The goal of this CISESS Seed Grant Project is to demonstrate the feasibility of retrieving latent heating profiles of the atmospheric column using passive microwave satellite observations. This advance could potentially provide the foundation for unique cloud system analyses and new insights into cloud processes, potential for advances in numerical prediction, and a better understanding of energy and water budgets at global and regional scales.
The CISESS Seed Project takes hyperspectral data from NASA’s Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) Ocean Color Instrument (OCI). This data is processed with both supervised-learning and self-teaching machine learning to classify hyperspectral data to ocean composition and phytoplankton types. This approach will be validated using hyperspectral radiometer data from field and lab experiments using the CISESS Remote Sensing Lab (RSL) instruments.
The Resilience Inference Measurement (RIM) model was developed to quantify resilience to natural disasters like fires and hurricanes. In this paper, CISESS Scientists Wenhui Wang, Yan Bai, Xi Shao, Sirish Uprety and Hong-Lie Qiu introduce an approach that integrates observations from several satellite platforms so that a recovery assessment can be made more quickly.