Improving seasonal forecasts of air temperature using a genetic algorithm.

ABSTRACT: Seasonal forecasts of air-temperature generated by numerical models provide guidance to the planners and to the society as a whole. However, generating accurate seasonal forecasts is challenging mainly due to the stochastic nature of the atmospheric internal variability. Therefore, an array of ensemble members is often used to capture the prediction signals. With large spread in the prediction plumes, it becomes important to employ techniques to reduce the effects of unrealistic members. One such technique is to create a weighted average of the ensemble members of seasonal forecasts. In this study, we applied a machine learning technique, viz. a genetic algorithm, to derive optimum weights for the 24-ensemble members of the coupled general circulation model; the Scale Interaction Experimen

SUBMITTER: Ratnam JV 

PROVIDER: S-EPMC6726601 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature