Chronic pain is known as a complex disease due to its comorbidities with other symptoms and the lack of effective treatments. As a consequence, chronic pain seems to be under-diagnosed in more than 75% of patients. At the same time, the advance in brain imaging, the popularization of machine learning techniques and the development of new diagnostic tools based on these technologies have shown that these tools could be an option in supporting decision-making of healthcare professionals. In this study, we computed functional brain connectivity using resting-state fMRI data from one hundred and fifty participants to assess the performance of different machine learning models, including deep learning (DL) neural networks in classifying chronic pain patients and pain-free controls. The best result was obtained by training a convolutional neural network fed with data preprocessed using the MSDL probabilistic atlas and using the dynamic time warping (DTW) as connectivity measure. DL models had a better performance compared to other less costly models such as support vector machine (SVM) and RFC, with balanced accuracy ranged from 69 to 86%, while the area under the curve (ROC) ranged from 0.84 to 0.93. Also, DTW overperformed correlation as connectivity measure. These findings support the notion that resting-state fMRI data could be used as a potential biomarker of chronic pain conditions.

Storm Gloria was the 10th named storm in Europe for the 2019–2020 winter season, and it severely affected Spain and France. This powerful storm represents an excellent study case to analyze the capabilities of the different ocean model systems available in the Spanish Mediterranean coasts to simulate extreme events, as well as to assess their suitability to enhance preparedness in maritime disasters with high impacts on coastal areas. Five different operational ocean forecasting services able to predict the storm-induced ocean circulation are evaluated. Three of the systems are delivered by the Copernicus Marine Service (hereafter CMEMS): the CMEMS global scale solution (GLO-1/12°), the specific Mediterranean basin scale one (MED-1/24°), and the regional solution for the Atlantic façade (IBI-1/36°), which includes also part of the western Mediterranean. These CMEMS core products are complemented with two higher resolution models focused on more limited areas, which provide operational forecasts for coastal applications: the WMOP system developed at the Balearic Islands Coastal Observing and Forecasting System (SOCIB) with a horizontal resolution of roughly 2 km and the Puertos del Estado (PdE) SAMOA systems with a 350-m resolution that cover the coastal domains of the Spanish Port Authorities of Barcelona, Tarragona, Castellón and Almeria. Both the WMOP and SAMOA models are nested in CMEMS regional systems (MED and IBI, respectively) and constitute good examples of coastal-scale-oriented CMEMS downstream services. The skill of these five ocean models in reproducing the surface dynamics in the area during Gloria is evaluated using met-ocean in situ measurements from numerous buoys (moored in coastal and open waters) and coastal meteorological stations as a reference to track the effects of the storm in essential ocean variables such as surface current, water temperature, and salinity throughout January 2020. Furthermore, modeled surface dynamics are validated against hourly surface current fields from the two high-frequency radar systems available in the zone (the SOCIB HF-Radar system covering the eastern part of the Ibiza Channel and the PdE one at Tarragona, which covers the Ebro Delta, one of the coastal areas most impacted by Gloria). The results assess the performance of the dynamical downscaling at two different levels: first, within the own CMEMS service (with their regional products, as enhanced solutions with respect to the global one) and second in the coastal down-streaming service side (with very high-resolution models reaching coastal scales). This multi-model study case focused on Storm Gloria has allowed to identify some strengths and limitations of the systems currently in operations, and it can help outlining future model service upgrades aimed at better forecasting extreme coastal events.

Abstract. An ocean data assimilation system to assimilate Argo temperature (T ) and salinity (S) profiles into the HYbrid Coordinate Ocean Model (HYCOM) was constructed, implemented and evaluated for the first time in the Atlantic Ocean (78 • S to 50 • N and 98 • W to 20 • E). The system is based on the ensemble optimal interpolation (EnOI) algorithm proposed by Xie and Zhu (2010), especially made to deal with the hybrid nature of the HYCOM vertical coordinate system with multiple steps. The Argo T -S profiles were projected to the model vertical space to create pseudoobserved layer thicknesses ( p obs ), which correspond to the model target densities. The first step was to assimilate p obs considering the sub-state vector composed by the model layer thickness ( p) and the baroclinic velocity components. After that, T and S were assimilated separately. Finally, T was diagnosed below the mixed layer to preserve the density of the model isopycnal layers. Five experiments were performed from 1 January 2010 to 31 December 2012: a control run without assimilation, and four assimilation runs considering the different vertical localizations of T , S and p. The assimilation experiments were able to significantly improve the thermohaline structure produced by the control run. They reduced the root mean square deviation (RMSD) of T and S calculated with respect to Argo independent data in 34 and 44 %, respectively, in comparison to the control run. In some regions, such as the western North Atlantic, substantial corrections in the 20 • C isotherm depth and the upper ocean heat content towards climatological states were achieved.The runs with a vertical localization of p showed positive impacts in the correction of the thermohaline structure and reduced the RMSD of T (S) from 0.993 • C (0.149 psu) to 0.905 • C (0.138 psu) for the whole domain with respect to the other assimilation runs.