UAE Research Program for Rain Enhancement Science

Project Brief

This project proposes to use artificial intelligence (AI) to improve precipitation estimates
and pave the way for enhanced forecasting and cloud targeting by leveraging vast ground-based and spaceborne data sets and operational numerical weather prediction products from national meteorological centers worldwide. With the unprecedented abundance of data sets from diverse observations and models available to operational rainfall enhancement programs, the exploration of AI algorithms already results 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 effectively extract and extrapolate inputs and the required cloud features necessary to define seedable cloud patches. These features and inputs, along with extrapolated satellite and radar data, 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 AI and existing assets, this project assembled a multidisciplinary and diverse research team led by the Scripps Institution of Oceanography at the 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.

Approach and Methods

Task 1: Datasets Gathering and Quality Control

Figure 1. Surface stations over the study area

Figure 2. Radar locations over the study area

Figure 3. . Radar Quality Control and Composite Product Development. ML model uses 16 frames of sequential images as an input and predicts 16 frames of images. This predicts up to 90 minutes since the time gap between the images is 5 minutes.

Task 2: Development of the Machine Learning Models

Figure 1. Build AI framework to generate (1) cloud-patch features important to cloud seedability, (2) QPE at high spatiotemporal resolution, and (3) short-term precipitation forecasts (0-6 h of lead time).

Task 3: Development of a Real-time Prototype System for Analysis and Nowcasting
Task 4: Capacity Building and Knowledge Transfer

Publication and Presentations

Delle Monache, L., Axisa, D., Cobb, A., Ghazvinian, M., Daminiani, E., Al-Hamadi, H., Abououf, M., Chandrasekar, V., Eun, Y. K., and Radhakrishnan, C., 2023. “A Hybrid Machine Learning Framework for Enhanced Precipitation Nowcasting”. 103rd American Meteorological Society (AMS) Annual Meeting, January 8-12, Denver, Colorado, USA.

Axisa, D., 2023. “Advances In Intelligent System Design And Application For Rainfall Enhancement”. 6th International rain enhancement forum, January 24-26, Abu Dhabi, UAE.

Delle Monache, L., “Research And Operations (AI-RAO): A Hybrid Machine Learning Framework To Combine Satellite, Radar Observations, And Numerical Weather Predictions For Enhanced Cloud-seeding Operations”. 6th International rain enhancement forum, January 24-26, Abu Dhabi, UAE.


Center for Western Weather and Water Extremes

Khalifa University

Colorado State University

National Center of Meteorology

Luca Delle Monache, PhD.


Duncan Axisa, PhD.


Zhenhai Zhang, PhD.


Vesta Afzali Gorooh, PhD.


Vaghef Ghazvinian, PhD.


V Chandrasekar, PhD.


Chandrasekar Radhakrishnan, PhD.


EunYeol Kim, PhD.


Ernesto Damiani, PhD.


Hussam Al Hamadi, PhD.


Muhammad Muneeb



This material is based on work supported by the National Center of Meteorology, Abu Dhabi, UAE, under the UAE Research Program for Rain Enhancement Science.


Any opinions, findings and conclusions, or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Center of Meteorology, Abu Dhabi, UAE, funder of the research.