潘宝祥，美国劳伦斯利弗摩尔国家实验室博士后，主要研究兴趣包括概率信息理论、结合机器学习与动力模式的天气-气候尺度预报、动力系统可预报性。2012年本科毕业于武汉大学，2015年在清华大学获得工学硕士学位，2019年于加州大学欧文分校（University of California, Irvine）获得工学博士学位，师从Dr. Soroosh Sorooshian, Dr. Kuolin Hsu, Dr. Amir AghaKouchak。（来源：大气物理研究所）
The fidelity of climate projections is often undermined by biases in climate models due to their simplification or misrepresentation of the climate processes. While various bias correction methods have been developed to post-process model outputs to match observations, existing approaches usually focus on limited, lower-order statistics, or break either spatiotemporal consistency of the target variable, or its dependency upon model resolved dynamics. We develop a Regularized Adversarial Domain Adaptation (RADA) methodology to overcome these deficiencies, and enhance efficient identification and correction of climate model biases.
