Blending HF radar and model velocities in Monterey Bay through normal mode analysis

Lipphardt, B. L.; Kirwan, A. D.; Grosch, C. E.; Lewis, James K.; Paduan, Jeffrey D.

doi:10.1029/1999jc900295

Cited by 71 publications

(75 citation statements)

References 13 publications

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“…To overcome these data gaps, various interpolation techniques have been applied to HF radar total vector fields. These algorithms that include 2DVAR (Yaremchuk and Sentchev 2009), normal modes (Lipphardt et al 2000), open modal analysis (Kaplan and Lekien 2007), and statistical mapping (Barrick et al 2012;O'Donnell et al 2005) have largely been applied to the UWLS surface current maps after the UWLS combination. Recently, Kim et al (2007) introduced a method that interpolates data as part of the combination step from radial component vectors to total vector maps.…”

Section: Hf Radar Processingmentioning

confidence: 99%

Evaluation of two algorithms for a network of coastal HF radars in the Mid-Atlantic Bight

et al. 2012

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The National High Frequency (HF) Surface Current Mapping Radar Network is being developed as a backbone system within the U.S. Integrated Ocean Observing System. This paper focuses on the application of HF radarderived surface current maps to U.S. Coast Guard Search and Rescue operations along the Mid-Atlantic coast of the USA. In that context, we evaluated two algorithms used to combine maps of radial currents into a single map of total vector currents. In situ data provided by seven drifter deployments and four bottom-mounted current meters were used to (1) evaluate the well-established unweighted least squares (UWLS) and the more recently adapted optimal interpolation (OI) algorithms and (2) quantify the sensitivity of the OI algorithm to varying decorrelation scales and error thresholds. Results with both algorithms were shown to depend on the location within the HF radar data footprint. The comparisons near the center of the HF radar coverage showed no significant difference between the two algorithms. The most significant distinction between the two was seen in the drifter trajectories. With these simulations, the weighting of radial velocities by distance in the OI implementation was very effective at reducing both the distance between the actual drifter and the cluster of simulated particles as well as the scale of the search area that encompasses them. In this study, the OI further reduced the already improved UWLS-based search areas by an additional factor of 2. The results also indicated that the OI output was relatively insensitive to the varying decorrelation scales and error thresholds tested.

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Section: Hf Radar Processingmentioning

confidence: 99%

Evaluation of two algorithms for a network of coastal HF radars in the Mid-Atlantic Bight

et al. 2012

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“…Our study uses the MZD to construct appropriate EOFs for the analysis of the spatio-temporal structure of the model attractor at 50 m depth. Other oceanographic applications of MZD can be found in Eremeev et al (1992), Lipphardt et al (2000) and Chu et al (2003a, b) selected as examples.…”

Section: Toroidal and Poloidal Eof Decompositionmentioning

confidence: 99%

Lagrangian predictability of high-resolution regional models: the special case of the Gulf of Mexico

Chu

Ivanov

Kantha

et al. 2004

Nonlin. Processes Geophys.

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Abstract. The Lagrangian prediction skill (model ability to reproduce Lagrangian drifter trajectories) of the nowcast/forecast system developed for the Gulf of Mexico at the University of Colorado at Boulder is examined through comparison with real drifter observations. Model prediction error (MPE), singular values (SVs) and irreversible-skill time (IT) are used as quantitative measures of the examination. Divergent (poloidal) and nondivergent (toroidal) components of the circulation attractor at 50 m depth are analyzed and compared with the Lagrangian drifter buoy data using the empirical orthogonal function (EOF) decomposition and the measures, respectively. Irregular (probably, chaotic) dynamics of the circulation attractor reproduced by the nowcast/forecast system is analyzed through Lyapunov dimension, global entropies, toroidal and poloidal kinetic energies. The results allow assuming exponential growth of prediction error on the attractor. On the other hand, the q-th moment of MPE grows by the power law with exponent of 3q/4. The probability density function (PDF) of MPE has a symmetrical but nonGaussian shape for both the short and long prediction times and for spatial scales ranging from 20 km to 300 km. The phenomenological model of MPE based on a diffusion-like equation is developed. The PDF of IT is non-symmetric with a long tail stretched towards large ITs. The power decay of the tail was faster than 2 for long prediction times.

