Abstract:A Lagrangian trajectory model, TRACMASS with the use of velocity fields calculated by the Rossby Centre (Swedish Hydrological and Meteorological Institute) circulation model, is employed to analyse trajectories of current-driven surface transport in the Gulf of Finland, the Baltic Sea, for the period of 1987-1991. Statistical analysis of trajectories is performed to calculate a map of probabilities for adverse impacts released in different sea areas to hit the coast. There is a clearly defined curve (equiproba… Show more
“…For the Baltic Sea, there have been several studies using model-simulated trajectories (Döös et al 2004;Soomere et al 2010;Corell et al 2012), but very little observational Lagrangian data. To the authors' knowledge, there has been only one experiment using Surface Velocity Program (SVP) drifters similar to the ones used in this study (Håkansson and Rahm 1993;Launiainen et al 1993).…”
Results from experiments with surface drifters in the Baltic Sea in 2010-2011 are presented and discussed. In a first experiment, 12 SVP-B (Surface Velocity Program, with Barometer) drifters with a drogue at 12-18 m depth were deployed in the Baltic Sea. In a second experiment, shallow drifters extending to a depth of 1.5 m were deployed in the Gulf of Finland. Results from the SVP-B drifter experiment are compared to results from a regional ocean model and a trajectory code. Differences between the observed SVP-B drifters and simulated drifters are found for absolute dispersion (i.e., squared displacement from initial position) and relative dispersion (i.e., squared distance between two initially paired drifters). The former is somewhat underestimated since the simulated currents are neither as fast nor as variable as those observed. The latter is underestimated both due to the abovementioned reasons and due to the resolution of the ocean model.For the shallower drifters, spreading in the upper 1-2 m of the Gulf of Finland is investigated. The spreading rate is about 200 m/day for separations <0.5 km, 500 m/day for separations below 1 km and in the range of 0.5-3 km/day for separations in the range of 1-4 km. The spreading rate does not follow Richardson's law. The initial spreading, up to a distance of about d = 100-150 m, is governed by the power law d ∼ t 0.27 whereas for larger separations the distance increases as d ∼ t 2.5 .
“…For the Baltic Sea, there have been several studies using model-simulated trajectories (Döös et al 2004;Soomere et al 2010;Corell et al 2012), but very little observational Lagrangian data. To the authors' knowledge, there has been only one experiment using Surface Velocity Program (SVP) drifters similar to the ones used in this study (Håkansson and Rahm 1993;Launiainen et al 1993).…”
Results from experiments with surface drifters in the Baltic Sea in 2010-2011 are presented and discussed. In a first experiment, 12 SVP-B (Surface Velocity Program, with Barometer) drifters with a drogue at 12-18 m depth were deployed in the Baltic Sea. In a second experiment, shallow drifters extending to a depth of 1.5 m were deployed in the Gulf of Finland. Results from the SVP-B drifter experiment are compared to results from a regional ocean model and a trajectory code. Differences between the observed SVP-B drifters and simulated drifters are found for absolute dispersion (i.e., squared displacement from initial position) and relative dispersion (i.e., squared distance between two initially paired drifters). The former is somewhat underestimated since the simulated currents are neither as fast nor as variable as those observed. The latter is underestimated both due to the abovementioned reasons and due to the resolution of the ocean model.For the shallower drifters, spreading in the upper 1-2 m of the Gulf of Finland is investigated. The spreading rate is about 200 m/day for separations <0.5 km, 500 m/day for separations below 1 km and in the range of 0.5-3 km/day for separations in the range of 1-4 km. The spreading rate does not follow Richardson's law. The initial spreading, up to a distance of about d = 100-150 m, is governed by the power law d ∼ t 0.27 whereas for larger separations the distance increases as d ∼ t 2.5 .
“…Consequently, it takes, on average, more than 20 days for two initially closely located particles to drift into different grid cells for the 2-nm horizontal resolution of the RCO model. This suggests that for the given length of calculations (10 days in Soomere et al 2010 and 20 days in the current study), the ignoring of subgridscale processes may modify a part of trajectories, but the majority of the simulated trajectories apparently will remain at a distance of less than one grid step from their position according to more realistic calculations. Therefore, it is apparently reasonable to assume that for the particular horizontal resolution of the ocean model and length of trajectories, the ignoring of subgrid spreading does not significantly affect the resulting 2D fields.…”
Section: Modelling Environmentmentioning
confidence: 92%
“…An early attempt to use the entire procedure using a reasonable amount of computation resources and offering a simple example of an approximate solution to the relevant inverse problem-the equiprobability line-is described in Soomere et al (2010). The new development here is the comparative analysis of the resulting optimum locations for the fairway based on different criteria (minimum probability, maximum particle age, equidistribution of probability) and establishing several interesting properties of the relevant 2D distributions and locations for almost optimum fairways.…”
Section: Modelling Environmentmentioning
confidence: 98%
“…Therefore, there exists a high probability that various adverse impacts may be released along the shipping route, whereas consequences of major oil pollution could be devastating for this particularly sensitive sea area (Kachel 2008). On the other hand, there exist reliable and thoroughly validated ocean models for this region ) that is known to host numerous semi-persistent patterns of both surface and subsurface currents (Andrejev et al 2004a, b;Soomere et al 2010).…”
We address possibilities of minimising environmental risks using statistical features of current-driven propagation of adverse impacts to the coast. The recently introduced method for finding the optimum locations of potentially dangerous activities (Soomere et al. in Proc Estonian Acad Sci 59:156-165, 2010) is expanded towards accounting for the spatial distributions of probabilities and times for reaching the coast for passively advecting particles released in different sea areas. These distributions are calculated using large sets of Lagrangian trajectories found from Eulerian velocity fields provided by the Rossby Centre Ocean Model with a horizontal resolution of 2 nautical miles for 1987-1991. The test area is the Gulf of Finland in the northeastern Baltic Sea. The potential gain using the optimum fairways from the Baltic Proper to the eastern part of the gulf is an up to 44% decrease in the probability of coastal pollution and a similar increase in the average time for reaching the coast. The optimum fairways are mostly located to the north of the gulf axis (by 2-8 km on average) and meander substantially in some sections. The robustness of this approach is quantified as the typical root mean square deviation (6-16 km) between the optimum fairways specified from different criteria. Drastic variations in the width of the 'corridors' for almost optimal fairways (2-30 km for the average width of 15 km) signifies that the sensitivity of the results with respect to small changes in the environmental criteria largely varies in different parts of the gulf.
“…Lagrangian trajectories of virtual particles (v-particles) carried passively or actively by ocean currents are very useful for estimating the fate of oil spills (Soomere et al, 2010) or living organisms (Corell, 2012;Ohashi and Sheng, 2015), as well as for planning rescue operations or finding lost goods. In large enough quantities, Lagrangian trajectories of v-particles can be used to track entire water masses, or to map the mean flow (Richardson, 1983).…”
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