Ocean‐generated magnetic field study based on satellite geomagnetic measurements: 1. An error reduction technique for gridded data

Golubev, Y. N.

doi:10.1029/2011jc007152

Cited by 1 publication

(6 citation statements)

References 26 publications

(32 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The first step in synthetic data processing was to assign the irregularly distributed measurements to a regular grid. The search algorithm [ Golubev , 2011] was applied to the data. First, the coordinates of the data and the coordinates of the grid's nodes were transformed from the coordinate system ENU (east‐north‐up) to the system ECEF (Earth‐centered‐Earth‐fixed).…”

Section: Simulation Studymentioning

confidence: 99%

“…The magnetic field on the grid has an error which, being small for the main field, may exceed the ocean‐generated magnetic field by several orders of magnitude. As the data analysis has shown [ Golubev , 2011], one of the error's sources is a changing satellite orbit. To reduce the error and to improve a signal‐to‐noise ratio, an approach based on a solution of the Helmholtz equation was applied to the gridded data.…”

Section: Signal‐to‐noise Ratio Improvementmentioning

confidence: 99%

“…The error reduction technique was not applied in the simulation study because a constant altitude of 400 km was used. For real data, the error reduction technique must be applied to fields on a grid as an additional and necessary step [ Golubev , 2011].…”

Section: Signal‐to‐noise Ratio Improvementmentioning

confidence: 99%

“…For the first iteration ( n = 1), the gridded fields of b φ , b θ , and b r were used as zero approximations in relationship in order to compute function F. Afterward, ε n is determined as a solution of . The next iteration begins by updating the components of the magnetic field using the following relationships:

b_{φ}^{n} = b_{φ}^{n - 1} + \frac{1}{r sin θ} \frac{\partial ε^{n}}{\partial φ}, b_{θ}^{n} = b_{θ}^{n - 1} + \frac{1}{r} \frac{\partial ε^{n}}{\partial θ}, b_{r}^{n} = b_{r}^{n - 1} + \frac{\partial ε^{n}}{\partial r},

which follows from and where the potential P is assumed to be the potential on a previous iteration and the potential u is the potential on a current iteration (for more details see Golubev [2011]). Once the components of the field are corrected, the next iteration for ε is performed.…”

Section: Signal‐to‐noise Ratio Improvementmentioning

confidence: 99%

“…The magnitude of the OGMF at satellite altitude (∼400 km) comprises 10 −4 of the Earth's main magnetic field, making the data error issue critically important. Therefore, as a first step, a special technique for improving the signal‐to‐noise ratio was developed in Golubev [2011]. The technique, based on a numerical solution of the Laplace (Helmholtz) equation, allows a reduction of the error in the data on a regular spatial grid by up to 6 orders of magnitude and makes satellite geomagnetic measurements useable for studying the relatively weak ocean‐generated magnetic field.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Ocean‐generated magnetic field study based on satellite geomagnetic measurements: 2. Signal inference

Golubev¹

2012

J. Geophys. Res.

Self Cite

View full text Add to dashboard Cite

[1] The work presented here is the second part of an ocean-generated magnetic field study and provides a procedure for inferring the ocean-generated magnetic field from satellite geomagnetic measurements. The procedure was first tested on synthetic data. The simulation employed a hypothetical satellite measuring the magnetic field at an altitude of 400 km. The "measurements" (generated by the CM4 and CHAOS models) included the core field, the lithospheric field, the ionospheric and magnetospheric fields, the secular variation, and the ocean-generated magnetic field. The search algorithm, as proposed in part 1, converts irregular measurements into fields on a regular grid. The filtration procedure is based on the Savitzky-Golay algorithm. The procedure includes four steps providing seven unknown filter parameters. Parameter values were obtained by solving the problem of minimizing the spatially averaged squared residuals between the inferred field and the model field. Then, the parameters were used in the filter to infer the ocean-generated magnetic field that was initially added to the "measurements." The inferred signal, although spatially corrupted and having a smaller magnitude (60% of the magnitude of the initial signal), indicated the presence of magnetic anomalies within the Southern Ocean. The technique was then applied to CHAMP geomagnetic measurements. The result of filtering was clear magnetic anomalies within the Southern Ocean with a spatial character that were close to what models of the ocean-generated magnetic field provided. The magnitude of the inferring signal was 5 nT, the corrected values were 7-8 nT, 1-2 nT larger than the modeled field magnitude. To compare the temporal variability of the inferred field with the variability of sea surface height, ten 10 by 10 areas were selected within the Southern Ocean and the root-mean-square of both SSH and the magnetic field were computed for each area. A comparison of the results indicated a close similarity between SSH and the magnetic field temporal variability, which allowed the identification of the inferred field as the ocean-generated magnetic field.Citation: Golubev, Y. (2012), Ocean-generated magnetic field study based on satellite geomagnetic measurements: 2. Signal inference,

show abstract

Section: Simulation Studymentioning

confidence: 99%

Section: Signal‐to‐noise Ratio Improvementmentioning

confidence: 99%

Section: Signal‐to‐noise Ratio Improvementmentioning

confidence: 99%