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[1] A network of high-frequency (HF) radars was installed in the northern Adriatic in the second half of 2007, aimed to measure surface currents in the framework of the North Adriatic Surface Current Mapping (NASCUM) project. This study includes a detailed analysis of current measurements from February to August 2008, a period in which three radars were simultaneously operational. Current patterns and temporal evolutions of different physical processes were extracted by using self-organizing map (SOM) analysis. The analysis focused on subtidal frequency band and extracted 12 different circulation patterns on a 4 × 3 rectangular SOM grid. The SOM was also applied on a joint data set that included contemporaneous surface wind data obtained from the operational hydrostatic mesoscale meteorological model ALADIN/HR. The strongest currents were recorded during energetic bora episodes, being recognized by several current patterns and having the characteristic downwind flow with magnitudes exceeding 35 cm/s at some grid points. Another characteristic wind, the sirocco, was represented by three current patterns, while the remaining current structures were attributed to weak winds and the residual thermohaline circulation. A strong resemblance has been found between SOM patterns extracted from HF radar data only and from combined HF radar and wind data sets, revealing the predominant wind influence to the surface circulation structures and their temporal changes in the northern Adriatic. These results show the SOM analysis being a valuable tool for extracting characteristic surface current patterns and forcing functions.Citation: Mihanović, H., S. Cosoli, I. Vilibić, D. Ivanković, V. Dadić, and M. Gačić (2011), Surface current patterns in the northern Adriatic extracted from high-frequency radar data using self-organizing map analysis,

An ocean surface currents forecasting system, based on a Self-Organizing Maps (SOM) neural network algorithm, high-frequency (HF) ocean radar measurements and numerical weather prediction (NWP) products, has been developed for a coastal area of the northern Adriatic and compared with operational ROMS-derived surface currents. The two systems differ significantly in architecture and algorithms, being based on either unsupervised learning techniques or ocean physics. To compare performance of the two methods, their forecasting skills were tested on independent datasets. The SOM-based forecasting system has a slightly better forecasting skill, especially during strong wind conditions, with potential for further improvement when data sets of higher quality and longer duration are used for training.

Abstract. In this evaluation study, the coupled atmosphere–ocean Adriatic Sea and Coast (AdriSC) climate model, which was implemented to carry out 31-year evaluation and climate projection simulations in the Adriatic and northern Ionian seas, is briefly presented. The kilometre-scale AdriSC atmospheric results, derived with the Weather Research and Forecasting (WRF) 3 km model for the 1987–2017 period, are then thoroughly compared to a comprehensive publicly and freely available observational dataset. The evaluation shows that overall, except for the summer surface temperatures, which are systematically underestimated, the AdriSC WRF 3 km model has a far better capacity to reproduce surface climate variables (and particularly the rain) than the WRF regional climate models at 0.11∘ resolution. In addition, several spurious data have been found in both gridded products and in situ measurements, which thus should be used with care in the Adriatic region for climate studies at local and regional scales. Long-term simulations with the AdriSC climate model, which couples the WRF 3 km model with a 1 km ocean model, might thus be a new avenue to substantially improve the reproduction, at the climate scale, of the Adriatic Sea dynamics driving the Eastern Mediterranean thermohaline circulation. As such it may also provide new standards for climate studies of orographically developed coastal regions in general.

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Objectives:The aim of this study was to identify guidelines and assessment tools used by health technology agencies for quality assurance of registries and investigate the current use of registry data by HTA organizations worldwide.Methods:As part of a European Network for Health Technology Assessment Joint Action work package, we undertook a literature search and sent a questionnaire to all partner organizations on the work package and all organizations listed in the International Society for Pharmaco-economics and Outcomes Research directory.Results:We identified thirteen relevant documents relating to quality assurance of registries. We received fifty-five responses from organizations representing twenty-one different countries, a response rate of 40.5 percent (43/110). Many agencies, particularly in Europe, are already drawing on a range of registries to provide data for their HTA. Less than half, however, use criteria or standards to assess the quality of registry data. Nearly all criteria or standards in use have been internally defined by organizations rather than referring to those produced by an external body. A comparison of internal and external standards identified consistency in several quality dimensions, which can be used as a starting point for the development of a standardized tool.Conclusion:The use of registry data is more prevalent than expected, strengthening the need for a standardized registry quality assessment tool. A user-friendly tool developed in conjunction with stakeholders will support the consistent application of approved quality standards, and reassure critics who have traditionally considered registry data to be unreliable.

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Predictive models of bathing water quality are a useful support to traditional monitoring and provide timely and adequate information for the protection of public health. When developing models, it is critical to select an appropriate model type and appropriate metrics to reduce errors so that the predicted outcome is reliable. It is usually necessary to conduct intensive sampling to collect a sufficient amount of data. This paper presents the process of developing a predictive model in Kaštela Bay (Adriatic Sea) using only data from regular (official) bathing water quality monitoring collected during five bathing seasons. The predictive modelling process, which included data preprocessing, model training, and model tuning, showed no silver bullet model and selected two model types that met the specified requirements: a neural network (ANN) for Escherichia coli and a random forest (RF) for intestinal enterococci. The different model types are probably the result of the different persistence of two indicator bacteria to the effects of marine environmental factors and consequently the different die-off rates. By combining these two models, the bathing water samples were classified with acceptable performances, an informedness of 71.7%, an F-score of 47.1%, and an overall accuracy of 80.6%.