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J W Gjerloev - One of the best experts on this subject based on the ideXlab platform.
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an empirical Orthogonal Function reanalysis of the northern polar external and induced magnetic field during solar cycle 23
arXiv: Space Physics, 2018Co-Authors: Robert Shore, M P Freeman, J W GjerloevAbstract:We apply the method of data-interpolating empirical Orthogonal Functions (EOFs) to ground-based magnetic vector data from the SuperMAG archive to produce a series of month length reanalyses of the surface external and induced magnetic field (SEIMF) in 110,000 km$^{2}$ equal-area bins over the entire northern polar region at 5 min cadence over solar cycle 23, from 1997.0 to 2009.0. Each EOF reanalysis also decomposes the measured SEIMF variation into a hierarchy of spatiotemporal patterns which are ordered by their contribution to the monthly magnetic field variance. We find that the leading EOF patterns can each be (subjectively) interpreted as well-known SEIMF systems or their equivalent current systems. The relationship of the equivalent currents to the true current flow is not investigated. We track the leading SEIMF or equivalent current systems of similar type by intermonthly spatial correlation and apply graph theory to (objectively) group their appearance and relative importance throughout a solar cycle, revealing seasonal and solar cycle variation. In this way, we identify the spatiotemporal patterns that maximally contribute to SEIMF variability over a solar cycle. We propose this combination of EOF and graph theory as a powerful method for objectively defining and investigating the structure and variability of the SEIMF or their equivalent ionospheric currents for use in both geomagnetism and space weather applications. It is demonstrated here on solar cycle 23 but is extendable to any epoch with sufficient data coverage.
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an empirical Orthogonal Function reanalysis of the northern polar external and induced magnetic field during solar cycle 23
Journal of Geophysical Research, 2018Co-Authors: Robert Shore, M P Freeman, J W GjerloevAbstract:A netcdf-formatted file containing the original binned data (described in Shore et al [2017], doi pending), in their state before they were subjected to EOF analysis. These have had additional processing applied to the SuperMAG data (publically available at http://supermag.jhuapl.edu/) in the form of sampling them to the centroid of the bins, thus they are worth providing here despite the large file size (approximately 12GB). To conserve file space, we have removed empty bins, thus the temporal and spatial basis for these data are provided for each filled bin element. Please note that the binned data had not had the temporal mean values (described in Shore et al [2017], doi pending, and available in the Supporting Information) removed when they were stored in this netcdf file. The file contains 144 (monthly) sets of 8 variables. These variables are named: 1: filled_bin_data_YYYYMM_r 2: filled_bin_data_YYYYMM_theta 3: filled_bin_data_YYYYMM_phi Variables 1 to 3 contain the nanoTesla vales of the binned data for each of the three magnetic field components in the Quasi-Dipole frame. 4: filled_bin_contrib_stations_YYYYMM The three-letter SuperMAG acronym of the station which contributed to each 5-minute mean data point. 5: filled_bin_colats_YYYYMM 6: filled_bin_longs_YYYYMM Variables 5 and 6 are the co-latitude and longitude coordinates of each filled bin element. 7: filled_bin_times_YYYYMM The 5-minute-mean epoch of each filled bin element, with columns in the order: year, month, day, hour, minute, second). 8: filled_bin_indices_YYYYMM A set of fiducial values describing how the sparse elements of the 1D vector of filled bin values relate to the fiducials of the (transposed!) EOF prediction a 2D matrix product of the spatial and temporal eigenvectors with values in every bin. An example of the usage of these data is given in the MATLAB program Shore-ms01.m, provided in the Supporting Information of Shore et al [2017], (doi pending). This VERSION 2.0 data set has been corrected for a bug which led to the bins which span the local midnight meridian having fewer samples than they should. The data density in these bins is now in-line with the rest of the polar coverage. Apart from that change, the original and updated data sets are the same.
Tomoko Matsuo - One of the best experts on this subject based on the ideXlab platform.
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modes of high latitude auroral conductance variability derived from dmsp energetic electron precipitation observations empirical Orthogonal Function analysis
Journal of Geophysical Research, 2015Co-Authors: Ryan Mcgranaghan, Tomoko Matsuo, D J Knipp, Humberto C Godinez, R J Redmon, S C Solomon, S K MorleyAbstract:We provide the first ever characterization of the primary modes of ionospheric Hall and Pedersen conductance variability as empirical Orthogonal Functions (EOFs). These are derived from six satellite years of Defense Meteorological Satellite Program (DMSP) particle data acquired during the rise of solar cycles 22 and 24. The 60 million DMSP spectra were each processed through the Global Airlglow Model. Ours is the first large-scale analysis of ionospheric conductances completely free of assumption of the incident electron energy spectra. We show that the mean patterns and first four EOFs capture ∼50.1 and 52.9% of the total Pedersen and Hall conductance variabilities, respectively. The mean patterns and first EOFs are consistent with typical diffuse auroral oval structures and quiet time strengthening/weakening of the mean pattern. The second and third EOFs show major disturbance features of magnetosphere-ionosphere (MI) interactions: geomagnetically induced auroral zone expansion in EOF2 and the auroral substorm current wedge in EOF3. The fourth EOFs suggest diminished conductance associated with ionospheric substorm recovery mode. We identify the most important modes of ionospheric conductance variability. Our results will allow improved modeling of the background error covariance needed for ionospheric assimilative procedures and improved understanding of MI coupling processes.
