A two-step methodology was developed to forecast tropospheric ozone (O3) concentration levels, k hours ahead (k = 1, 8, 12, 24), combining meteorological, air quality and industrial emissions data, across three air quality monitoring stations in Sines Portuguese region. Firstly, the best O3 concentration predictors have been identified through Classification and Regression Trees techniques; then Multilayer Perceptron models were adopted to forecast O3 levels for each monitoring site.The obtained generalization model performances are very good to classify in advance the expected class of O3 concentration level. Performance results vary from 70% of success to forecast O3 class above 70 μg/m3 24 h in advance up to 99% to predict the next hour in advance. These successful results are favorable to be implemented in a real-time tool for health and environmental advisories, allowing the forecast of air pollutants concentrations up to 24 h ahead, improving the local air quality management systems

Most of the recent studies and projections of precipitation patterns, based on records observed in the past and climate change scenarios for the Mediterranean basin, suggest a relatively slow decrease in rainfall amounts over the years but an increase in the frequency of extreme precipitation events. These are key factors in desertification processes and these will cause social and environmental impacts in the short term, mainly because changes in heavy rainfall events may have severe implications and impacts on soil erosion, resulting in increased risk of soil degradation.The main objective of the present work is to evaluate the spatial–temporal dynamics of extreme precipitation events in southern Portugal, using a direct sequential simulation algorithm (DSS models) in order to assess the relationships between spatial and temporal extreme rainfall patterns. Local probability density functions (pdfs) and spatial uncertainty are evaluated by a set of equiprobable simulated images of the chosen extreme precipitation indices.The used dataset in this work comprises a set of 105 station records of observed daily precipitation within the period 1961–2000. Two indices of extreme precipitation were selected: one representing the frequency of extremely heavy precipitation events (R30) and another characterizing the occurrence of dry events (RL10), both obtained from observed daily precipitation series.Results show that the spatial continuity of extreme precipitation events has increased in the last 40 years in southern Portugal. It also demonstrates a decrease in spatial variability, implying that extreme precipitation events tend to be more spatially homogeneous, which may have a severe impact on water resources, agriculture and soil erosion, particularly when associated with desertification risks. Copyright © 2009 Royal Meteorological Society

The topographic characteristics and spatial climatic diversity are significant in the South of continental Portugal where the rainfall regime is typically Mediterranean. Direct sequential cosimulation is proposed for mapping an extreme precipitation index in southern Portugal using elevation as auxiliary information. The analysed index (R5D) can be considered a flood indicator because it provides a measure of medium-term precipitation total. The methodology accounts for local data variability and incorporates space-time models that allow capturing long-term trends of extreme precipitation, and local changes in the relationship between elevation and extreme precipitation through time. Annual gridded datasets of the flood indicator are produced from 1940 to 1999 on 800 m×800 m grids by using the space-time relationship between elevation and the index. Uncertainty evaluations of the proposed scenarios are also produced for each year. The results indicate that the relationship between elevation and extreme precipitation varies locally and has decreased through time over the study region. In wetter years the flood indicator exhibits the highest values in mountainous regions of the South, while in drier years the spatial pattern of extreme precipitation has much less variability over the study region. The uncertainty of extreme precipitation estimates also varies in time and space, and in earlier decades is strongly dependent on the density of the monitoring stations network. The produced maps will be useful in regional and local studies related to climate change, desertification, land and water resources management, hydrological modelling, and flood mitigation planning.

The frequency and intensity of extreme hot events have increased worldwide, particularly over the past couple of decades. Europe has been affected by unprecedented mega heatwaves, namely the events that struck Western Europe in 2003 and Eastern Europe in 2010. The year 2018 was also reported as an unusually hot year, with record-breaking temperatures in many parts of Europe during spring and summer, associated with severe and unusual wildfires and significant crop losses in central and northern Europe. We show the ability of Land Surface Temperature (LST), retrieved from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard Meteosat Second Generation (MSG) to monitor heat extremes, using the 2018 European event as a showcase. The monitoring approach relies on monthly anomalies performed as departures from the median and the monthly number of hot days (NHD), both computed for satellite LST derived from MSG and MODIS, and for 2 m air temperature (T2m) from ERA5 reanalysis, using as threshold the 90th percentiles. Results show strong monthly LST anomalies during the spring and summer of 2018 extending over central and north Europe. Over a vast region in Central and Northern Europe, LST reached the last 15 years high record. Moreover, those outstanding warm LSTs persisted for more than four months. Results obtained using MODIS LST and ERA5 T2m show similar patterns, which, although slightly less intense, corroborate the exceptionality of the heat extremes observed over central and northern Europe during 2018. The spatial pattern of the number of monthly record high anomalies over the MSG observations period clearly depicts the regions in Northern and Central Europe affected by the complex phenomena that occurred in 2018, which resulted from the combined effect of an extreme heatwave in spring and summer with extensive dry conditions. Therefore, the results highlighted the suitability of MSG LST to evaluate and monitor heat extremes alone or combined with dry and bright conditions and prompts the potential of other climate data records from geostationary satellites to characterize these climate extremes that could become the norm in the near future over central and northern Europe.