Contour regression: A distribution-regularized regression framework for climate modeling

Zubin Abraham, Pang-Ning Tan, Perdinan, Julie A. Winkler, Shiyuan Zhong, Malgorzata Liszewska

Abstract: Regression methods are commonly used to learn the mapping from a set of predictor variables to a continuous-valued target variable such that their prediction errors are minimized. However, minimizing the errors alone may not be sufficient for some applications, such as climate modeling, which require the overall predicted distribution to resemble the actual observed distribution. On the other hand, histogram equalization methods, such as quantile mapping, are often used in climate modeling to alter the distribution of input data to fit the distribution of observed data, but they provide no guarantee of accurate predictions. This paper presents a flexible regression framework known as contour regression that simultaneously minimizes the prediction error and removes biases in the predicted distribution. The framework is applicable to linear, nonlinear, and conditional quantile models and can utilize data from heterogenous sources. We demonstrate the effectiveness of the framework in fitting the daily minimum and maximum temperatures as well as precipitation for 14 climate stations in Michigan. The framework showed marked improvement over standard regression methods in terms of minimizing their distribution bias.

External IDs:dblp:journals/sadm/AbrahamTPWZL14