This paper examines the relationship between COVID-19 spread and weather patterns across 89 cities in Saudi Arabia using machine learning. The study uses daily COVID-19 case reports from the Saudi Ministry of Health and historical weather data. The results indicate that temperature and wind speed have the strongest correlation with the spread of COVID-19, with a random forest model achieving the best performance.
MBZUAI and the University of Chicago are collaborating on a program to train governments in low- and middle-income countries (LMICs) to use AI weather forecasting models. Funded by a grant from the UAE Presidential Court, the program's first cohort includes staff from Bangladesh, Chile, Ethiopia, Kenya, and Nigeria, receiving training in the UAE at MBZUAI and NCM. The program aims to expand to 30 countries, potentially benefiting millions of farmers by improving yields and livelihoods. Why it matters: This initiative democratizes access to advanced weather forecasting, enabling LMICs to leverage AI for climate resilience and agricultural productivity.
KAUST researchers found a 25-30% increase in winter rainfall in the eastern Arabian Peninsula since 1981, with a 10-20% decrease in the south and northeast. This change correlates with a shifting El Niño pattern in the tropical Pacific Ocean, affecting sea surface temperatures and westerly winds. The study used rainfall data from the University of East Anglia and 39 stations across the peninsula from 1951-2010. Why it matters: Improved understanding of these climate drivers could enhance long-term rainfall predictions, benefiting agriculture and water resource management in this arid region.
The KAUST Supercomputing Core Lab (KSL) and the National Center of Meteorology (NCM) have been collaborating since 2016 to enhance weather forecasting capabilities in Saudi Arabia. KSL provides consultation, data storage, and computing time on the Shaheen II supercomputer to NCM. This collaboration has led to a significant increase in NCM's HPC facility computing capacity, from 10 to 380 TFLOPS, with ongoing work to reach 1.8 PFLOPS. Why it matters: This partnership strengthens Saudi Arabia's ability to provide accurate and timely weather forecasts, crucial for public safety, aviation, and national security, demonstrating the importance of HPC in addressing critical environmental challenges.
KAUST and K.A.CARE have partnered to study solar irradiation and atmospheric weather conditions in Saudi Arabia, leveraging K.A.CARE's Renewable Resources Atlas Project. The collaboration uses KAUST's Shaheen II supercomputer to simulate weather and atmospheric conditions from 2005-2018. The long-term goal is daily forecasting of weather and air quality across the Arabian Peninsula. Why it matters: This initiative will provide crucial data for renewable energy development and environmental monitoring in the region, supporting Saudi Arabia's sustainability goals.
A KAUST report, in collaboration with AEON Collective and KAPSARC, warned of increasing flash floods in the Arabian Peninsula due to climate change. The report predicts a 33% increase in annual maximum rainfall by the end of the century under a high emissions scenario. KAUST is supporting MEWA to improve dam management and flash flood warning systems, leveraging its data and supercomputing capabilities. Why it matters: The study highlights the urgent need for infrastructure adaptation and improved warning systems in the region to mitigate the increasing risk of climate-related disasters.