A new study introduces an innovative framework that harnesses satellite observations and machine learning models to quantify the direct impacts of typhoons on vegetation canopy structure and photosynthesis. 

A new study published in Advances in Atmospheric Sciences explores how machine learning and statistical techniques can refine forecasts of photovoltaic by correcting errors in weather models.

A new study published in the journal Science Advances by an international team of climate scientists presents the first realistic supercomputer simulation that resolves the complex interactions between fire, vegetation, smoke and the atmosphere. The authors find that increasing greenhouse gas emissions will likely increase the global lightning frequency by about 1.6% per degree Celsius global warming, with regional hotspots in the eastern United States, Kenya, Uganda and Argentina [Figure 1A]. Locally this could intensify wildfire occurrences. However, the dominant drivers for the growing area burned by fires each year [Figure 1B] remain shifts in global humidity and a more rapid growth of vegetation, which can serve as wildfire fuel.

The research team led by KIM Jae Kyoung, Professor in the Department of Mathematical Sciences at KAIST and Chief Investigator of the Biomedical Mathematics Group at the Institute for Basic Science (IBS), has unveiled new insights into how weather influences the spread of dengue fever. Their study identifies temperature and rainfall as critical factors driving the global surge in dengue cases and offers actionable strategies for mitigating the disease's impact.