Developing Passive Satellite Cloud Remote Sensing Algorithms using Collocated Observations, Numerical Simulation and Deep Learning
This talk will give an overview and recent developments of an on-going project funded by the NASA ACCESS program. The overarching goal of this project is to develop an extensible platform that combines collocated satellite observations, numerical simulations, and deep learning methods to generate a highly accurate cloud property training dataset for NASA, NOAA, and the broad science community to develop and benchmark algorithms for passive satellite cloud remote sensing. This project will deliver: 1) Novel deep-learning-based domain-adaptation algorithms to retrieve passive satellite remote sensing cloud bulk properties (e.g., cloud mask and thermodynamic phase) by leveraging one or more available active sensing data. 2) A novel hybrid approach combining advanced 3-D radiative transfer simulations based on collocated global satellite observations and deep learning based multi-pixel cloud microphysical and optical property retrieval. 3) Scalable data processing and analytics services in a public cloud computing environment (i.e., Amazon Web Service) for the above components/capabilities. 4) Comprehensive data quality evaluation of the training datasets (where retrieved cloud properties are data labels) to be delivered from multiple aspects including statistics, climatology, ground observation, and ad hoc case studies.
Bio: Dr. Jianwu Wang is an Associate Professor at the Department of Information Systems, University of Maryland, Baltimore County (UMBC). He is also an affiliated faculty at the Joint Center for Earth Systems Technology (JCET), UMBC. He is on sabbatical visiting the AI Center of Excellence at GSFC this fall. He got his Ph.D. degree in Computer Science from Institute of Computing Technology, Chinese Academy of Sciences in 2007. He has published 110+ papers with more than 2000 citations (h-index: 22), including 20+ papers on machine learning and data analytics research for cloud/dust data products. He is/was associate editor or editorial board member of four international journals, organization committee member of eight conferences and co-chair of eight related workshops. Since joining UMBC in 2015, he has received multiple external grants as PI (over $3.3M in total) funded by NSF, NASA, DOE, State of Maryland, and Industry. He received UMBC Early-Career Faculty Excellence Award in 2019 and NSF CAREER Award in 2020. His current research interests include Big Data Analytics, Distributed Computing and Climate Informatics.
613 Seminar Series Coordinators Reed.Espinosa@nasa.gov Jie.Gong@nasa.gov