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Dr. Lulin Xue obtained his Ph.D. degree in Meteorology from Saint Louis University in 2009. He then joined the National Center for Atmospheric Research (NCAR) in the United States as an Advanced Study Program postdoctoral fellow. Dr Xue joined the advisory panel of the Beijing Weather Modification Office in 2016 and became the Chief Scientist of Hua Xin Chuang Zhi Science and Technology LLC in 2017. 

Dr Xue has been the key scientist responsible for the numerical modeling aspect of several projects carried out at NCAR since 2009. He has conducted original and applied scientific research for a cloud seeding project in Saudi Arabia, a wintertime orographic cloud seeding project for the Idaho Power Company, and several programs in Wyoming.

Dr Xue’s areas of expertise are in aerosol-cloud-precipitation interactions, cloud microphysics and dynamics, boundary layer and mountain meteorology and numerical modeling. His research efforts have led to the development of a real-time cloud seeding forecasting system.

Project Brief:

“Using Advanced Experimental - Numerical Approaches to Untangle Rain Enhancement (UAE-NATURE)”

This project involves a consortium of research institutes and universities from China, Hungary, UAE and USA collaborating on an innovative research approach to rain enhancement based on advanced laboratory experiments and state-of-the-art numerical models.

The core objectives of the proposed study are: 1) improve knowledge of hygroscopic seeding impacts on warm rain initiation; 2) discriminate the dynamical and microphysical processes by which natural and seeded precipitation forms and evolves within clouds; and 3) quantify potential seeding impacts on UAE rainfall in relation to climate variables over a 10-year period using high-resolution regional climate and ensemble seeding simulations.

Additional objectives are to understand how cloud seeding affects cloud cover lifetimes, assess impacts  on resultant groundwater availability, and quantify spatial and temporal rainfall distribution in the UAE.

Research Progress:

In the first year, the team conducted cloud chamber experiments on natural warm cloud formation and assessed hygroscopic seeding effects on warm cloud and precipitation formation.

Observations to validate a 10-year regional climate simulation have been assembled, enabling the team to start determining the model simulation configurations and to make necessary observations to validate these. Dr Xue is currently working with AIDA chamber group in Karlsruhe Institute of Technology to establish the right instrumentation configurations for its experiments.

The analysis of the existing data will provide basis for future analysis on a new 10-year simulation. The testing of model configurations and 1-year test run are critical for a successful 10-year regional climate simulation. The model configurations were tested for the regional climate simulation over the UAE region using data relating to a two-month period in 2017.

In addition, the Beijing Aerosol Cloud Interaction Chamber (BACIC) has also been employed for continuous testing, improvement and measurement of instrumentation configurations needed for the warm-phase cloud experiments. A new high resolution regional climate data set for the UAE area was also analyzed.


In the project’s second year, a cloud chamber experiment related to glaciogenic seeding effects on cloud and precipitation formation will be conducted. The finalization of the 10-year regional climate simulation and initiation of model validation work will then enable the team to compare cloud seeding simulations and model-observation.

In the third year of the project, the team will conduct chamber experiments on secondary ice formation and electric effects on clouds and rain formation. Seeding cases based on 10-year regional climate simulation will be identified and seeding potential studied according to large-scale weather patterns identified using 10-year regional climate simulation data. The seeding effect of the cases identified from the regional climate simulation will then be quantified.


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