Dr. Jian-Wen Bao received his M.S. in 1990 and Ph.D. in 1993 from the Department of Meteorology at The Pennsylvania State University. He is currently a Research Meteorologist in the Physical Sciences Laboratory of NOAA’s Earth System Research Laboratories. Two years before he went to the Pennsylvania State University for graduate study, he started working on mesoscale modeling as a research assistant in the Mesoscale and Mircoscale Meteorology Division at NCAR. His master thesis was on cloud prediction using a mesoscale model. He became involved in four-dimensional data assimilation during his Ph.D. work, doing idealized studies of the variational data assimilation using the adjoint technique. As graduate research assistant at the Pennsylvania State University, Dr. Bao participated in the research of modeling weather events that affect aviation under a project jointly sponsored by the Federal Aviation Administration and the National Science Foundation. He spent a year at the National Severe Storms Laboratory of NOAA as a student scientist. During his Ph.D. work, Dr. Bao was awarded the Penn State Earth System Sciences fellowship and NASA EOS fellowship. In 1993, Dr. Bao was selected as a Postdoctoral Researcher by the Advanced Study Program at NCAR. He worked with NCAR scientists on the variational sensitivity analysis in numerical modeling studies and large scale dynamics involved in explosive marine cyclogenesis events. After joining NOAA through CU/CIRES in 1994, Dr. Bao has been working with scientists at NOAA on modeling marine boundary layer processes using an air-sea coupled mesoscale model. In 2000, Dr. Bao became a formal NOAA Research Meteorologist. Since then, Dr. Bao has been leading the research on the impact of air-sea interaction on tropical storm development in the Physical Sciences Laboratory. He led a team effort to evaluate the performance numerical weather prediction models in air quality simulations and prediction. Recently, he has been responsible for developing and testing the physics component of the ESRL global model. He is also leading an effort to develop and improve the grid-resolved and subgrid cloud parameterization schemes in numerical weather prediction models.