LAUSR

Abstract A circulation classification technique based on daily sea level pressure fields was applied to classify homogeneous synoptic types in the period 2001–2019 for the Iberian Peninsula (IP). The main synoptic meteorological patterns (SMP) were thus discriminated and then validated by the analysis of meteorological variables and atmospheric stability parameters registered in the Madrid air basin in the year 2015. Then, their utility to characterize atmospheric processes like air pollution episodes or new particle formation in the study area was evaluated. Specific SMP clearly influenced wind circulations and turbulent processes at different spatial scales. At regional scale they contributed to discriminate among pollution episodes giving rise to exceedances of air quality standards for particulate matter, nitrogen dioxide and ozone at monitoring stations. For instance, two of the resulting SMP, characterized by the presence of intense high pressure systems close to the IP and strong atmospheric stability in winter and autumn, produced slightly different urban high-pollution episodes in this region. The highest mean levels of nitrogen dioxide, PM10 and PM2.5 (particles lower than 10 and 2.5 μm, respectively) and of ultrafine particles number concentration registered at different urban traffic, urban background and suburban stations, happened during these specific SMP. Likewise, most new particle formation events took place during two other SMP, which mainly occurred in spring and summer. One of them was characterized by high values of solar irradiance and surface temperature, which favour nucleation processes. The highest mean levels of ozone were also registered at the urban and rural stations during this SMP. The other one represented an unusual meteorological scenario in the IP compared to the others obtained in the study, with high pressures displaced northwest producing relatively high values of surface wind speed. This fact reduced the condensation sink and favoured new particle formation in the midday period. In short, this methodology that considers a large amount of interconnected variables can be very useful for characterizing atmospheric situations that have a strong influence on atmospheric pollution processes in other regions.