Quantitative modeling of the ionospheric response to geomagnetic activity

A physical model of the coupled thermosphere and ionosphere has been used t o determine the accuracy of model predictions of the ionospheric response t o geomagnetic activity, and assess our understanding of the physical proces ses. The physical model is driven by empirical descriptions of the high-lat itude electric field and auroral precipitation, as measures of the strength of the magnetospheric sources of energy and momentum to the upper atmosphe re. Both sources are keyed to the time-dependent TIROS/NOAA auroral power i ndex. The output of the model is the departure of the ionospheric F region from the normal climatological mean. A. 50-day interval towards the end of 1997 has been simulated with the model for two cases. The first simulation uses only the electric fields and auroral forcing from the empirical models , and the second has an additional source of random electric field variabil ity. In both cases, output from the physical model is compared with F-regio n data from ionosonde stations. Quantitative model/data comparisons have be en performed to move beyond the conventional "visual" scientific assessment , in order to determine the value of the predictions for operational use. F or this study, the ionosphere at two ionosonde stations has been studied in depth, one each from the not-them and southern midlatitudes. The model cle arly captures the seasonal dependence in the ionospheric response to geomag netic activity at mid-latitude: reproducing the tendency for decreased ion density in the summer hemisphere and increased densities in winter. In cont rast to the "visual" success of the model, the detailed quantitative compar isons, which are necessary for space weather applications, are less impress ive. The accuracy, or value, of the model has been quantified by evaluating the daily standard deviation, the root-mean-square error, and the correlat ion coefficient between the data and model predictions. The modeled quiet-t ime variability, or standard deviation, and the increases during geomagneti c activity, agree well with the data in winter, but is low in summer. The R MS error of the physical model is about the same as the IRT. empirical mode l during quiet times. During the storm events the RLMS error of the model i mproves on IRI, but there are occasionally false-alarms. Using unsmoothed d ata over the full interval, the correlation coefficients between the model and data are low, between 0.3 and 0.4. Isolating the storm intervals increa ses the correlation to between 0.43 and 0.56, and by smoothing the data the values increases up to 0.65. The study illustrates the substantial differe nce between scientific success and a demonstration of value for space weath er applications.