Dr. Luca Delle Monache is the Deputy Director of the Center for Western Weather and Water Extremes (CW3E), Scripps Institute of Oceanography, University of California San Diego. His goal is to provide support for the Director, Marty Ralph, in managing activities within the Center, and in developing new science and application directions to support CW3E’s Vision and Mission. Specifically, Dr. Delle Monache oversees the development of the Center’s modeling, data assimilation, postprocessing, and artificial intelligence capabilities, with the goal of maintaining state-of-the-art models and tools while actively exploring innovative algorithms and approaches. In close coordination with the Center Director and the management team, he develops new scientific and programmatic strategies to maintain and further expand CW3E leadership on understanding, observing, and predicting extreme events in Western North America.
He earned a Laurea (~M.S.) in Mathematics from the University of Rome, Italy (1997), an M.S. in Meteorology from the San Jose State University, U.S. (2002), and a Ph.D. in Atmospheric Sciences from the University of British Columbia, Canada (2005). His interests include the design of ensemble methods, probabilistic prediction and uncertainty quantification, numerical weather prediction, data assimilation, inverse modeling, postprocessing methods including artificial intelligence algorithms, renewable energy, air quality and transport and dispersion modeling. Among his main scientific accomplishments, there is the development during his Ph.D. of the first ensemble for air quality prediction, and later in his career the design of the analog ensemble which has been applied successfully in several of the fields, and is based on a new paradigm for ensemble design. Luca Delle Monache has been the principal investigator of several multi-institution multi-million projects funded by the National Science Foundation, the National Oceanic and Atmospheric Administration, the National Aeronautics and Space Administration, the Department of Energy, the Department of Defense, and the private sector. Before joining CW3E, he was a postdoc and then a staff scientist at the Lawrence Livermore National Laboratory, Livermore, California (2006-2009), and a project scientist and then the Science Deputy Director of the National Security Applications Program at the National Center for Atmospheric Research, Boulder, Colorado (2009-2018).
“A Hybrid Machine Learning Framework for Enhanced Precipitation Nowcasting”
This project proposes to use artificial intelligence (AI) to improve precipitation estimates and pave the way for enhanced forecasting and cloud targeting leveraging vast ground-based and spaceborne data sets and operational numerical weather prediction products from national meteorological centers around the world. With the unprecedented abundance of data sets from diverse observations and models available to operational rainfall enhancement programs, the exploration of AI algorithms is already resulting in significantly improved weather forecasts.
This project aims to create an AI research and operations testbed in the UAE. This entails building a novel AI framework to blend satellite observations, ground-based weather radar data, rain gauges, and numerical weather prediction estimates to extract features and generate products to determine optimal cloud seeding timing and location, and to generate more accurate quantitative precipitation estimation for rainfall enhancement program evaluation. An advanced deep learning algorithm is proposed to learn from several thousands of examples from historical data how to effective extract and extrapolate inputs and the required cloud features important to define cloud patches that are seedable. These features and inputs, along with extrapolated satellite and radar data, as well as numerical weather prediction data and rain gauges, are utilized as input to an AI-based model to generate precipitation predictions six hours in the future.
To expand rain enhancement capabilities in the UAE through use of AI and existing assets, this project assembled a multidisciplinary and diverse research team led by the Scripps Institution of Oceanography at University of California San Diego with collaborators from Khalifa University and Colorado State University. The main deliverable at the end of the project will consist of a prototype of the AI-based predictive capabilities. The prototype will be deployed at the National Center for Meteorology (NCM) in the UAE through a research and operations partnership.