Artificial intelligence is increasingly being adopted to enhance weather forecasting capabilities globally, leading to improvements in the accuracy and speed of meteorological predictions. This technological transformation aids various sectors reliant on climate data, from agriculture to disaster management. Sharjah24 likely discusses these advancements, potentially highlighting their relevance or application within the UAE. Why it matters: Improved weather forecasting through AI has critical implications for infrastructure planning, water resource management, and climate resilience in the Middle East.
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.
MBZUAI researchers are developing an AI-powered tool for flood assessment using satellite data and computer vision, prompted by the recent extreme weather in the Gulf region. The prototype analyzes spatial satellite imagery from before and after the storm to detect changes and identify heavily impacted roads and critical infrastructure. The tool uses AI models, Sentinel-2 imagery, and OpenStreetMap data to locate affected areas and estimate water depth. Why it matters: This research offers a way to automate and improve rapid response to extreme weather events, providing local authorities with critical information for rescue, recovery, and future urban planning in the face of climate change.