The storm series algorithms in the SWIFT (Severe Weather Integrated Forecastin g Tools), including storm cell identification, storm tracking and storm forecast , are discussed. Storm cell identification algorithm tests the intensity and con tinuity of the objective echoes by multipleprescribed thresholds to build 3D storms. It uses multiple reflectivity thresholds, newly designs the techniques of cell nucleus extraction and closespaced storms processing, and therefore is capable of identifying embedded cells in multicellular storms. The strong area components at a long distance are saved as 2D storms. Storm cells identified in two consecutive volume scans are associated temporally to determine the cell tracking. The distance between the centroid of each cell detected in the current volume scan and each of the firstguess location is calculated to check distanc e correlation. Those similar storms with distance correlation are matched. The m otion vector for each storm is computed by using the technique of TREC (Tracking Radar Echoes by Correlation), and storm locations in the next hour are provided . During the second trial of the FDP (Forecast Demonstration Project) in 2007, t hese algorithms have been applied. It is found that 3891 storms are identified a nd the mean absolute errors in the X-axis and Y-axis for 30min storm fore cast are 7.1 and 6.2 km respectively. With the increase of forecast time length, the mean absolute errors of the storm product become larger, and the X-axis error is greater than that in the Y-axis. The statistical analysis also shows t hat the mean forecast velocity in the X-axis is less than the mean actual veloc ity of storms, but the conclusion is contrary in the Y-axis.