- quantify the characteristics and vertical profile of background aerosol population, and the key meteorological drivers of cloud formation in the study region
- identify optimal seeding strategies (e.g., time, location, amount of seeding) by using cloud-resolving model simulations with highly advanced aerosol microphysics
- quantify the characteristics of theoretically optimal IN seed aerosol in order to support future experimental work,
- utilize novel statistical approaches to identify the major sources of uncertainty in prediction of rain enhancement success and to guide future research efforts.
WP1 will provide a comprehensive observation-based quantification of the role of aerosols in cloud-precipitation interactions in the UAE. The novelty is in the co-located measurements of aerosols, water vapor as well as turbulence and convection, observed from the surface up to the free troposphere. This allows the identification of cloud formation and cycling processes with the finest details. The new data will be obtained by performing a one-year in-situ and ground-based remote-sensing measurement campaign in the UAE starting in August 2017. Results will be scaled up by using complementary remote sensing data.
WP2 will use a variety of modelling tools to explore the optimal seeding strategies for typical UAE conditions. First, one of the leading mesoscale weather forecast models will be used to characterize the conditions leading to cloud formation and potential surface precipitation. This information will be utilized as input for a cloud-resolving model incorporating highly detailed aerosol and hydrometeor description to determine optimal seeding particle characteristics as well as the optimal seeding strategy for different environmental conditions. Finally, breakthrough potential in seeding of ice and mixed-phase clouds will be sought by utilizing state-of-the-art molecular-level quantum mechanical simulation methods to study optimal IN characteristics.
WP3 will utilize state-of-the-art stochastic methods, together with the new observational and modelling results from the other WPs, to conduct a sensitivity analysis that will identify the major sources of uncertainty related to prediction of rain enhancement potential under different environmental conditions, as well as result in a prioritized list of future research needs. As such, the work will integrate the project findings into a practical tool that can be used directly to advance scientific understanding, as well as be further developed for operational purposes.
The research will be carried by six complementary research groups affiliated with the Finnish Meteorological Institute (FMI), University of Helsinki (UH) and Tampere University of Technology (TUT). The work is funded by the second cycle of the UAE Research Program for Rain Enhancement Science.