Number Of Drifters Research Articles

On Characterizing Ocean Kinematics from Surface Drifters

Abstract Horizontal kinematic properties, such as vorticity, divergence, and lateral strain rate, are estimated from drifter clusters using three approaches. At submesoscale horizontal length scales , kinematic properties become as large as planetary vorticity f, but challenging to observe because they evolve on short time scales . By simulating surface drifters in a model flow field, we quantify the sources of uncertainty in the kinematic property calculations due to the deformation of cluster shape. Uncertainties arise primarily due to (i) violation of the linear estimation methods and (ii) aliasing of unresolved scales. Systematic uncertainties (iii) due to GPS errors, are secondary but can become as large as (i) and (ii) when aspect ratios are small. Ideal cluster parameters (number of drifters, length scale, and aspect ratio) are determined and error functions estimated empirically and theoretically. The most robust method—a two-dimensional, linear least squares fit—is applied to the first few days of a drifter dataset from the Bay of Bengal. Application of the length scale and aspect-ratio criteria minimizes errors (i) and (ii), and reduces the total number of clusters and so computational cost. The drifter-estimated kinematic properties map out a cyclonic mesoscale eddy with a surface, submesoscale fronts at its perimeter. Our analyses suggest methodological guidance for computing the two-dimensional kinematic properties in submesoscale flows, given the recently increasing quantity and quality of drifter observations, while also highlighting challenges and limitations. Significance Statement The purpose of this study is to provide insights and guidance for computing horizontal velocity gradients from clusters (i.e., three or more) of Lagrangian surface ocean drifters. The uncertainty in velocity gradient estimates depends strongly on the shape deformation of drifter clusters by the ocean currents. We propose criteria for drifter cluster length scales and aspect ratios to reduce uncertainties and develop ways of estimating the magnitude of the resulting errors. The findings are applied to a real ocean dataset from the Bay of Bengal.

Investigation of rip current processes along Visakhapatnam beaches, east coast of India: A study based on GNSS drifters and dye experiments

Rip currents are known as one of the most dangerous hazards on sandy beaches worldwide. Indian beaches are least explored to study the variability of rip currents in space and time. The present study is the first attempt made to understand its dynamics by utilizing indigenously developed GNSS (Global Navigation Satellite System) drifters and Rhodamine-B dye experiments. The Rama Krishna (RK) and Rushikonda Beaches of Visakhapatnam are chosen for this study, due to a recorded number of rip current-related drowning cases observed during the past decade. Few experiments were conducted during pre- and post-monsoon seasons of 2018. Drifters work on the Lagrangian principle, where they measure the current velocities along their paths driven by the surface currents. Error analysis of drifter measurements showed that they are capable of resolving surf zone motions very accurately. Strong rip currents were observed in few locations in the study area, where at times current velocities reached ~1 ms−1. Also, Rhodamine-B dye was released into the rip current prone zones along with the drifters and observed that the dye patches also followed the drifters. From these experiments, it has been observed that the rip currents are relatively strong during the post-monsoon season, which could be due to the change in the beach morphology. Similar experiments with more number of drifters would help in understanding rip current dynamics and would help in reducing rip current drowning in the beaches.

