In a paper published in SCIENCE CHINA Earth Sciences, researchers assessed the parameterization of orographic gravity wave drag (OGWD) in 3-km-resolution WRF simulations for precipitation forecast over the highly complex-terrain Southwest China. Their results reveal that OGWD parameterization, previously considered negligible in kilometer-scale models, can greatly improve the predicted rainfall intensity and location by modulating the low-level winds and moisture transport. These findings lay an important scientific foundation for operational precipitation forecasting in regions of complex terrain.

A team of northern New England universities and institutions has been awarded $5.9 million from the National Science Foundation (NSF) to advance snow research and explore connections between winter weather and the outdoor recreation economy in Vermont, New Hampshire and Maine. The University of Vermont (UVM), which will lead the effort, is the recipient of $3.3 million and the University of New Hampshire (UNH) is the recipient of an additional $2.6 million as a collaborator. The project will include the University of Maine and eight other regional institutions.

Researchers used combinatorial solid-phase peptide synthesis and infrared thermography to screen 300 tripeptide sequences for interactions with CO₂. Two tripeptides, His(Boc)-Phe-Asn and Phe-Ala-Glu(OtBu), showed reproducible thermal responses after CO₂ exposure. The signals were reproduced on glass, functionalised cellulose, and Rink-amide resin, indicating that thermographic screening can help identify peptide candidates for further development as bioinspired CO₂-binding materials.

Researchers found that wildfire smoke and rain, through relatively infrequent, can deliver disproportionally large amounts of nitrogen, phosphorus and potassium to ecosystems. Analyzing satellite smoke observations and national network rainfall data from 250 sites across 16 climate regions in 2014, 2020 and 2022, the study found a striking increase in smoke-rain days in 2022 compared with 2014, with regional hotspots shifted from year to year. The findings show wildfire emissions can be an important source of material deposited into ecosystems, but not necessarily in a way that benefits growth or productivity.

Pure water only freezes at around minus 38 degrees Celsius. Tiny mineral dust particles act as so‑called ice nucleators—crystallization seeds on which ice crystals form. A research team at Bielefeld University and the University of Vienna in cooperation with researchers from Helsinki University has now demonstrated for the first time, at the molecular scale, why the mineral microcline is particularly effective at forming ice in clouds. Published in a renowned scientific journal, the study provides a new explanation for processes that influence climate and precipitation worldwide.

A major two-year trial by RMIT University, Westernport Water and Australia’s national science agency CSIRO measured the impact of floating platforms of native plants on greenhouse gas emissions from a wastewater lagoon in Phillip Island, southeast Australia.  

The results published in the Journal of Environmental Management reveal dramatic results, with CO₂ emissions dropping by up to 36%, methane by up to 66% and nitrogen by 18% in the lagoon with a floating wetland installed, compared to the control lagoon with no wetland platform. 

Plasma bubbles in the ionosphere over equatorial and low-latitude regions are a common nighttime plasma density depletion structure that can cause severe scintillation interference on high-frequency communications, making them an important target of space weather monitoring. Traditional detection methods rely on statistical thresholds derived from temporal fluctuations in ion density; however, plasma bubbles exhibit significant variability in spatiotemporal scales (ranging from kilometers to hundreds of kilometers), and their occurrence frequency is strongly modulated by solar activity and seasonal factors. Consequently, fixed-threshold methods struggle to accommodate this diversity, with miss rates reaching up to 40% during periods of low solar activity. Although deep learning has demonstrated powerful capabilities in automatically extracting and recognizing complex waveform features in fields such as medical image segmentation and signal peak detection, its systematic application to ionospheric plasma bubble detection—and thus addressing the adaptability deficiencies of traditional threshold methods—remains underexplored.