The researchers developed a new method called Time-Expanded Sampling Approach to improve the Ensemble Kalman Filter system. By using this approach, they were able to reduce the computational time needed for prediction runs while maintaining accuracy. They found that having a small ensemble size can save on computational costs, but if the size is too small, the filter may not work properly. The Time-Expanded Sampling Approach helps to balance these factors and improve the efficiency of the system.