The direct relationship between aerosols and clouds strongly influences the effects of clouds on the global climate. Aerosol particles act as cloud condensation nuclei (CCN) and ice nuclei (IN), affecting cloud formation, microphysics, and precipitation, as well as increasing the reflection of solar radiation at the cloud tops. Processes such as gas-to-particle conversion and new particle formation (NPF) control aerosol properties that, together with meteorological conditions, regulate cloud droplet nucleation through Köhler theory and related effects. The indirect aerosol effects described by Twomey and Albrecht demonstrate how changes in aerosols impact droplet number, cloud lifetime, and precipitation efficiency. Cloud microphysical processes, including droplet growth, collision-coalescence, and solid-phase mechanisms such as riming, vapor diffusion, and aggregation, shape precipitation development in warm, cold, and mixed-phase clouds. Ice nucleation remains a significant uncertainty due to the diversity of aerosol types and nucleation modes. This work synthesizes these physical interactions to better understand how the chemical and physical properties of aerosols influence cloud and precipitation processes, supporting improvements in weather and climate prediction models despite numerical challenges arising from the complexity of aerosol–cloud interactions.

Reanalysis datasets provide gridded, high-frequency estimates of atmospheric variables that are essential for studying weather and climate, particularly in regions with sparse observational networks. Despite their widespread use, the quality of reanalysis products remains insufficiently validated in tropical regions, particularly in areas with complex terrain. In this study, we evaluate the performance of surface-level temperature and atmospheric pressure fields from ERA5 and ERA5-Land in the state of Alagoas, northeastern Brazil. The analysis is based on a 12-year comparison (2008–2019) with observational data from the National Institute of Meteorology (INMET). Prior to validation, altitude corrections were applied to minimize elevation-induced biases in the reanalysis fields. Performance was assessed using statistical metrics. Both reanalyses showed strong agreement with observations, with average correlations exceeding 0.91 for temperature and pressure. ERA5 temperature biases ranged from −0.2 °C to 0.3 °C, and those for ERA5-Land from −0.6 °C to −0.3 °C, with RMSE around 1.6 °C. Pressure biases were initially larger (−20 hPa to +6 hPa in ERA5), but were reduced to below 0.5 hPa at key reference stations after correction. Diurnal and seasonal cycle analyses confirmed the datasets’ ability to reproduce temporal variability, though both reanalyses tended to overestimate minimum temperatures and underestimate maximum temperatures. Further investigation is needed to identify the origin of anomalous temperature jumps in ERA5’s diurnal cycle, which seem unrelated to the assimilation cycles. Overall, the results highlight the robust performance of ERA5 and ERA5-Land in representing surface atmospheric conditions in tropical coastal regions, while also emphasizing the continued need for regional validation and preprocessing before application in high-resolution or short-term studies.