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principal modes of thermospheric density variability empirical Orthogonal Function analysis of champ 2001 2008 data
Journal of Geophysical Research, 2010Co-Authors: Tomoko Matsuo, J M ForbesAbstract:[1] In this paper we characterize the dominant modes of global thermosphere density variability as empirical Orthogonal Functions (EOFs) using densities obtained from the accelerometer experiment on board the CHAMP satellite during 2001–2008. We determine the significance of different types of thermospheric density variability to the overall density variation and also examine the drivers of these primary modes of variability. From a sequential nonlinear regression analysis of the density observations along satellite trajectories, we obtain a set of EOFs in magnetic latitude and magnetic local time coordinates and their orbit time-dependent amplitudes. EOF1 includes a strong global mean component and takes the form of the diurnal variation. It correlates highly with the daily F10.7 index. It underscores that solar EUV is by far the strongest driver of the overall thermospheric density variability. Additionally, the primary mode is modulated semiannually, and its magnitude decreases with declining solar activity. EOF2 has a hemispherically asymmetric structure and represents the summer-to-winter annual density variation. Density responses to geomagnetic forcing are primarily manifested in three different modes: EOF1 that represents the global mean response, EOF3 that has a pronounced magnetic local time-dependent feature, and EOF4, whose main features are high-latitude density increases. These modes have very different response time scales with respect to changes in solar wind parameters. In addition, EOF3 and EOF4 contain signatures of local time variability that are possibly connected with the effects of a solar terminator wave and high-order solar tides that propagate upward from the lower atmosphere and/or with local plasma-neutral interactions in the F region.
A Ercha - One of the best experts on this subject based on the ideXlab platform.
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a global model empirical Orthogonal Function analysis of total electron content 1999 2009 data
Journal of Geophysical Research, 2012Co-Authors: Donghe Zhang, Zuo Xiao, A Ercha, A J Ridley, Yongqiang HaoAbstract:[1] A global ionospheric total electron content (TEC) model based on the empirical Orthogonal Function (EOF) analysis method is constructed using the global ionosphere maps provided by Jet Propulsion Laboratory during the years 1999–2009. The importance of different types of variation to the overall TEC variability as well as the influence of solar radiation and geomagnetic activity toward TEC can be well represented by the characteristics of EOF base Functions Ek and associated coefficients Pk. The quick convergence of EOF decomposition makes it possible to use the first four orders of the EOF series to represent 99.04% of the overall variance of the original data set. E1 represents the essential feature of global spatial and diurnal variation of the TEC. E2 contains a hemispherically asymmetric pattern manifesting the summer-to-winter annual variation. E3 and E4 can well reflect the equatorial anomaly phenomenon. P1 contains an obvious solar cycle variation pattern as well as annual and semiannual variation components. P2 mainly includes an annual fluctuation component. P3 has a strong annual variation and a weak seasonal variation pattern. P4 has both evident annual and semiannual oscillation components. The Fourier series as a combination of trigonometric and linear Functions are used to represent the solar cycle, annual, and semiannual variation of the coefficients. Therefore the global TEC model is established through incorporating the modeled EOF series. The accuracy and quality of the model have been validated through the model-data comparison, which indicates that the model can reflect the majority of the variations and the feature of temporal-spatial distribution of the global ionospheric TEC.
Robert G Dean - One of the best experts on this subject based on the ideXlab platform.
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shoreline variability via empirical Orthogonal Function analysis part ii relationship to nearshore conditions
Coastal Engineering, 2007Co-Authors: Jon K Miller, Robert G DeanAbstract:The method of empirical Orthogonal Function (EOF) or principal component analysis (PCA) was used to investigate the spatial and temporal variability of shoreline data sets from Duck, North Carolina, the Gold Coast, Australia, and the United States Pacific Northwest. In the present work, an attempt is made to relate the individual modes of shoreline variability identified by the EOF analyses to select parameterizations of the nearshore environment. The parameters considered include the wave energy (E), the cross-shore and longshore wave energy fluxes (Fx and Fy), the wave steepness (Ho/Lo), the non-dimensional fall velocity parameter (Ω), the profile parameter (P), the surf-similarity parameter (ζ), and a surfzone Froude number (Fr). Correlation analyses were used to evaluate the linear relationship between each of these parameters and the temporal eigenFunctions, ck(t), associated with individual modes of shoreline change. Typically, strong correlations were observed between longshore uniform modes and the monthly means of several of the nearshore parameters.
