A polynomial network for predicting temperature distributions

Complete temperature distributions are unavailable for many locations throughout the world. This distributional information is important for product design and operational planning. The problem of obtaining these temperature distributions is quite difficult and current techniques are limited in accuracy. This paper describes a new and effective approach to this problem that matches data-deficient locations to maximally similar locations with known distributions. A polynomial network is used to predict which of a set of sites with known distributions is most similar to a data-deficient site. Then an information theoretic criterion is optimized to find the unknown distribution that closely matches this maximally similar site. Tests with this approach demonstrate its effectiveness and its superiority to current methods.