1,721,047 research outputs found
Coastal sea responses to atmospheric forcings at two different resolutions
No full textWe investigated coastal sea responses to three, multi-day strong wind episodes that occurred in the middle Adriatic during the Target Operational Period (TOP) of the European COastal sea OPerational observing and forecasting system (ECOOP) project. A high-resolution oceanographic model (1 km horizontal, 16 σ vertical layers) based on the modified Princeton Ocean Model (POM) was applied to a highly complex domain located in the coastal area of the eastern Adriatic Sea. The oceanographic model was nested into the Adriatic REGional model (AREG-2) covering the entire Adriatic Sea. Meteorological forcing was prepared by two atmospheric models. The coarser model was the European Centre for Medium-range Weather Forecast model (ECMWF, with horizontal and temporal resolutions of 0.25° and 6 h, respectively), and the finer one was the Aire Limitée Adaptation dynamique Développement InterNational model (ALADIN, with horizontal and temporal resolutions of 8 km and 3 h, respectively, and winds dynamically adapted to a horizontal resolution of 2 km). The results show that small-scale atmospheric features, which arise due to the orographically complex mainland and the number of islands and were not reproduced by the coarser atmospheric model, substantially affected surface currents, mass transports, sea surface temperature (SST) and surface salinity in the coastal area during strong Bora. For strong Sirocco, the atmospheric model's resolution was important for currents on the lee sides of islands. © Author(s) 2011
Optimal Assimilation of Daytime SST Retrievals from SEVIRI in a Regional Ocean Prediction System
No full textExploiting the potential of space-borne oceanic measurements to characterize the sub-surface structure of the ocean becomes critical in areas where deployment of in situ sensors might be difficult or expensive. Sea Surface Temperature (SST) observations potentially provide enormous amounts of information about the upper ocean variability. However, the assimilation of daytime SST retrievals, e.g., from infrared sensors into ocean prediction systems, requires a specific treatment of the diurnal cycle of skin SST, which is generally under-estimated in current ocean models due to poor vertical resolution at the air–sea interface and lack of proper parameterizations. To this end, a simple off-line bias correction scheme is proposed, where the bias predictors include, among others, the warm layer and cool skin warming/cooling deduced from a prognostic model. Furthermore, a localization procedure that limits the vertical penetration of the SST information in a hybrid variational-ensemble data assimilation system is formulated. These two novelties are implemented and assessed within a regional ocean prediction system in the Ligurian Sea for the assimilation of daytime SST data retrieved with hourly frequency from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard the geostationary satellite Meteosat-10. Experiments are validated against independent measurements collected by gliders, moorings, and drifters during the Long-term Glider Missions for Environmental Characterization (LOGCMEC17) sea trial. Results suggest that the simple bias correction scheme is effective in improving both the sea surface and mixed layer accuracy, correctly thinning the mixed layer compared to the control experiment, outperforming experiments with night-only data assimilation, and improving the forecast skill scores. Localization further improves the prediction of the mixed layer depth. It is therefore recommended that sophisticated bias correction and localization procedures are adopted for fruitfully assimilating daytime SST data in operational oceanographic analysis systems
Numerical simulation and decomposition of kinetic energy in the Central Mediterranean: Insight on mesoscale circulation and energy conversion
No full textThe spatial and temporal variability of eddy and mean kinetic energy of the Central Mediterranean region has been investigated, from January 2008 to December 2010, by mean of a numerical simulation mainly to quantify the mesoscale dynamics and their relationships with physical forcing. In order to understand the energy redistribution processes, the baroclinic energy conversion has been analysed, suggesting hypotheses about the drivers of the mesoscale activity in this area. The ocean model used is based on the Princeton Ocean Model implemented at 1/32° horizontal resolution. Surface momentum and buoyancy fluxes are interactively computed by mean of standard bulk formulae using predicted model Sea Surface Temperature and atmospheric variables provided by the European Centre for Medium Range Weather Forecast operational analyses. At its lateral boundaries the model is one-way nested within the Mediterranean Forecasting System operational products. The model domain has been subdivided in four sub-regions: Sardinia channel and southern Tyrrhenian Sea, Sicily channel, eastern Tunisian shelf and Libyan Sea. Temporal evolution of eddy and mean kinetic energy has been analysed, on each of the four sub-regions, showing different behaviours. On annual scales and within the first 5 m depth, the eddy kinetic energy represents approximately the 60 % of the total kinetic energy over the whole domain, confirming the strong mesoscale nature of the surface current flows in this area. The analyses show that the model well reproduces the path and the temporal behaviour of the main known sub-basin circulation features. New mesoscale structures have been also identified, from numerical results and direct observations, for the first time as the Pantelleria Vortex and the Medina Gyre. The classical kinetic energy decomposition (eddy and mean) allowed to depict and to quantify the permanent and fluctuating parts of the circulation in the region, and to differentiate the four sub-regions as function of relative and absolute strength of the mesoscale activity. Furthermore the Baroclinic Energy Conversion term shows that in the Sardinia Channel the mesoscale activity, due to baroclinic instabilities, is significantly larger than in the other sub-regions, while a negative sign of the energy conversion, meaning a transfer of energy from the Eddy Kinetic Energy to the Eddy Available Potential Energy, has been recorded only for the surface layers of the Sicily Channel during summer. © Author(s) 2011. CC Attribution 3.0 License
The eastern Mediterranean Sea biogeochemical dynamics in the 90’s: A numerical study.
