Statistical analyses were performed to investigate the relative success and accuracy of daily maximum X-ray flux (MXF) predictions, using both multilinear regression and autoregressive time-series prediction methods. As input data for this work, we used 14 solar activity parameters recorded over the prior 2 year period (1989-1990) during the solar maximum of cycle 22. We applied the multilinear regression method to the following three groups: all 14 variables (G1), the 2 so-called 'cause' variables (sunspot complexity and sunspot group area) showing the highest correlations with MXF (G2), and the 2 'effect' variables (previous day MXF and the number of flares stronger than C4 class) showing the highest correlations with MXF (G3). For the advanced three days forecast, we applied the autoregressive timeseries method to the MXF data (GT). We compared the statistical results of these groups for 1991 data, using several statistical measures obtained from a 2x2 contingency table for forecasted versus observed events. As a result, we found that the statistical results of G1 and G3 are nearly the same each other and the 'effect' variables (G3) are more reliable predictors than the 'cause' variables. It is also found that while the statistical results of GT are a little worse than those of G1 for relatively weak flares, they are comparable to each other for strong flares. In general, all statistical measures show good predictions from all groups, provided that the flares are weaker than about M5 class; stronger flares rapidly become difficult to predict well, which is probably due to statistical inaccuracies arising from their rarity. Our statistical results of all flares except for the X-class flares were confirmed by Yates' X2 statistical significance tests, at the 99% confidence level. Based on our model testing, we recommend a practical strategy for solar X-ray flare predictions.

We have developed a two fluid solar wind model from the Sun to 1 AU. Its basic equations are mass, momentum and energy conservations. In these equations, we include a wave mechanism of heating the corona and accelerating the wind. The two fluid model takes into account the power spectrum of Alfvenic wave fluctuation. Model computations have been made to fit observational constraints such as electron(Te) and proton(Tp) temperatures and solar wind speed(V) at 1 AU. As a result, we obtained physical quantities of solar wind as follows: Te is 7.4 X 10.5 K and density(n) is 1.7 X 107 cm-3 in the corona. At 1 AU Te is 2.1 X 105 K and n is 0.3 cm-3, and V is 511 km s-1. Our model well explains the heating of protons in the corona and the acceleration of the solar wind.