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Abstract: This paper describes different numerical models of ocean circulation the output of which can be applied to study patterns and pathways of drifting marine debris. The paper focuses on model output that is readily available rather than on numerical models that could be configured and run locally. These include operational models from the US Navy (the Navy Layered Ocean Model (NLOM), Coastal Ocean Model (NCOM), and Hybrid Coordinate Ocean Model (HYCOM)), data assimilating reanalysis models (the Simple Ocean Data Assimilation (SODA), the Global Ocean Data Assimilation Experiment (GODAE) models), and the European Center for Medium-Range Weather Forecasts (ECMWF) ocean reanalysis (Ocean Reanalysis System, ECMWF/ORA-S3). The paper describes the underlying physics in each model system, limitations, and where to obtain the model output. [Copyright &y& Elsevier]

This paper analyses the dependency of regional air pollution in Europe on the atmospheric circulation as represented by a set of 8 weather regimes (WRs). For this purpose, daily average PM 2.5 concentrations and daily maximum concentrations of O 3 , NO 2 and CO obtained from an atmospheric composition reanalysis have been used. The results indicate that the pollutant concentrations change substantially with the occurrence of each WR according to the associated circulation patterns. In the extended winter, the responses of PM 2.5 , NO 2 and CO are quite similar, with zonal circulation regimes leading to better air quality than anticyclonic regimes linked to steady flows over Europe. On the other hand, the spatial patterns of O 3 anomalies under most WRs resemble those of temperature in the extended summer as temperature enhancements favour the photochemical production of O 3. The WR characterized by anticyclonic anomalies over Scandinavia leads to the highest concentrations at continental scale and is concurrent with an increased probability of exceeding the poor air quality limits of winter PM 2.5 and summer O 3 in most European countries. It is also found that a multiple linear regression model on the monthly frequency of WRs can explain a considerable fraction of the intermonthly variability of concentration anomalies over large parts of Europe regardless of the pollutant, with R2 values around 0.6–0.8 over large regions. Overall, the application of these models to sub-seasonal weather forecasts does not seem to bring major improvements to the prediction of month-ahead concentration anomalies as compared to the climatology. The poor skill of sub-seasonal forecasts in predicting the WRs for lead times beyond 15 days limits the forecast capability. However, these results open the possibility of extending current air quality forecast to scales of 5–10 days at a low cost in a near future. [Display omitted] • European-wide study of the modulation of air quality by weather regimes (WRs). • WRs explain 60–80% of the monthly variability in air pollution over large regions. • The poorest air quality in most of Europe is associated with Scandinavian blocking. • Evaluation of WRs as a tool to predict month-ahead air pollution anomalies. [ABSTRACT FROM AUTHOR]

Abstract: Forecasting hailstorms is an important challenge. Improving these forecasts is of vital importance to reduce the risks that this phenomenon causes to the environment and the population. This paper presents a methodology for the characterization of hailstorms in different scales by means of: the definition of the synoptic environment favorable to convection, the establishment of mesoscale configurations on hailstorm days, and an analysis of prediction parameters for the presence of hail. Simulations with the WRF model were done for 100 study days in which hailstorms were produced in the Middle Ebro Valley. The synoptic environment was defined from the average geopotential and temperature fields at 500hPa. Using multivariate techniques of the Principal Component Analysis in T-Mode and a Cluster Analysis, four mesoscale configurations were defined for the fields of convective instability, water vapor flux divergence and wind flow and humidity at low layers. Finally, the study included several hail forecast parameters: CAPE, Storm Relative Helicity between 0 and 3km (SRH0–3), Energy–Helicity Index (EHI) and Showalter Index (SI); making a comparison between hail/hail-free grid points, which allowed us to determine their values and thresholds in order to discern the presence of hail within convective precipitation areas. The analysis, along with these three basic ingredients, provides a very useful tool that allows for a good approximation for predicting storms with hail in the study area. [Copyright &y& Elsevier]

Abstract: A European chemical weather forecasting portal is presented in this paper that has been developed within the COST (European Cooperation in Science and Technology) ES0602 action, “Towards a European Network on Chemical Weather Forecasting and Information Systems”. The portal includes an access to a substantial number (currently 21) of available chemical weather forecasting systems and their numerical forecasts; these cover in total more than 30 regions in Europe. This portal can be used, e.g., to find out, which services are available for a specific domain, for specific source categories or for specific pollutants. The portal currently expands its functionalities to allow for a harmonized presentation and inter-comparison of the various available forecasts, as well as for the computation of model ensemble predictions. It provides functions for obtaining relevant supplementary information, e.g., using the Model Documentation System of the European Environmental Agency. The new portal is an open access system, through which chemical weather forecasts can be added to the system, and the predictions can be accessed, analysed and inter-compared. Such a single point of reference for the European chemical weather forecasting information has previously not been in operation. We present the characteristics of the new portal, and discuss how this activity complements the GEMS and PROMOTE air quality forecasting portals. [Copyright &y& Elsevier]

The number of large-scale solar thermal installations has increased rapidly in Europe in recent years, with 70 % of these systems operating with flat-plate solar collectors. Since these systems cannot be easily switched on and off but directly depend on the solar radiation, they have to be combined with other technologies or integrated in large energy systems. In order to most efficiently integrate and operate solar systems, it is of great importance to consider their expected energy yield to better schedule heat production, storage and distribution. To do so the availability of accurate forecasting methods for the future solar energy yield are essential. Currently available forecasting methods do not meet three important practical requirements: simple implementation, automatic adaption to seasonal changes and wide applicability. For these reasons, a simple and adaptive forecasting method is presented in this paper, which allows to accurately forecast the solar heat production of flat-plate collector systems considering weather forecasts. The method is based on a modified collector efficiency model where the parameters are continuously redetermined to specifically consider the influence of the time of the day. In order to show the wide applicability the method is extensively tested with measurement data of various flat-plate collector systems covering different applications (below 200 ° Celsius), sizes and orientations. The results show that the method can forecast the solar yield very accurately with a Mean Absolute Range Normalized Error (MARNE) of about 5 % using real weather forecasts as inputs and outperforms common forecasting methods by being nearly twice as accurate. • Self-learning approach with only very few parameters to be determined. • Automatically considers seasonal changes and pollution of collector fields. • Simple regarding its mathematical structure and straightforward to implement. • Experimental long-term validation with data from various collector systems. • Superior to state-of-the-art forecasting methods regarding accuracy. [ABSTRACT FROM AUTHOR]