Do non-orthogonally and irregularly sampled scalar velocities contain sufficient information to reconstruct an orthogonal vector current field?

Do non-orthogonally and irregularly sampled scalar velocities contain sufficient information to... AbstractWe address how well a two-dimensional orthogonal vector current field can be reconstructed from a set of non-orthogonally and irregularly sampled scalar velocity data. High-frequency radar (HFR)-derived surface radial scalar velocities are sampled on a polar coordinate grid as a directional projection of two-dimensional vector currents for a viewing angle of the individual HFRs. We generate synthetic radial velocity maps by sampling two-dimensional surface vector currents obtained from a simple spectral model and a realistic regional circulation model on the polar or elliptical grid points configured similarly as the operational HFRs. Then, the sampled radial velocity maps are combined into a vector current field using inverse methods: leastsquares fitting and optimal interpolation. We define uncertainty and misfit as the degrees of insufficiency to resolve the vector current and the difference between the true and estimated vector currents, respectively. We evaluate the uncertainty and misfit in terms of (1) several simulation parameters built into the simple spectral model and (2) the degrees of the quality and the observational error of the radial velocity maps associated with the simulated missing data and noise level, respectively. A greater number of missing data and higher observational errors correspond to an increase in the standard deviation of the misfit and a significant reduction in the effective spatial coverage of the vector current fields. This paper provides technical details for resolving a vector current field and guidelines for the practical design of the spatial sampling of the current field using the HFRs. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Atmospheric and Oceanic Technology American Meteorological Society

Do non-orthogonally and irregularly sampled scalar velocities contain sufficient information to reconstruct an orthogonal vector current field?

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Publisher
American Meteorological Society
Copyright
Copyright © American Meteorological Society
ISSN
1520-0426
D.O.I.
10.1175/JTECH-D-17-0062.1
Publisher site
See Article on Publisher Site

Abstract

AbstractWe address how well a two-dimensional orthogonal vector current field can be reconstructed from a set of non-orthogonally and irregularly sampled scalar velocity data. High-frequency radar (HFR)-derived surface radial scalar velocities are sampled on a polar coordinate grid as a directional projection of two-dimensional vector currents for a viewing angle of the individual HFRs. We generate synthetic radial velocity maps by sampling two-dimensional surface vector currents obtained from a simple spectral model and a realistic regional circulation model on the polar or elliptical grid points configured similarly as the operational HFRs. Then, the sampled radial velocity maps are combined into a vector current field using inverse methods: leastsquares fitting and optimal interpolation. We define uncertainty and misfit as the degrees of insufficiency to resolve the vector current and the difference between the true and estimated vector currents, respectively. We evaluate the uncertainty and misfit in terms of (1) several simulation parameters built into the simple spectral model and (2) the degrees of the quality and the observational error of the radial velocity maps associated with the simulated missing data and noise level, respectively. A greater number of missing data and higher observational errors correspond to an increase in the standard deviation of the misfit and a significant reduction in the effective spatial coverage of the vector current fields. This paper provides technical details for resolving a vector current field and guidelines for the practical design of the spatial sampling of the current field using the HFRs.

Journal

Journal of Atmospheric and Oceanic TechnologyAmerican Meteorological Society

Published: Jan 17, 2018

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