The Effect of Drifter GPS Errors on Estimates of Submesoscale Vorticity

AbstractDifferential kinematic flow properties (DKP), such as vertical vorticity, have been estimated from surface drifters. However, previous DKP error estimates were a posteriori and did not include correlated errors across drifters. To accurately estimate submesoscale (≤1 km) DKPs from drifters, errors must be better understood. Here, the a priori vorticity standard error is derived that depends upon the number of drifters in the cluster, the drifter cluster major and minor axes lengths, the instrument velocity error, and the cross-drifter error correlation. Two stationary GPS experiments, with zero vorticity, were performed at separations of O(101–103) m to understand vorticity error and test the derivation using 1 Hz position differences and Doppler shift velocities. Vorticity errors of ±5f (where f is the local Coriolis parameter)were found for ≈40 m separations. The frequency-dependent velocity variances and GPS-to-GPS correlations are quantified. Vorticity estimated with a “blended” velocity has reduced error. The stationary vorticity error can be well predicted given velocity error, correlation, and minor axis length. Vorticity error analysis is applied to submesoscale-sampling in situ GPS drifters near Point Sal, California. The derivation predicts when large high-frequency vorticity fluctuations (indicating noise) occur. Previously, cluster area or ellipticity were used as criteria to distinguish error. We show that the drifter cluster minor axis (narrowness) is a key time-dependent factor affecting vorticity error, and even for velocity errors <0.004 m s−1, the vorticity error exceeds ±5f when cluster minor axis <50 m. These results will aid submesoscale drifter deployment planning.

The MEDESS-GIB database: tracking the Atlantic water inflow

Abstract. On 9 September 2014, an intensive drifter deployment was carried out in the Strait of Gibraltar. In the frame of the MEDESS-4MS Project (EU MED Program), the MEDESS-GIB experiment consisted of the deployment of 35 satellite tracked drifters, mostly of CODE-type, equipped with temperature sensor sampling at a rate of 30 min. Drifters were distributed along and on both sides of the Strait of Gibraltar. The MEDESS-GIB deployment plan was designed as to ensure quasi-synoptic spatial coverage. To this end, four boats covering an area of about 680 NM2 in 6 h were coordinated. As far as these authors know, this experiment is the most important exercise in the area in terms of number of drifters released. Collected satellite-tracked data along drifter trajectories have been quality controlled and processed to build the presented MEDESS-GIB database. This paper reports the MEDESS-GIB data set that comprises drifter trajectories, derived surface currents and in situ SST measurements collected along the buoys tracks. This series of data is available through the PANGAEA (Data Publisher for Earth and Environmental Science) repository, with the following doi:10.1594/PANGAEA.853701. Likewise, the MEDESS-GIB data will be incorporated as part of the Copernicus Marine historical products. The MEDESS-GIB data set provides a complete Lagrangian view of the surface inflow of Atlantic waters through the Strait of Gibraltar and thus, very useful data for further studies on the surface circulation patterns in the Alboran Sea, and their links with one of the most energetic Mediterranean Sea flows: the Algerian Current.

Comparison of two data assimilation algorithms for shallow water flows

This article presents the comparison of two algorithms for data assimilation of two dimensional shallow water flows. The first algorithm is based on a linearization of the model equations and a quadratic programming (QP) formulation of the problem. The second algorithm uses Ensemble Kalman Filtering (EnKF) applied to the non-linear two dimensional shallow water equations. The two methods are implemented on a scenario in which boundary conditions and Lagrangian measurements are available. The performance of the methods is evaluated using twin experiments with experimentally measured bathymetry data and boundary conditions from a river located in the Sacramento Delta. The sensitivity of the algorithms to the number of drifters, low or high discharge and time sampling frequency is studied.

Predictability of Drifter Trajectories in the Tropical Pacific Ocean

Abstract Predictability of particle motion in the ocean over a timescale of one week is studied using three clusters of buoys consisting of 5–10 drifters deployed in the tropical Pacific Ocean. The analysis is conducted by using three techniques with increasing complexity: the center of mass of the cluster, advection by climatological currents, and a new technique that relies on the assimilation of both velocity and position data from the surrounding drifters into a Markov model for particle motion. A detailed mathematical description of the theory leading to this model is given. The predictability of drifter motion in these clusters is characterized using the data density Nd, defined as the number of drifters over an area scaled by the mean diameter of the cluster. The data density Nd decreases along the drifter trajectories due to the tendency of particles to disperse by turbulent fluid motion. In the first regime, which corresponds to the period after the release of drifters in a tight cluster when Nd ...

