We have developed an algorithm for tracking coronal mass ejection (CME) propagation that allows us to estimate CME speed and its arrival time at Earth. The algorithm may be used either to forecast the CME’s arrival on the day of the forecast or to update the CME tracking information for the next day’s forecast. In our case study, we successfully tracked CME propagation using the algorithm based on g-values of interplanetary scintillation (IPS) observation provided by the Institute for Space- Earth Environmental Research (ISEE). We were able to forecast the arrival time (Δt = 0.30 h) and speed (Δv = 20 km/s) of a CME event on October 2, 2000. From the CME-interplanetary CME (ICME) pairs provided by Cane & Richardson (2003), we selected 50 events to evaluate the algorithm’s forecast capability. Average errors for arrival time and speed were 11.14 h and 310 km/s, respectively. Results demonstrated that g-values obtained continuously from any single station observation were able to be used as a proxy for CME speed. Therefore, our algorithm may give stable daily forecasts of CME position and speed during propagation in the region of 0.2–1 AU using the IPS g-values, even if IPS velocity observations are insufficient. We expect that this algorithm may be widely accepted for use in space weather forecasting in the near future.

We examine the effects of the complexity of tax-related information on the issuance of analyst’s pre-tax income forecast and its value relevance. If analysts respond adequately to the needs of investors, they are more likely to provide a pre-tax income forecast. The provision of a pre-tax income forecast may indicate analysts’ confidence in assessing the quality of earnings. Thus, investors, in turn, would be more confident in the analysts’ pre-tax income forecasts if analysts provide both pre-tax and earnings forecasts than only the latter. Using a sample of Korean listed companies for 2005–2014, we find that analysts are likely to provide an implicit tax forecast when the volatility of the effective tax rate is low and the book-tax differences are small. We also find that when analysts provide pretax and after tax income forecasts, the value relevance for unexpected earnings increases. These results indicate that analysts are likely to be interested in corporate tax information and the complexity of tax-related information affects the availability of implicit tax forecasts. Furthermore, this study provides empirical evidence that when analysts provide both pre-tax and after tax income forecasts, investors have more confidence in analysts’ earnings forecasts, which results in greater investors’ responses.

In this study, instead of economic estimation of forecast value, we evaluated the value score(VS) of the user satisfaction using the concept of satisfaction/dissatisfaction. We compared the collective Value Scores (cVS) based on outputs of probabilistic forecasts of precipitation seasonally in Seoul and Busan during the period of 2004 to 2013 and ÿnally found the optimum threshold that can improve cVS of both cities. When using 30% threshold, the users can expect a higher cVS compared with those using other thresholds. When using the seasonal optimum threshold in Seoul, the cVS is additionally higher by 9%. These results show the level of satisfaction of the forecast that can be improved when the meteorological communities inform the users the correct threshold of the rainfall probabilistic forecast.