Weather forecasting and verification is becoming ever more computer intensive. Temperature verification results show that guidance has increased substantially in accuracy in recent years. This compels human forecasters to stay ahead of advancing technology, learning to evaluate the performance of new computer products and combine their guidance to produce the most useful forecasts possible. The results of this study are part of the system referred to as Advanced Verification Information for Smart Operational Results (ADVISOR). In this particular research we present to the weather forecasters a graphical depiction of the spread of different model factories from a modified version of the DICast system. The spread is presented in conjunction with other meteorological parameters and the relationship found between the model spread and these additional parameters is archived for past weather situations as a learning and decision support mechanism to maximize effectiveness in temperature forecasting. This system allows us to extract information from numerical forecasts and forecast verifications in real time to support weather forecast operations.