Prediction of particle trajectories in the Adriatic Sea using Lagrangian data assimilation

The predictability of Lagrangian particle trajectories in the Adriatic Sea (a semi-enclosed sub-basin of the Mediterranean Sea) over a period of 1–2 weeks is investigated using three clusters consisting of 5–7 drifters. The analysis is conducted using a Gauss–Markov Lagrangian particle model, which relies on the estimate of climatological mean flow field, persistence of turbulence, and assimilation of velocity data from the surrounding drifters through a Kalman filtering technique. The results are described using the data density N R defined as the number of drifters within a distance on the order of the Rossby radius of deformation from the particle to be predicted. The clusters are inherently different with respect to this characteristic property with values ranging from N R<0.5 to N R≥2.0 over the analysis period, depending on the initial launch pattern of the clusters and the dispersion processes. The results indicate that during the period when N R≥1, the assimilation of surrounding drifter data leads to an improvement of predicted trajectories with respect to those based on advecting the drifters with the mean flow. When N R<1, the drifters are too far apart to exhibit correlated motion, and the assimilation method does not lead to an improvement. The effects of uncertainties in the mean flow field and initial release position are discussed. The results are also compared to simple estimates of particle location by calculating the center of mass of the cluster.

The use of simulated drifters to estimate vorticity

A primitive equation ocean model is used to generate trajectories of simulated clusters of drifters in the California Current (CC) region. These trajectories allow us to evaluate a least squares (LS) method of estimating vorticity and vertical velocity along a cluster's path. Two clusters provide examples of successful and less successful estimates of vorticity and vertical velocity. Our analysis quantifies the dependence of estimate quality on several parameters that can be used as error predictors in the LS estimate of vorticity: cluster separation, number of drifters in a cluster, and cluster shape. A combination of cluster separation and ellipticity shows the most promise as an indicator of quality for the vorticity estimate.

On the Predictability of Lagrangian Trajectories in the Ocean

The predictability of particle trajectories in oceanic flows is investigated in the context of a primitive equation, idealized, double-gyre ocean model. This study is motivated not only by the fact that this is an important conceptual problem but also by practical applications, such as searching for objects lost at sea, and ecological problems, such as the spreading of pollutants or fish larvae. The original aspect of this study is the use of Lagrangian drifter data to improve the accuracy of predicted trajectories. The prediction is performed by assimilating velocity data from the surrounding drifters into a Gauss–Markov model for particle motion. The assimilation is carried out using a simplified Kalman filter. The performance of the prediction scheme is quantified as a function of a number of factors: 1) dynamically different flow regimes, such as interior gyre, western boundary current, and midlatitude jet regions; 2) density of drifter data used in assimilation; and 3) uncertainties in the knowledge of the mean flow field and the initial conditions. The data density is quantified by the number of data per degrees of freedom NR, defined as the number of drifters within the typical Eulerian space scale from the prediction particle. The simulations indicate that the actual World Ocean Circulation Experiment sampling (1 particle/[5° × 5°] or NR ≪ 1) does not improve particle prediction, but predictions improve significantly when NR ≫ 1. For instance, a coverage of 1 particle/[1° × 1°] or NR ∼ O(1) is already able to reduce the errors of about one-third or one-half. If the sampling resolution is increased to 1 particle/[0.5° × 0.5°] or 1 particle/[0.25° × 0.25°] or NR ≫ 1, reasonably accurate predictions (rms errors of less than 50 km) can be obtained for periods ranging from one week (western boundary current and midlatitude jet regions) to three months (interior gyre region). Even when the mean flow field and initial turbulent velocities are not known accurately, the information derived from the surrounding drifter data is shown to compensate when NR > 1. Theoretical error estimates are derived that are based on the main statistical parameters of the flow field. Theoretical formulas show good agreement with the numerical results, and hence, they may serve as useful a priori estimates of Lagrangian prediction error for practical applications.