An empirical analysis of solar wind‐magnetosphere energy coupling functions is reported. Using the technique of linear prediction filtering with 2.5 minute data, we examine the relationship of auroral zone geomagnetic activity to solar wind power input functions which depend on the solar wind quantities VB², VBs, or V²Bs. In this analysis a least squares prediction filter or impulse response function which relates a solar wind power function to an auroral zone geomagnetic index is designed directly from the data. We find that the computed impulse response functions have the characteristics of a low pass filter with a time delay which may be dependent on the strength of the energy input. While the AL index is reasonably well related to the solar wind energy functions, the AU index shows a substantially poorer relationship. In addition, high frequency variations of the auroral indices and some substorm expansions are not predictable with solar wind information alone, suggesting that internal magnetospheric processes partially control the AL index. We also find that the ϵ parameter which depends on VB² in the solar wind has a poorer relationship to auroral zone geomagnetic activity than a power parameter having a VBs solar wind dependence.

The technique of empirical linear prediction filters is used to investigate the extent to which the low latitude dawn‐dusk magnetic asymmetry is controlled by the dawn‐dusk solar wind motional electric field VBs and/or by substorm processes measured by the westward auroral electrojet index AL. The dawn‐dusk asymmetry is measured by a new index defined as the difference between dawn and dusk deviations in the X (geomagnetic Northward) magnetic field component. The empirically determined filters obtained from this analysis provide quantitative information which characterizes the coupling processes. For example, the VBs to AL filter is a delayed pulse beginning after a delay of about 15 minutes, peaking at 60 minutes and returning to zero at 120 min. The filter has the characteristics of a low pass filter with a cutoff frequency at 10−4 Hz. The VBs to ASYM filter is also a delayed pulse with similar constants. The VBs to ASYM filter, however, also has a long tail which gradually decays reaching zero near 5 or 6 hours lag time. The AL to ASYM filter is acausal, being nonzero at future lag times. The peak of the filter occurs at a delay of about 10 min. and decays exponentially becoming nearly zero after 4 hours lag. Our results indicate that some currents are directly driven by the solar wind‐magnetosphere interaction and that their magnetic perturbations contribute to both the AL and ASYM indices. A portion of the AL index that is uncorrelated with VBs is, however, correlated with ASYM suggesting that internal magnetospheric processes contribute to AL and ASYM as well.

High‐precision geomagnetic measurement depends on eliminating variations of ionospheric and magnetospheric origin. The commonly used technique of simply taking differences between total field magnetometers is only partially successful. Our method involves finding the multichannel Wiener filters which predict the field variation at a given total field magnetometer of an array from the fields of the remaining magnetometers and a three‐component magnetometer. The difference is then taken between the total field and the predicted field, leaving a cleaned total field. Filter lengths and number and choice of input channels are determined using methods of statistical parametric model fitting. The resulting filters which are defined on short noise free record sections are found to be effective over the remainder of the record. Detailed analysis of data from the University of California, Los Angeles, array in southern California, for which we have the best vector data, shows that for optimal predictive cleaning it is essential to use vector components and that only the two components orthogonal to the total field direction are necessary. This means that the field‐aligned component may be omitted from a vector station, which simplifies the instrumentation considerably. Analysis of the standard deviation of the residuals after cleaning (0.1 nT for hourly averages) shows that it is 8 times that expected from digitization noise alone. The most probable explanation for this is that measurement noise is about 3 times the digitization interval (0.25 nT) and that higher‐precision measurements are required for further improvement. This conclusion has been confirmed by tests on the instruments at close spacing. Application of the method to data taken in central California and Hawaii reveals tectonomagnetic effects which are otherwise hidden in noise. Wiener filters are especially suitable for real‐time analysis, which is an important factor in earthquake prediction.

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