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.
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.
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.
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 scientists are developing models to predict extreme weather events like the 2009 Jeddah flood, which caused significant damage. Prof. Ibrahim Hoteit's team is using data from satellites, international sources, and local entities like PME and the Jeddah Municipality to build high-resolution models. The aim is to improve predictions of extreme rain events by one or two days and issue timely warnings. Why it matters: Improving extreme weather prediction is crucial for mitigating the impact of climate change in vulnerable regions like the GCC.
The International Renewable Energy Agency (IRENA) has published a report titled 'From Prediction to Power: Applying Weather, Climate Forecasting, and AI in Renewable Energy'. This publication explores the integration of artificial intelligence with advanced weather and climate forecasting models. It details how these technologies can enhance the efficiency, reliability, and predictability of renewable energy sources, such as solar and wind power. Why it matters: This work highlights the critical role of AI in accelerating renewable energy adoption and achieving global climate goals by transforming intermittent energy sources into more stable and manageable power generation assets.
The Directed Energy Research Center (DERC) received a US$1.5 million grant from the UAE Research Program for Rain Enhancement Science (UAEREP). The grant was awarded at the UAEREP's 5th Cycle Awarding Ceremony in Abu Dhabi. DERC was recognized for its research on laser-induced rain and the development of a mobile high-power pulsed laser using remote sensing. Why it matters: This funding supports the development of sustainable, chemical-free rain enhancement technologies, addressing critical water security challenges in arid regions.