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.
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.
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 novel wind speed forecasting (WSF) framework is proposed combining Wavelet Packet Decomposition (WPD), Seasonal Adjustment Method (SAM), and Bidirectional Long Short-term Memory (BiLSTM). The SAM method eliminates the seasonal component of the decomposed subseries generated by WPD to reduce forecasting complexity. The model was tested on five years of hourly wind speed observations acquired from the Dumat Al-Jandal wind farm in Al-Jouf, Saudi Arabia, achieving high forecasting accuracy.
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.
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.
KAUST's HALO group launched a CubeSat in 2023 for high-precision Earth observation in the Gulf region, combining GNSS Reflectometry and hyperspectral sensing. The satellite monitors vegetation, soil, agriculture, and ecosystem health, providing detailed estimates of irrigation water use and vegetation health. The Extreme Statistics (XSTAT) research group at KAUST focuses on the mathematical modeling and prediction of extreme weather and climate events. Why it matters: These KAUST initiatives enhance climate resilience in the region through advanced monitoring, statistical modeling, and predictive capabilities.