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“…This influx is due to numerous methods of data collection which are being implemented: HF or high frequency radar, Lagrangian drifters, synthetic aperture radar, new generation passive remote-sensing platforms and towed arrays which can collect information on current velocity fields of a ship's wake . 1 This gives scientists a significantly improved picture of current flow throughout many coastal regions.…”

Section: Introductionmentioning

confidence: 99%

“…2,3 In the paper by Eremeev, the data was received from autonomous drifting buoys (ADB) in the Black Sea was used to extrapolate velocity fields for this closed body of water using what was later termed by Lipphardt as normal mode analysis (NMA). 4,5 Eremeev and his collaborators found that this process allowed him to model the large scale currents measured by the ADB's with a relatively small number of modes. One set of data required only 76 modes and yet still accounted for 70% of the kinetic energy associated with the observed field.…”

Section: Introductionmentioning

confidence: 99%

Normal Mode Analysis of the Chesapeake Bay

Gillary¹

2005

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________________________________________(signature) ________________________ (date) USNA-1531-2 REPORT DOCUMENTATION PAGE Form Approved OMB No. 074-0188Public reporting burden for this collection of information is estimated to average 1 hour per response, including g the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. This document has been approved for public release; its distribution is UNLIMITED. 12b. DISTRIBUTION CODE ABSTRACT:The purpose of this project was to find the normal modes for a mathematical model of the Chesapeake Bay geometry.The method used, normal mode analysis, was similar to that of and Lipphardt et al. [2000]. Normal mode analysis uses a truncated basis set of velocity fields to approximate the flow for a specific body of water. The approach taken in this project uses the three modes described by for application to Monterey Bay with one mode corresponding to flows with streamline potentials, one mode to flows with velocity potentials and an inhomogeneous mode which takes into account forcing functions at the boundaries. In practice linear combinations of these three normal modes are used to provide a complete picture of the flows in a specific body of water from limited amounts of empirical or model data. The ability to accurately fill in partial empirical velocity fields can be used to provide the military with current data in coastal waters for mission planning or navigation. This approach is also useful for studying the spread of wet life in a body of water. There is no analytical solution for the normal mode equations with a boundary as complicated as the Chesapeake Bay, which has 11,684 miles of shoreline but is only 189 miles long and 30 miles wide. Therefore, the normal modes have been calculated using a finite differencing method in MATLAB® alongside the finite element based program FEMLAB®. Convergence and accuracy of the solutions were first tested on the square, the circle and the equilateral triangle geometries, then the normal mode equations were solved for a representation of the Chesapeake Bay. This project has produced two useful products: the normal modes of the Chesapeake Bay and open source MATLAB® code that uses the finite difference method. . The purpose of this project was to find the normal modes for a mathematical model of the Chesapeake Bay geometry. The method used, normal mode analysis, was similar to that of and Lipphardt et al. [2000]. Normal mode analysis uses a truncated basis set of velocity fields to approximate the flow for a specific body of water. The approach taken in this project uses the three modes described by for application to Monterey NUMBER OF PAGESBay with one mode corresponding to flows with streamline potentials, one mode to flows with velocity potentials and an inhomogeneous mode which takes into account forcing functions at the boundaries. In practice linear combinations of these three normal modes are used to provide a...

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Blending HF radar and model velocities in Monterey Bay through normal mode analysis

Cited by 71 publications

References 13 publications

Evaluation of two algorithms for a network of coastal HF radars in the Mid-Atlantic Bight

Evaluation of two algorithms for a network of coastal HF radars in the Mid-Atlantic Bight

Lagrangian predictability of high-resolution regional models: the special case of the Gulf of Mexico

Normal Mode Analysis of the Chesapeake Bay

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