible heat flux; (v) Energy balance closure significantly improved with the inclusion of urban canopy heat storage and soil heat flux, highlighting their critical roles in reducing energy imbalances. The measurements presented in this study are the first Eddy Covariance measurements for an urban region of the Amazon. These results are important for urban climate modeling in tropical regions, providing insights into the impacts of urbanization in the Amazon region.

The nocturnal boundary layer height (hN) was investigated using one year of data (2022) collected sonic anemometers installed at 11 heights, above the canopy top on the towers of the Amazon Tall Tower Observatory (ATTO) in Central Amazon. Unlike previous assessments relying on indirect methodologies, in present study hN was directly estimated from measurements of turbulent fluxes of momentum and kinematic sensible heat. Our findings highlighted the dynamic effect of forest topography: under northeast winds, associated with lower roughness, hN varied between 81 m during very stable stratification and 172 neutral conditions. Conversely, under southeast winds, where roughness is higher, hN ranged between 81 223 m. These estimates reveal the significant control exerted by atmospheric stability and topography on hN variability. Interestingly, under neutral and weakly stable stratifications our finds align with the theoretical parameterization proposed in previous works. However, discrepancies emerged in very stable stratification when the boundary layer structure is influenced by topography.

We analyzed the planetary boundary layer (PBL) characteristics in Warsaw, Poland for a day of summer, autumn, winter, and spring of 2021 by integrating and comparing measured and simulated data. Using remote sensing lidar sensor data, the PBLH was calculated using wavelet covariance transform (WCT) and the gradient method (GM). Also, simulations of turbulent fluxes were performed utilizing the large eddy simulation (LES) from the Parallel Large Eddy Simulation Model (PALM) to better understand how turbulence and convection behave across different seasons in Warsaw. The PBLH diurnal cycles showed pronounced changes in their vertical structure as a function of the season: the winter heights were shallow (~0.7 km), while summer heights were deeper (~1.7 km). The spring and autumn presented transient characteristics of PBLH around 1.0 km. This study is crucial for enhancing urban air quality and climate modeling. The PBLH simulations from PALM showed agreement with the measured data, with an underestimation of approximately 10% in both methods. Through PALM, it was possible to observe that summer exhibited increased convection, enhanced mixing efficiency, and a deeper boundary layer compared to other seasons throughout the daily cycle. Winter has a lower sensible heat flux and little convection throughout the day. Spring and autumn showed intermediate characteristics. In this way, the effectiveness of the applicability of the PALM model to obtain flows within the PBL and their heights is highlighted, because correlations ranged from strong to very strong (r ≥ 0.70).