Eric A. Kihn

Universal time variations of the auroral electrojet indices

Using the hourly mean AE indices for the past 20 years, amounting to a total of 175,296 hours, we examine how the longitudinal station gaps of the present AE network affect the ability to monitor accurately the auroral electrojets. The latitudinal shift of the auroral electrojet location with magnetic activity also affects the reliability of the AE indices. These combined effects would result in pronounced universal time (UT) variations of the AE indices. By counting the number of occurrences recorded during the given ranges of activity, say every 100 and 200 nT for the AU and AL indices, respectively, for each hour of universal time, the UT variations of the two indices are examined separately. The result demonstrates clearly that they are strongly dependent upon UT. Furthermore, it is noted that the equatorward expansion of the auroral electrojets is more responsible for the UT variation than are the longitudinal station gaps. For the range of the magnetic activity levels examined in this study, i.e., 0 to 500 nT and 0 to -1000 nT for the AU and AL indices, the centers of the eastward and westward electrojets seem to be located within the latitudinal ranges of 71°-65° and 68°-62°, respectively. The seasonal change of ionospheric conductance also contributes to the UT variation, particularly that of the AL index. While maintaining a similar variation pattern, the amplitude of the variation increases during winter and decreases during summer. It indicates that the UT variation of the AL index is more serious during winter than summer. With more AE stations being located within the former range than the latter, it is easily understood why the AL index is more strongly dependent on UT than is the AU index. Considering such a latitudinal distribution, it is highly probable that the present AL indices often underestimate disturbed conditions during specific universal time intervals, particularly 0200-0800 UT.

Seasonal and solar cycle variations of the auroral electrojet indices

Abstract Using hourly mean auroral electrojet indices for the past 20 years, we examine the seasonal and solar cycle variations of the AU and AL indices as well as the smaller time-scale fluctuations in these indices. The AU and AL indices maximize during summer and equinoctial months, respectively. By removing the effects of the solar conductance from the AU index, it is found that the electric field contribution to the AU index exhibits the same semiannual variation pattern as the AL index, indicating that the semiannual magnetic variations are controlled by the electric field. Since the auroral electrojets are mostly Hall currents flowing in the east–west direction, the fluctuations of the auroral electrojet indices can be interpreted in terms of fluctuations in the north–south component of the electric field and the Hall conductance. The AU fluctuation is largely due to that of the electric field, while the AL fluctuation is attributed to both the electric field and Hall conductance with their contributions being comparable. The high fluctuation of AL compared to that of AU is attributed to particle precipitation associated with substorm activity. However, the fluctuations of the electric field and conductance do not show any noticeable seasonal dependence. The variation pattern of the yearly mean AL index follows the mirror image of the AU index during the past 20 years, indicating that the absolute values of the two indices are proportional to each other. This suggests again that the electric field is the main modulator of magnetic disturbance. On the other hand, they show a tendency to become higher during the declining phase of the solar cycle. This is the same variation pattern confirmed from the aa index. However, the fluctuations of the electric field and the Hall conductance do not show any apparent dependence on the solar cycle.

A statistical analysis of the assimilative mapping of ionospheric electrodynamics auroral specification

[1] The assimilative mapping of ionospheric electrodynamics (AMIE) technique utilizes a wide range of electrodynamics measurements to determine high-latitude maps of the electric potential, electron particle precipitation (average energy and total energy flux), and ionospheric conductance (Hall and Pedersen). AMIE does this by conducting a least squares fit to the difference between the data and a background model. This fit is then added to the background model. This allows for a very stable technique with even minimal amounts of data. The background models are typically statistical models that are driven by the solar wind and interplanetary magnetic field or the hemispheric power index. This study presents results of a statistical validation of the AMIE conductance and particle precipitation calculations and quantifies how using ground magnetometer derived measurements improves upon the result obtained using only a background statistical model. Specifically, we compare AMIE using the Fuller-Rowell and Evans (1987) model of particle precipitation and ionospheric conductances to DMSP particle precipitation measurements during the period from May to November 1998. The conductances are derived from the particle precipitation using the Robinson et al. (1987) formulation. The Fuller-Rowell and Evans (1987) results show low (39-21% with increasing AE) energy flux integrals with respect to DMSP auroral passes and differences in mean electron energies. The AMIE runs, in which ground-based magnetometers were used to modify the particle precipitation using the formulation by Ahn et al. (1983) and Ahn et al. (1998), show significant improvement in correlation to the observational data. We show that it more accurately predicts the particle precipitation than when using only the background model, especially in the 1800-0300 MLT nightside sectors where solar conductance is not significant. In addition, the AMIE results show a clear increase in accuracy with increasing number of magnetometers in a sector.