No full textThe coupled physical-biogeochemical dynamics of the Mediterranean Sea have been
hindcasted for the decade 1990–2000 with the Nucleus for European Modeling of the
Ocean-Biogeochemical Flux Model coupled modeling system. This work describes and
discusses the simulated changes in the Eastern Mediterranean Sea physical and
biogeochemical dynamics occurring in the 1990s, contemporary to the establishment of
the Eastern Mediterranean Transient. The physical component of the modeling system
reproduces several changes in the Eastern Mediterranean physical dynamics and
thermohaline structure that are consistent with observations pertinent to the transient
period. The simulated change in the atmospheric forcing during the early 1990s is
considered sufficient to develop upwelling favorable conditions that determine an overall
upward displacement of the simulated deep Eastern Mediterranean nutrient pool. Model
results indicate that in the post transient period, the displaced nutrients were advected
westward along with the reestablishment of the Levantine Intermediate Water pathway,
and together with the occurrence of strong winter mixing events in the Ionian Sea, they
determined an increase of the primary production processes in the euphotic layer along the
eastern coast of Ionian Sea and northern Levantine basin. The biogeochemical model
suggests that such an increase in productivity apparently impacted mostly the microbial
branch of the marine trophic web
A study of the hydrographic conditions in the Adriatic Sea from numerical modelling and direct observations (2000–2008)
Full text linkThe inter-annual variability of Adriatic Sea hydrographic characteristics is investigated by means of numerical simulation and direct observation. The period under investigation runs from the beginning of 2000 to the end of 2008. The model used to carry out the simulation is derived from the primitive equation component of the Adriatic Forecasting System (AFS). The model is based on the Princeton Ocean Model (POM) adapted in order to reproduce the features of the Adriatic. Both numerical findings and observations agree in depicting a strong inter-annual variability in the entire Adriatic Sea and its sub-basins. Nevertheless, two model deficiencies are identified: an excessive vertical/horizontal mixing and an inaccurate representation of the thermohaline properties of the entering Mediterranean Waters. The dense water formation process has been found to be intermittent. In addition to inter-annual variability, a long-scale signal has been observed in the salinity content of the basin as a consequence of a prolonged period of reduced Po river runoff and high evaporation rates. As a result, the temperature and salinity of the northern Adriatic dense water vary considerably between the beginning and the end of the period investigated
Introducing along-track error correlations for altimetry data in a regional ocean prediction system
No full textBecause of the systematic error in the processing of altimetry data, sea level anomaly (SLA) observation errors are likely affected by nonnegligible spatial correlations. To account for these, we exploit the synergy of altimetry data with in situ profiles from gliders, piloted to follow the altimetry tracks during the Long-Term Glider Mission for Environmental Characterization 2017 (LOGMEC17) observational campaign in the Ligurian Sea. The assimilation of along-track unfiltered sea level anomalies in a regional ocean analysis and forecast system is consequently optimized by means of introducing spatial correlations for the SLA observation errors. In particular, collocated data of glider and altimetry are used to derive an along-track error covariance model for the sea level anomaly assimilation, assuming that most of the covariance behavior versus separation distance stems from altimetry. Spatial scales of the altimetry error are found to have a correlation radius of about 12 km for the dataset utilized in the Ligurian Sea, using a simple Gaussian shape for the error correlation, shorter than the correlation radius found through assimilation output diagnostics. A variational data assimilation system is modified to relax the usual assumption of uncorrelated altimetry observation errors, thus allowing for along-track error correlations. Its implementation provides promising results in the regional ocean prediction system, outperforming in most verification skill scores the use of uncorrelated observational errors without compromising the analysis scheme efficiency
Navigation of AUVs based on Ocean Fields Variability
No full textA key to enable long-endurance and large area missions for Autonomous Underwater Vehicles is the development of novel navigation methods. Typical solutions based on periodic surfacing or the deployment of static beacons suffer from a number of limitations ranging from interrupting the vehicle task to constrain the operational area. To overcome some of these limitations, this paper moves the first steps into a different direction and aims at using marine environmental information (e.g. temperature, salinity, etc.) as navigational aid for the robots. Towards this aim the paper presents a Particle Filter able to use temperature and salinity maps produced by state-of-the-art ocean models, and assesses the navigation performance over a week long simulated mission. The obtained numerical results show that the proposed approach is able to substantially bound the navigation error, and hence to support the navigation of underwater robots for long-range missions. Discussion on advantages, limitations and promising ways forward are also presented
