On the relationship between convective environments and cold pools at the Amazon Tall Tower Observatory (ATTO)

Deep moist convection significantly disrupts the evolution of the planetary boundary layer (PBL) through the formation of convective cold pools (CCPs). Oliveira et al. (2026) investigated CCPs and their impacts on turbulence within the PBL using micrometeorological data collected at the Amazon Tall Tower Observatory (ATTO). To obtain a large CCP dataset, a detection algorithm originally developed to identify surface CCPs was adapted for application to micrometeorological data collected at multiple levels of the ATTO towers. Applying the algorithm to ATTO observations collected from August 2021 to December 2023 resulted in a dataset comprising 410 CCPs. The present study, which represents an extension of Oliveira et al. (2026), aims to investigate the relationship between CCP intensity and convective parameters associated with the atmospheric conditions preceding storms. These parameters were obtained from the fifth-generation reanalysis of the European Centre for Medium-Range Weather Forecasts (ERA5), considering atmospheric conditions approximately 1 h prior to the occurrence of each CCP event. Thermodynamic parameters exhibit a greater ability to discriminate the potential for generating downdrafts and cold pools, whereas kinematic parameters show negligible discriminatory ability among weak, moderate, and strong events. The predominance of thermodynamic over kinematic parameters persists across different seasons, with parameters associated with PBL depth and instability, as well as mid-level relative humidity, exhibiting the greatest discriminatory ability.