Do we really have to consider data mining techniques for meteorological data

Weather data have two classes´ synoptic data and climatic data. Real time data (Synoptic data) is used in applications like forecast modelling and aviation; climatic data is recorded over certain period of time. Formerly weather was forecasted manually by observing sky conditions and current weather conditions with manual calculations. Weather conditions are chaotic so there prediction must be precise and accurate. More sophisticated techniques have been developed which are far more efficient and accurate than manual calculations. Data mining is among one of such technologies. And it has a broader scope of applicability. One of such domains is Meteorology where data mining can enhance the productivity of its analysts extensively by transforming their huge, unmanageable data into valuable information knowledge. Meteorological changes of a region can cause economic and ecological damage and can harm human lives. Accurate prediction is therefore a key factor for controlling such occurrences. Various weather events e.g. Temperature, Humidity, Wind direction, Wind Speed, and Rain etc. are being predicted using various data mining techniques. The prime objective of this paper is to review research in data mining techniques applied in the field of weather prediction. A comparative study of various data mining techniques in weather forecasting is followed by a discussion on conventional preprocessing, challenges associated and issues of model evaluation and building methods. Therefore, this paper provides a roadmap to researchers for knowledge.