Accurate medium-range weather forecasts are important for decision-making, but current forecasts have limitations. By grouping weather patterns into categories, noise can be reduced, improving forecasts. A study used clustering to categorize circulation patterns in North America and compared different forecasting methods. The circulation pattern forecast was found to be the most accurate over an 8-11 day period, outperforming other methods. This approach shows promise for improving 1-2 week forecasts based on weather patterns.