Analog forecasting of ceiling and visibility using fuzzy sets

Analog forecasting of ceiling and visibility using fuzzy sets Abstract A fuzzy logic based methodology for knowledge acquisition is used to build a basic case-based reasoning system: a fuzzy k-nearest neighbor based prediction system. The methodology is used to acquire knowledge about what salient features of continuous-vector, temporal cases indicate significant similarity between cases. Such knowledge is encoded in a similarity- measuring function and thereby used to retrieve k nearest neighbors (k-nn) from a large database. Predictions for the present case are made from a weighted median of the outcomes of analogous past cases, the k-nn. Past cases are weighted according to their degree of similarity to the present case. Fuzzy logic imparts to case-based reasoning the case-discriminating ability of a domain expert. Fuzzy methods represent cases with any combination of words and numbers. The fuzzy k-nn technique retrieves similar cases by emulating a domain expert who understands and interprets similar cases. The major contribution of fuzzy logic to case-based reasoning (CBR) is that it enables us to directly acquire domain knowledge about feature salience. This enables us to retrieve a few most similar cases from a large database. This in turn helps us to avoid adaptation problems. Such a fuzzy k-nn system enables a new form of "persistence climatology" (PC), which is widely recognized as formidable benchmark for very-short- range weather prediction. PC has until now had built-in limitations because it uses an eager learning approach, whereas the fuzzy k-nn system has no such built-in limitations because it uses a lazy learning approach. Meteorologists regard such a fuzzy k-nn forecasting system as "custom climatology on-the-fly." Such a system for making airport weather predictions will let us tap many, large, unused archives of airport weather observations, ready repositories of temporal cases. This will help to make airport weather predictions more accurate, which will make air travel safer and make airlines more profitable. Accordingly, a fuzzy k-nn based prediction system, called WIND-1, is proposed, implemented, and tested. Its unique component is an expertly- tuned fuzzy k-nn algorithm with a temporal dimension. It is tested with the problem of producing 6-hour predictions of cloud ceiling and visibility at an airport, given a database of 300,000 consecutive hourly airport weather observations (34 years of record). Its prediction accuracy is measured with standard meteorological statistics and compared to a benchmark prediction technique, persistence. In realistic simulations, WIND-1 is significantly more accurate.