Linear Inverse Modeling for Coupled Atmosphere‐Ocean Ensemble Climate Prediction

Coupled Climate System Modeling

Model complexity versus ensemble size: allocating resources for climate prediction.

A perennial question in modern weather forecasting and climate prediction is whether to invest resources in more complex numerical models or in larger ensembles of simulations. If this question is to be addressed quantitatively, then information is needed about how changes in model complexity and ensemble size will affect predictive performance. Information about the effects of ensemble size is...

Feature Engineering and Ensemble Modeling for Paper Acceptance Rank Prediction

Measuring research impact and ranking academic achievement are important and challenging problems. Having an objective picture of research institution is particularly valuable for students, parents and funding agencies, and also attracts attention from government and industry. KDD Cup 2016 proposes the paper acceptance rank prediction task, in which the participants are asked to rank the import...

Optimal Multi-model Ensemble Method in Seasonal Climate Prediction

Given a large number of dynamical model predictions, this study endeavors to improve seasonal climate prediction through optimizing multi-model ensemble (MME) method. We have developed a new MME method and evaluated it using 15 dynamical models’ retrospective forecasts for the period 1981-2001 in comparison with other MME methods. The strengths of the new method lie in a statistical error corre...

DC-constrained linear prediction for glottal inverse filtering

Closed phase covariance (CP) analysis is a glottal inverse filtering method which estimates the vocal tract during the glottal closed phase. Since closed phase durations are typically short, the vocal tract computation with linear prediction is vulnerable to the covariance frame position. This study proposes a modified CP algorithm in which a DC-gain constraint is imposed in optimizing the line...