Researchers conducted a detailed mobile observation survey of methane emissions in the Osaka metropolitan area. Researchers found overlooked sources of greenhouse gas emissions.

POSTECH analyzed the contribution of wildfires to changes in fine particulate matter PM_2.5 concentrations during drought periods.

This finding revealed that weaker lightning during thunderstorm development can also ignite forest fires. 

Analysis revealed that ignition occurred during the development stage of the thunderstorm—not the mature stage as traditionally thought. The lightning frequency and intensity were lower in this phase, but the discharge characteristics were unique.

Allowing dairy cows to graze on fresh grass in open pastures for a significant proportion of the year seems more natural and sustainable. And there are proven advantages for the environment: stable grasslands promote biodiversity, protect the soil, and support carbon storage. However, farms face challenges as they are heavily dependent on the weather and the time of the year. Farming is more productive when the pastures have a wide variety of different grasses, herbs and legumes, such as clover and chicory. A research team at the University of Göttingen has analysed this strategy using a meta-analysis of a number of studies. The analysis showed that the diversity of pastureland has no effect on milk production or on emissions of the greenhouse gas methane, which cows emit mainly when they burp. However, a higher proportion of legumes can promote milk production. The results were published in the journal Food and Energy Security.

In a paper published in SCIENCE CHINA Earth Sciences, a team of researchers investigated a fine-scale lightning forecasting approach based on weather foundation models (WFMs) and proposed a dual-source data-driven forecasting framework that integrates the strengths of both WFMs and recent lightning observations to enhance predictive performance. Furthermore, a gated spatiotemporal fusion network (gSTFNet) is designed to address the challenges of cross-temporal and cross-modal fusion inherent in dual-source data integration. Experimental results demonstrate that the dual-source framework significantly improves forecasting performance compared to models trained solely on WFMs and outperforms both the ECMWF HRES lightning product and other deep-learning spatiotemporal forecasting models.