Please select the desired project time frame:
- July 2026
- January 2026
- July 2025
- January 2025
- July 2024
- January 2024
- July 2023
- January 2023
- July 2022
- January 2022
- July 2021
- January 2021
- July 2020
- January 2020
- July 2019
- January 2019
- July 2018
- January 2018
- July 2017
- January 2017
- July 2016
- January 2016
- July 2015
- January 2015
- July 2014
- January 2014
- July 2013
- January 2013
- July 2012
- January 2012
- July 2011
- January 2011
- July 2010
- January 2010
- July 2009
- January 2009
- July 2008
- January 2008
- July 2007
- January 2007
- July 2006
- January 2006
- July 2005
- January 2005
- July 2004
- January 2004
- July 2003
- January 2003
- July 2002
- January 2002
- July 2001
- January 2001
Start of funding 01.01.2021
AI-augmented Cloud Tomography for next-generation Remote Sensing of Cloud Properties to Improve Short-Term Solar Energy Forecasting
Prof. Dr. Bernhard Mayer
Ludwig-Maximilians-University of Munich
Lehrstuhl für Experimentelle Meteorologie
Prof. Dr. Katherine L. Bouman
California Institute of Technology, Pasadena (CALTECH)
Department of Computing and Mathematical Sciences
Clouds are a key component of the Earth’s climate system, and artificial intelligence (AI) has the potential of significantly advancing observation techniques relating to cloud properties. This project aims at developing a novel tomographic remote sensing method similar to Computed Tomography in medical imaging. We will develop an AI-augmented retrieval method for reconstruction of 3D cloud properties from multi-angle observations. In traditional retrievals, clouds are simplified as one-dimensional, horizontally homogeneous cloud layers. This assumption causes a significant bias for convective clouds, which make up a large fraction of global cloud coverage. Thus, advanced methods for investigation of cloud properties are needed to improve short-term forecasting for solar energy harvesting. Furthermore, long-term monitoring of cloud properties via satellite will improve our understanding of the clouds’ role in the Earth’s energy budget.