ANOMALY CONSTRUCTION IN CLIMATE DATA: ISSUES AND CHALLENGES
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Abstract
Earth science data consists of a strong seasonality component as indicated by the cycles of repeated patterns in climate variables such as air pressure, temperature and precipitation. The seasonality forms the strongest signals in this data and in order to find other patterns, the seasonality is removed by subtracting the monthly mean values of the raw data for each month. However since the raw data like air temperature, pressure, etc. are constantly being generated with the help of satellite observations, the climate scientists usually use a moving reference base interval of some years of raw data to calculate the mean in order to generate the anomaly time series and study the changes with respect to that.