Understanding altimetry signals in the Northeastern Ligurian sea using a multi-platform approach
No full textDuring the Long-Term Glider Mission for Environmental Characterization 2016 sea trial, carried out in the eastern Ligurian Sea (Northwestern Mediterranean Sea), two gliders rated to a maximum depth of 1000 m operated continuously from 3 May 2016–27 June 2016. When possible, glider tracks were synchronized with the contemporaneous footprints of the Jason-2, SARAL/AltiKa and CryoSat-2 altimeters. Temperature and salinity measured by the gliders that were co-localised with the altimeter passages were used to calculate along-track dynamic heights (DH). The latter were then compared with the altimeters’ near real-time absolute dynamic topography (ADT) measurements. ADT and DH values showed very similar, but shifted patterns, suggesting that gliders had sampled the same structures but at different times. Average surface absolute geostrophic velocities at the time of glider transit were used in a novel relocation technique to reposition glider measurements where they would have been at the time of the altimeter passage. The relocation increases the correlation between datasets to a value close to 1 and reduces the RMSE of an order of magnitude, also when the time-difference between measurements was greater than 3 days. The sea level spatial and temporal variability is attributed to the presence of meanders generated by the baroclinic instability of the Liguro- Provençal Current (LPC). These meanders are characterized by a 38 km amplitude and 0.12 m s −1 average propagation speed that are confirmed by the larger scale ocean colour measurements and are in agreement with those previously described in the literature. We found that the presence of LPC meanders in along-track ADT and DH measurements is evidenced by a decrease in sea level when profiles sampled the Modified Atlantic Water, and an increase in sea level when measurements sampled the adjacent waters of Tyrrhenian Sea origin. The relocation technique introduced here is expected to improve future altimetry-glider comparisons, as similar validation experiments were considered possible only when glider and altimetry measurements were near synoptic, and collected up to one day from each other
A Neural network based observation operator for coupled ocean acoustic variational data assimilation
Full text linkVariational data assimilation requires implementing the tangent-linear and adjoint (TA/AD) version of any operator. This intrinsically hampers the use of complicated observations.Here, we assess a new data-driven approach to assimilate acoustic underwater propagation measurements [transmission loss (TL)] into a regional ocean forecasting system. TL measurements depend on the underlying sound speed fields, mostly temperature, and their inversion would require heavy coding of the TA/AD of an acoustic underwater propagation model. In this study, the nonlinear version of the acoustic model is applied to an ensemble of perturbed oceanic conditions. TL outputs are used to formulate both a statistical linear operator based on canonical correlation analysis (CCA), and a neural network based (NN) operator. For the latter, two linearization strategies are compared, the best-performing one relying on reverse-mode automatic differentiation. The new observation operator is applied in data assimilation experiments over the Ligurian Sea (Mediterranean Sea), using the observing system simulation experiments (OSSE) methodology to assess the impact of TL observations onto oceanic fields. TL observations are extracted from a nature run with perturbed surface boundary conditions and stochastic ocean physics. Sensitivity analyses indicate that theNNreconstruction of TL is significantly better than CCA. BothCCAandNNare able to improve the upper-ocean skill scores in forecast experiments, with NN outperforming CCA on the average. The use of the NN observation operator is computationally affordable, and its general formulation appears promising for the adjoint-free assimilation of any remote sensing observing network. SIGNIFICANCE STATEMENT: Deep learning algorithms are now widely spread in a diverse range of fields to help with solving automatic classification and regression problems. Here, we present and assess a strategy aimed at introducing an observation operator based on neural networks in data assimilation. Linearization of such an operator, required by variational schemes, is also discussed and implemented. The methodology is applied to the coupled oceanic acoustic data assimilation problem, and provides promising results. Our approach may be extended in the future to assimilate any remotely sensed type of observations
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