Using moving averages to detect climate anomalies

We have recently developed two anomaly detection methods to detect climate changes and deviations. More recently, we have focused on anomaly detection techniques involving moving averages. We are using these detection methods to find anomalies in the Global Historical Climatology Network (GHCN) monthly data . More specifically, we have used moving averages to detect short-term as well as long-term deviations in different climate data types, such as: minimum and maximum temperature, precipitation, and snowfall. Using Google Earth, we will also be able to analyze short-term and long-term anomalies visually, hopefully providing better insight into climate changes.