Sensitivity of sea-level forecasting to the horizontal resolution and sea surface forcing for different configurations of an oceanographic model of the Adriatic Sea

Abstract

Abstract. At the Hydro-meteo-climate service of the Regional environmental agency of Emilia-Romagna, Italy (Arpae-SIMC), the oceanographic numerical model AdriaROMS is used in the operational forecasting suite to compute sea level, temperature, salinity and 3-D current fields of the Adriatic Sea (northern Mediterranean Sea). In order to evaluate the performance of the sea-level forecast and to study different configurations of the ROMS model, two marine storms occurred on the Emilia Romagna coast during the winter 2015-2016 are investigated.The main focus of this study is to analyse the sensitivity of the model to the horizontal resolution and to the meteorological forcing. To this end, the model is run with two different configurations and with two horizontal grids at 1 and 2 km resolution. To study the influence of the meteorological forcing, the two storms have been reproduced by running ROMS in ensemble mode, forced by the 16-members of the meteorological ensemble COSMO-LEPS system. Possible optimizations of the model set-up are deduced by the comparison of the different run outputs.

Integrated sea storm management strategy: the 29 October 2018 event in the Adriatic Sea

“…The western coast is largely sedimentary, with mild sloping and sandy beaches, while the eastern coast is composed of many islands and headlands and is generally high and rocky. Due to its alluvial origin, natural subsidence occurs in the north-western Adriatic Sea because of compaction of fine-grained deposits (Carbognin and Tosi, 2002), which is worsened by the human exploitation of underground water and gas in some areas. Several shallow coastal transitional water bodies are present along the Italian coastline; the main ones are the lagoon of Marano-Grado, the lagoon of Venice, the system of lagoons of the Po delta, the lagoon of Lesina and the lagoon of Varano (Umgiesser et al, 2014).…”

Edge-Based Missing Data Imputation in Large-Scale Environments

“…To evaluate the correctness of the proposed technique we considered a real dataset of environmental information provided by Arpae-SIMC, the weather service of the Emilia-Romagna region in Italy which provides weather warnings to the Italian Civil Protection Department. Beyond climatological data, Arpae-SIMC provides tools, products and indicators to evaluate the impact of storms on the coast [24]. About 48% of this region consists of plains while 27% is hilly and 25% is mountainous.…”

Estimating Missing Environmental Information by Contextual Data Cooperation

“…The proposed framework has been evaluated using a dataset of 196 real temperature samples from 80 weather stations located in the region of Emilia Romagna, Italy, provided by the ARPAE service [4]. Data from this dataset are acquired daily by weather stations.…”