In this study we use the ordinal logistic regression method to establish a prediction model, which estimates the probability for each solar active region to produce X-, M-, or Cclass flares during the next 1-day time period. The three predictive parameters are (1) the total unsigned magnetic flux T flux , which is a measure of an active region's size, (2) the length of the strong-gradient neutral line L gnl , which describes the global nonpotentiality of an active region, and (3) the total magnetic dissipation E diss , which is another proxy of an active region's nonpotentiality. These parameters are all derived from SOHO MDI magnetograms. The ordinal response variable is the different level of solar flare magnitude. By analyzing 174 active regions, L gnl is proven to be the most powerful predictor, if only one predictor is chosen. Compared with the current prediction methods used by the Solar Monitor at the Solar Data Analysis Center (SDAC) and NOAA's Space Weather Prediction Center (SWPC), the ordinal logistic model using L gnl , T flux , and E diss as predictors demonstrated its automatic functionality, simplicity, and fairly high prediction accuracy. To our knowledge, this is the first time the ordinal logistic regression model has been used in solar physics to predict solar flares.

Using line-of-sight Michelson Doppler Imager (MDI ) magnetograms of 89 active regions and Solar Geophysical Data (SGD) flare reports, we explored, for the first time, the magnitude scaling correlations between three parameters of magnetic fields and the flare productivity of solar active regions. These parameters are (1) the mean value of spatial magnetic gradients at strong-gradient magnetic neutral lines, (9B z ) NL ; (2) the length of strong-gradient magnetic neutral lines, L GNL ; and (3) the total magnetic energy, R (B z ) dA, dissipated in a layer of 1 m during 1 s over the active region's area. The MDI magnetograms of active regions used for our analysis are close to the solar central meridian (within AE10 ). The flare productivity of active regions was quantified by the soft X-ray flare index for different time windows from the time interval of the entire disk passage down to +1 day from the time of the analyzed magnetogram. Our results explicitly indicate positive correlations between the parameters and the overall flare productivity of active regions, and imminent flare production as well. The correlations confirm the dependence of flare productivity on the degree of nonpotentiality of active regions.

Frontside halo coronal mass ejections (CMEs) are generally considered as potential candidates for producing geomagnetic storms, but there was no definite way to predict whether they will hit the Earth or not. Recently Moon et al. suggested that the degree of CME asymmetries, as defined by the ratio of the shortest to the longest distances of the CME front measured from the solar center, be used as a parameter for predicting their geoeffectiveness. They called this quantity a direction parameter, D, as it suggests how much CME propagation is directed to Earth, and examined its forecasting capability using 12 fast halo CMEs. In this paper, we extend this test by using a much larger database (486 frontside halo CMEs from 1997 to 2003) and more robust statistical tools (contingency table and statistical parameters). We compared the forecast capability of this direction parameter to those of other CME parameters, such as location and speed. We found the following results: (1) The CMEs with large direction parameters (D ! 0:4) are highly associated with geomagnetic storms. (2) If the direction parameter increases from 0.4 to 1.0, the geoeffective probability rises from 52% to 84%. (3) All CMEs associated with strong geomagnetic storms ( Dst À200 nT) are found to have large direction parameters (D ! 0:6). (4) CMEs causing strong geomagnetic storms (Dst À100 nT), in spite of their northward magnetic field, have large direction parameters (D ! 0:6). (5) Forecasting capability improves when statistical parameters (e.g., ''probability of detection -yes'' and ''critical success index'') are employed, in comparison with the forecast solely based on the location and speed of CMEs. These results indicate that the CME direction parameter can be an important indicator for forecasting CME geoeffectiveness. Subject headingg s: solar-terrestrial relations -Sun: coronal mass ejections (CMEs)

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