No abstract

[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.

No abstract