Biography:
Prof Daniel Rosenfeld is a leading expert on weather modification and climate change at Institute of Earth Sciences, the Hebrew University of Jerusalem. He received his PhD from the Hebrew University in 1986 and later served as a postdoc at NASA before returning to the Hebrew University as a professor.
Prof. Rosenfeld has chaired the committee on weather modification of the American Meteorological Society and co-chaired the aerosol-cloud-precipitation Climate International initiative (ACPC). He also contributed as a Lead Author in the 6th assessment report of the Intergovernmental Panel on Climate Change (IPCC). With over 280 scientific papers and book chapters to his name, he has won recognition as a Fellow of the American Meteorological Society and the American Geophysical Union. He received numerous awards, including the Friendship Award from the Chinese Prime Minister, and the UAE/WMO Prize for weather modification.
Project Brief:
“Identification of Clouds' Microphysical Seedability in an Actionable Manner”
Bringing together leading scientists from four different countries, the overall objective of this project is to develop the capability to diagnosis cloud seedability in near real-time at a spatial resolution of the convective cloud cluster, based on operationally available satellite and meteorological data.
The project’s specific objectives include obtaining cloud microstructure in near real-time, retrieving precipitation forming processes in near real-time, obtaining cloud seedability in near real-time under various seeding methods and providing a decision tool on whether a given existing cloud system should be seeded, along with estimating the potential seeding effect if seeded.
The project will build on previous UAEREP projects that measured the cloud properties with aircraft, developed novel seeding materials and methods and developed ways to predict accurately the locations of growing clouds, which is critical for effective seeding. Therefore, diagnosing cloud seedability in this project is highly complementary and synergistic with most of the previous UAEREP projects.
The machine learning procedure will be trained using model simulations of actual cases validated by aircraft and radar data. Simulated clouds will undergo seeding simulations to determine seedability using various methods. This machine learning procedure requires consistent datasets of meteorology, cloud properties, and their radar and satellite images for training input. While such data cannot be obtained observationally, it can be generated through validated simulations.
To obtain the simulated satellite imagery, a cloud radiative transfer model will operate on numerically simulated clouds and will produce simulated multispectral satellite images. Each simulated image of a cloud cluster will have its seedability. The machine learning procedure will be trained on meteorological and satellite data to estimate the seedability while accounting for potential differences between simulated and real images.
The envisioned outcome of the project is an assessment of the seedability of a representative sample of clouds in the UAE. Furthermore, the machine-learning tool to be developed will allow NCM to perform such assessments in near real-time and guide cloud seeding flights, based on a combination of operational meteorological data and the new METEOSAT Third Generation geostationary satellite. This groundbreaking project has the potential to revolutionize the way cloud seeding is performed and evaluated.
