Correcting abnormalities in meteorological data by machine learning

Meteorological data collected from automatic weather stations have played an important role in forecasting and analyzing a large variety of phenomena. However, abnormal values are abundant in meteorological data due to manifold faults in observation systems. In this paper, we attempt to recover abnormal values. We present three estimation models based on machine learning techniques and compare them with traditional estimation methods, interpolations. Unlike the interpolation methods, which use only the target attribute, the proposed models utilize the additional information consisting of the associated attributes of the target station and the relevant data of the neighbor weather stations. Experiments were conducted for 692 locations in South Korea from 2007 to 2012. The results showed that the proposed approaches estimated target values better than the interpolation methods for all weather elements except one and the additional information helped achieve better performance.