Non-linear dynamical approaches for characterizing multi-sector climate impacts under irreducible uncertainty
Not provided.
Meteorology & Atmospheric Sciences↗
Engineering topics
Ganguly, Auroop
Publications and source records attributed to Ganguly, Auroop.
Finer resolutions and targeted process representations in earth system models improve hydrologic projections and hydroclimate impacts
Not provided.
Meteorology & Atmospheric Sciences↗
Hybrid physics-AI outperforms numerical weather prediction for extreme precipitation nowcasting
Not provided.
Meteorology & Atmospheric Sciences↗
Assurance by Design for Cyber Physical Data-Driven Systems
Currently, Cyber Physical Data-Driven Systems (CPDDS) employ machine learning for the classification, data fusion, and control of our nation’s infrastructure, such as the power grid, transportation networks (e.g., fuel distribution, air traffic control), and DoD long-duration collaborative autonomous platforms including unmanned underwater, ground, surface, space, and aerial systems. Many CPDDSs are system-of-systems that should be designed to communicate over disadvantaged networks. It is important to assure that the CPDDSs are resilient against physical and cyber threats by design. Additionally, their design should tolerate misclassification errors resulting from natural and/or adversarial distribution shifts within their data driven components. The all-domain nature of the problem of assuring the design of CPDDSs requires a multi-disciplinary perspective as outlined in this chapter.
Chikkagoudar, Satish↗
Deep Learning Emulation of Atmospheric Correction for Geostationary Sensors
New generation geostationary satellites make reflectance observations available at a continental scale with unprecedented spatiotemporal resolution and spectral range. Generating Earth monitoring products from these observations requires retrieval of the basic parameter, surface reflectance (SR), by atmospheric correction (AC). Algorithms for atmospheric correction, including Multi-Angle Implementation of Atmospheric Correction (MAIAC), are adapted for each sensor and are too computationally complex to be run in real time, relying instead on look-up tables with precomputed values. Machine learning methods, including convolutional neural networks, have demonstrated performance in learning complex, nonlinear mappings and extracting insight from high-dimensional remote sensing data. In this work, we present a deep learning emulator of MAIAC to retrieve both SR and cloud products. Using this adaptation of deep learning-based emulation to remote sensing, we demonstrate stable SR retrieval over a variety of land covers and viewing conditions and accurate cloud detection. Further, a comparison of computation time suggests emulation as a compelling alternative for expensive physical simulation, especially for applications benefited by near-real time data, such as agricultural management and disaster response.
Duffy, Kate↗