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The specific problems of determining and simulating the surface energy balance (SEB) and the mixing height (MH) over urban areas are examined. The SEB and MH are critical components of algorithms and numerical models for the urban boundary layer, though the constituent parts of the SEB and the MH are not routinely measured by national weather services. Parameterisations are thus needed in applications. In this investigation, several recently developed algorithms and models for estimating the SEB and MH were applied to new datasets and assessed. Results are discussed in terms of the need for spatial resolution and the parameters needed to describe the urban atmosphere. Limitations of models are identified and recommendations for further development and observations are given. Having identified gaps in knowledge, key findings from new urban experiments and numerical modelling for the SEB and MH are given. The diurnal cycle for the SEB is significantly different from rural conditions—urban heat storage is needed in urban parameterisations. The urban MH is increased over the rural MH, as shown by several numerical schemes and careful sodar analyses. This work has been carried out within the COST-715 Action “Meteorology applied to urban air pollution problems (1998–2004). COST 715 reached a consensus proposing representatively sited measurements of meteorological parameters and turbulent fluxes above roof-tops, and recognised that such data are needed to improve numerical models of the urban surface processes. [ABSTRACT FROM AUTHOR]

Abstract: We utilized a long-term data set of aerosol particle number size distributions (8–400nm) in the urban background air of Helsinki during 1998–2000. We analyzed the number concentrations of both ultra-fine particles (UFP diameter <100nm) and the fraction of fine particles (FP, diameter <2.5μm) larger than 100nm (accumulation mode), and we also investigated their dependencies on the relevant meteorological parameters. The meteorological parameters were obtained by a meteorological pre-processing model. Among the meteorological parameters considered in this study (wind speed and direction, temperature, atmospheric pressure, relative humidity, Monin–Obukhov length and mixing height), the ambient temperature and local wind conditions were found to be the most important factors that control the number concentrations of FP. We described the dependencies of FP number concentrations on meteorological variables by using an empirically based mathematical function that contains the ambient temperature, local wind speed and direction as independent variables. According to statistical analyses, the predicted number concentrations of accumulation mode particles follow this relationship more closely than those of UFP''s. This is mainly due to the origin and type of aerosol particles in the accumulation mode size range, being mainly regional and long-range transported. The main limitations of the mathematical function presented in this study are during new particle formation events, precipitation and long-range pollution episodes of aerosol particles. This study provides an attempt to predict the particle number concentrations of FP by utilizing a simple model that connects the relationship between the aerosol particle number concentrations and the relevant meteorological parameters. [Copyright &y& Elsevier]

Abstract: Forecasting models based on stepwise multiple linear regression (MLR) have been developed for Athens and Helsinki. The predictor variables were the hourly concentrations of pollutants (NO, NO2, NOx, CO, O3, PM2.5 and PM10) and the meteorological variables (ambient temperature, wind speed/direction, and relative humidity) and in case of Helsinki also Monin-Obukhov length and mixing height of the present day. The variables to be forecasted are the maximum hourly concentrations of PM10 and NOx, and the daily average PM10 concentrations of the next day. The meteorological pre-processing model MPP-FMI was used for computing the Monin-Obukhov length and the mixing height. The limitations of such statistical models include the persistence of both the meteorological and air quality situation; the model cannot account for rapid changes (on a temporal scale of hours or less than a day) that are commonly associated, e.g., with meteorological fronts, or episodes of a long-range transport origin. We have selected the input data for the model from one urban background and one urban traffic station both in Athens and Helsinki, in 2005. We have used various statistical evaluation parameters to analyze the performance of the models, and inter-compared the performance of the predictions for both cities. Forecasts from the MLR model were also compared to those from an Artificial Neural Network model (ANN) to investigate, if there are substantial gains that might justify the additional computational effort. The best predictor variables for both cities were the concentrations of NOx and PM10 during the evening hours as well as wind speed, and the Monin-Obukhov length. In Athens, the index of agreement (IA) for NOx ranged from 0.77 to 0.84 and from 0.69 to 0.72, in the warm and cold periods of the year. In Helsinki, the corresponding values of IA ranged from 0.32 to 0.82 and from 0.67 to 0.86 for the warm and cold periods. In case of Helsinki the model accuracy was expectedly better on the average, when Monin-Obukhov length and mixing height were included as predictor variables. The models provide better forecasts of the daily average concentration, compared with the maximum hourly concentration for PM10. The results derived by the ANN model where only slightly better than the ones derived by the MLR methodology. The results therefore suggest that the MLR methodology is a useful and fairly accurate tool for regulatory purposes. [Copyright &y& Elsevier]

Abstract Major nuclear accidents are typically low-probability–high-consequence events. This paper focuses on weather and sea-level events that might affect the safety of nuclear power plants (NPPs). Flooding risks at Finnish NPP sites have been re-evaluated based on investigations of long-term changes and short-term variations in sea level, which has led to improvements in the protection against extremely high sea water level. Climate change projections indicated that 1000-year return levels of high enthalpy of the air would increase by 13–27% by 2100. However, no clear future trends were projected for the all-year-round probabilities of freezing rain at the NPP sites. Simulations of the spread of hypothetical radioactive releases during two sea-breeze cases, using weather input data at a remarkably high spatial resolution, demonstrated the impacts of coastal meteorological phenomena on atmospheric dispersion. Overviews of various single and combined weather and marine events that could possibly affect normal NPP operation highlighted the need to broaden the scope of future research. Special attention was focused on various aspects of uncertainty. According to the results, a more extreme weather or marine event corresponded to a larger uncertainty in its probability of occurrence in the changing climate. Highlights • Safe production of nuclear energy requires appraisal of weather and sea-level risks. • Changing climate affects the occurrence of extreme weather and sea-level events. • Land uplift will moderate the increase of flooding risks on the Finnish coast. • Progress in data and methods reveals new potential hazards such as meteotsunamis. • High-resolution meteorological data is vital as input for dispersion modelling. [ABSTRACT FROM AUTHOR]