Space Physics Interactive Data Resource—SPIDR

SPIDR (Space Physics Interactive Data Resource) is a standard data source for solar-terrestrial physics, functioning within the framework of the ICSU World Data Centers. It is a distributed database and application server network, built to select, visualize and model historical space weather data distributed across the Internet. SPIDR can work as a fully-functional web-application (portal) or as a grid of web-services, providing functions for other applications to access its data holdings.

Structural indexing of satellite images using texture feature extraction for retrieval

A mixture of feature extraction (FE) and a Locality Sensitive Hashing (LSH) based searching algorithm to search for similarity in satellite imagery is presented. The goal is to build an accurate and fast query-by-example using content based image retrieval based on the information extracted from satellite image data. We have investigated and described various feature extraction methods relevant to our work in this paper. The experimental results demonstrate satisfactory retrieval efficiency based on the proposed model. The results show the effectiveness of our approach. 1 2

Real-time specifications of the Geospace Environment

We report the recent progress in our joint program of real-time mapping of ionospheric electric fields and currents and field-aligned currents through the Geospace Environment Data Analysis System (GEDAS) at the Solar-Terrestrial Environment Laboratory and similar computer systems in the world. Data from individual ground magnetometers as well as from the solar wind are collected by these systems and are used as input for the KRM and AMIE magnetogram-inversion algorithms, which calculate the two-dimensional distribution of the ionospheric parameters. One of the goals of this program is to specify the solar-terrestrial environment in terms of ionospheric processes, providing the scientific community with more than what geomagnetic activity indices and statistical models provide.

Satellite image retrieval application using Locality Sensitive Hashing in l 2 -space

This paper demonstrates the use of the Locality Sensitive Hashing technique operating in Euclidean metric space to build a data structure for Defense Meteorological Satellite Program (DMSP) satellite imagery database. Due to the high dimensionality of these images, their texture feature vectors are used. These features are extracted using pyramidal wavelet decomposition coupled with Gaussian central moments. Families of hash functions are drawn randomly and independently from a Gaussian distribution to create hash tables for these texture feature vectors of the images. The hash tables and the families of hash functions are then used to find similar satellite image matches to any query image in sublinear search time. When tested, our algorithm has proven to be about thirty three times faster than the linear search algorithm. In addition, our algorithm searches less than two percent of the entire database on the average to find the possible similar image matches to any given query without loss of accuracy. 1 2

Locality Sensitive Hashing for satellite images using texture feature vectors

This paper demonstrates the use of modified Locality Sensitive Hashing (mLSH) technique with Euclidean distance space to build a data structure for Defense Meteorological Satellite Program (DMSP) satellite imagery database that can be used to find similar satellite image matches in sublinear search time. Given the texture feature vectors of the images extracted using Gaussian central moments of wavelet edges after multi-resolution decomposition, a one-time linked-list hash table is created. A family of hash functions is drawn randomly and independently from a Gaussian distribution with mean zero and a standard deviation, d (i.e. dimensionality of the image feature vectors) to create the hash table. When tested, our algorithm has proved to be at least twenty times faster than the linear search algorithm. In addition, the algorithm ensures that the percentage of the entire database searched to find possible matches to any given query falls below ten percent. 1 2

Satellite image retrieval using low memory locality sensitive hashing in Euclidean space

This paper presents the use of the Low Memory Locality Sensitive Hashing (LMLSH) technique operating in Euclidean space to build a data structure for the Defense Meteorological Satellite Program (DMSP) satellite imagery database. The LMLSH technique finds satellite image matches in sublinear search time. The texture feature vectors of the images are extracted using pyramid-structured wavelet transform coupled with Gaussian central moment technique. These feature vectors and families of hash functions, drawn randomly and independently from a Gaussian distribution, are used to build hash tables. Given a query, the hash tables are used to pull out the best matches to that query and this is done in a sublinear search time complexity. When tested, our algorithm has proven to be approximately twenty six times faster than the Linear Search (LS) algorithm. In addition, the LMLSH algorithm searches about two percent of the entire database randomly to find the possible matches to any given query without loss of accuracy compared to the absolute best matches returned by its LS counterpart.

Detecting Environmental Change Using Self-Organizing Map Techniques Applied to the ERA-40 Database

Data mining is a valuable tool in meteorological applications. Properly selected data mining techniques enable researchers to process and analyze massive amounts of data collected by satellites and other instruments. Large spatial-temporal datasets can be analyzed using different linear and nonlinear methods. The Self-Organizing Map (SOM) is a promising tool for clustering and visualizing high dimensional data and mapping spatial-temporal datasets describing nonlinear phenomena. We present results of the application of the SOM technique in regions of interest within the European re-analysis data set. The possibility of detecting climate change signals through the visualization capability of SOM tools is examined.