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shoreline variability via empirical Orthogonal Function analysis part i temporal and spatial characteristics
Coastal Engineering, 2007Co-Authors: Jon K Miller, Robert G DeanAbstract:Empirical Orthogonal Functions (EOFs) or principal components were used to extract the significant modes of shoreline variability from several data sets collected at three very different locations. Although EOFs have proven to be a valuable tool in the analysis of nearshore data, most applications have focused on the ability of the technique to describe cross-shore or profile variability. Here however, EOFs were used to help identify the dominant modes of longshore shoreline variability at Duck, North Carolina, the Gold Coast, Australia, and at several locations within the Columbia River Littoral Cell in the U.S. Pacific Northwest. In part one of this analysis, characteristic patterns of shoreline variability identified by the EOF analysis are described in detail. At each site, the dominant modes consisting of the first four eigenFunctions were found to describe nearly 95% of the total shoreline variability. At both Duck and the Gold Coast, several interesting longshore periodic features suggestive of sand waves were identified, while boundary effects related to natural headlands and navigational structures/entrances dominated the Pacific Northwest data sets.
Robert Shore - One of the best experts on this subject based on the ideXlab platform.
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an empirical Orthogonal Function reanalysis of the northern polar external and induced magnetic field during solar cycle 23
arXiv: Space Physics, 2018Co-Authors: Robert Shore, M P Freeman, J W GjerloevAbstract:We apply the method of data-interpolating empirical Orthogonal Functions (EOFs) to ground-based magnetic vector data from the SuperMAG archive to produce a series of month length reanalyses of the surface external and induced magnetic field (SEIMF) in 110,000 km$^{2}$ equal-area bins over the entire northern polar region at 5 min cadence over solar cycle 23, from 1997.0 to 2009.0. Each EOF reanalysis also decomposes the measured SEIMF variation into a hierarchy of spatiotemporal patterns which are ordered by their contribution to the monthly magnetic field variance. We find that the leading EOF patterns can each be (subjectively) interpreted as well-known SEIMF systems or their equivalent current systems. The relationship of the equivalent currents to the true current flow is not investigated. We track the leading SEIMF or equivalent current systems of similar type by intermonthly spatial correlation and apply graph theory to (objectively) group their appearance and relative importance throughout a solar cycle, revealing seasonal and solar cycle variation. In this way, we identify the spatiotemporal patterns that maximally contribute to SEIMF variability over a solar cycle. We propose this combination of EOF and graph theory as a powerful method for objectively defining and investigating the structure and variability of the SEIMF or their equivalent ionospheric currents for use in both geomagnetism and space weather applications. It is demonstrated here on solar cycle 23 but is extendable to any epoch with sufficient data coverage.
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an empirical Orthogonal Function reanalysis of the northern polar external and induced magnetic field during solar cycle 23
Journal of Geophysical Research, 2018Co-Authors: Robert Shore, M P Freeman, J W GjerloevAbstract:A netcdf-formatted file containing the original binned data (described in Shore et al [2017], doi pending), in their state before they were subjected to EOF analysis. These have had additional processing applied to the SuperMAG data (publically available at http://supermag.jhuapl.edu/) in the form of sampling them to the centroid of the bins, thus they are worth providing here despite the large file size (approximately 12GB). To conserve file space, we have removed empty bins, thus the temporal and spatial basis for these data are provided for each filled bin element. Please note that the binned data had not had the temporal mean values (described in Shore et al [2017], doi pending, and available in the Supporting Information) removed when they were stored in this netcdf file. The file contains 144 (monthly) sets of 8 variables. These variables are named: 1: filled_bin_data_YYYYMM_r 2: filled_bin_data_YYYYMM_theta 3: filled_bin_data_YYYYMM_phi Variables 1 to 3 contain the nanoTesla vales of the binned data for each of the three magnetic field components in the Quasi-Dipole frame. 4: filled_bin_contrib_stations_YYYYMM The three-letter SuperMAG acronym of the station which contributed to each 5-minute mean data point. 5: filled_bin_colats_YYYYMM 6: filled_bin_longs_YYYYMM Variables 5 and 6 are the co-latitude and longitude coordinates of each filled bin element. 7: filled_bin_times_YYYYMM The 5-minute-mean epoch of each filled bin element, with columns in the order: year, month, day, hour, minute, second). 8: filled_bin_indices_YYYYMM A set of fiducial values describing how the sparse elements of the 1D vector of filled bin values relate to the fiducials of the (transposed!) EOF prediction a 2D matrix product of the spatial and temporal eigenvectors with values in every bin. An example of the usage of these data is given in the MATLAB program Shore-ms01.m, provided in the Supporting Information of Shore et al [2017], (doi pending). This VERSION 2.0 data set has been corrected for a bug which led to the bins which span the local midnight meridian having fewer samples than they should. The data density in these bins is now in-line with the rest of the polar coverage. Apart from that change, the original and updated data sets are the same.
