Storm forecasting is traditionally based on studying atmospheric conditions, but research that also looks at land surface conditions is set to transform early warning systems in tropical regions. This will enable communities to better adapt to the destructive impacts of climate change.
Southern Ocean warming may have a greater impact than Arctic warming in some regions, particularly affecting tropical rainfall patterns, according to a study published in Nature Communications. These effects could exacerbate weather and climate extremes in vulnerable regions.
Author(s): Anton Kananovich and J. Goree
The microscopic structure within a two-dimensional shock was studied using data from a dusty plasma experiment. A single layer of charged microparticles, levitated in a glow-discharge plasma, was perturbed by an electrically floating wire that was moved at a steady supersonic speed to excite a compr…
[Phys. Rev. E 111, 045201] Published Fri Apr 04, 2025
SummaryFull waveform inversion (FWI) has enjoyed increased attention the past decade, becoming the state of the art for estimating parameters influencing wave propagation in a medium. However, only a few recent emerging efforts have attempted to tackle the challenge of uncertainty quantification in FWI. In this study, we suggest joining FWI with the Bayesian approach, where we provide a post-processing step with an advantageous starting point defined by the global minimum stemming from a deterministic FWI algorithm. Then, using the local ensemble transform Kalman filter (LETKF), we obtain the uncertainty as a follow-up step to the FWI procedure. Within a probabilistic Bayesian inversion framework, the LETKF uses local seismic data to update sets of variables in the subsurface domain. Seismic data for each shot and receiver in the time-domain is in this way matched with subsurface layers, and assimilated in a sequential manner. The methodology is showcased on a realistic model of the Gullfaks field in the North Sea, where we study effects of various seismic acquisition design set-ups, algorithm and model parameter settings. We investigate how these acquisition designs and parameters influence the uncertainty reduction and bias of the inversion results. We highlight the importance of studying statistical performance metrics to ensure a balance between bias and underestimation of uncertainty.
SummaryModel-based information about the global water cycle, in particular the redistribution of terrestrial water masses, is highly relevant for the understanding of Earth system dynamics. In many geodetic applications, hydrological model results play an important role by augmenting observations with a higher spatio-temporal resolution and gapless coverage. Here we demonstrate the feasibility of the high-resolution, open-source hydrological model OS LISFLOOD to simulate terrestrial water storage (TWS) variations with a spatial sampling of up to about 5 km (0.05○). Validation against data from satellite gravimetry reveals that the choice of the maximum soil depth has a significant impact on long-term trends in TWS, mainly in the deepest soil layer. We find that refining the soil depth definition effectively reduces spurious TWS trends, while preserving accuracy in modeled river discharge. Using the modified model set-up, we show that in many regions TWS from OS LISFLOOD fits better to observations than TWS from the Land Surface Discharge Model (LSDM) routinely operated at the GFZ and used in geodetic applications worldwide. The advantage of the high spatial resolution of the OS LISFLOOD implementation is shown by comparing vertical surface displacements to GNSS observations in a global network of stations. The data set presented here is the first application of OS LISFLOOD to generate quasi-global (regions south of 60○S excluded) daily 0.05○ TWS fields for a 23-year period (2000–2022).
Publication date: Available online 25 March 2025
Source: Advances in Space Research
Author(s): Fridrich Valach, Miloš Revallo, Eduard Koči
Publication date: Available online 24 March 2025
Source: Advances in Space Research
Author(s): Zhifan Yan, Jinyun Guo, Maosheng Zhou, Jingwen Zong
Publication date: Available online 24 March 2025
Source: Advances in Space Research
Author(s): Tianxiao Xu, Weilin Wang, Hua Chai, Qiangqiang Xu
A new artificial intelligence (AI) model developed by Israeli researchers promises to revolutionize wildfire prediction, with a particular focus on lightning-induced blazes that are growing increasingly common due to climate change. The new AI model can predict where and when lightning strikes are most likely to cause wildfires, achieving more than 90% accuracy—a first in wildfire forecasting.
Eutrophication and oxygen depletion are well-known threats to the ecological balance of the Baltic Sea, which is increasingly under pressure due to climate change. In this context, large saltwater inflows from the North Sea play a crucial role. They transport oxygen-rich water into the deeper layers of the Baltic Sea, counteracting oxygen deficiency and so-called dead zones.
An international study led by the University of Eastern Finland and the Finnish Meteorological Institute has demonstrated that the formation and properties of lower-atmosphere clouds are highly sensitive to changes in atmospheric aerosol concentrations.
A research team, led by Professor Sung-Deuk Choi from the Department of Civil, Urban, Earth, and Environmental Engineering at UNIST, has developed a novel assessment technique to accurately identify the sources and spatial-temporal distribution of atmospheric mercury. The study has been published in the Journal of Hazardous Materials.
For decades, satellites have played a crucial role in our understanding of the remote polar regions. The ongoing loss of Antarctic ice, due to the climate crisis, is, sadly, no longer surprising. However, satellites do more than just track the accelerating flow of glaciers toward the ocean and measure ice thickness.
The El Niño and Southern Oscillation (ENSO) is one of the most influential interannual climate phenomena in the global ocean-atmospheric system, with profound impacts on weather patterns, particularly in the Asia-Pacific region.
The year may have started with a dry spell, but the end of California's storm season has brought more fresh snow to the Sierra Nevada, pushing the state's snowpack to 96% of average on April 1, when the snow season typically reaches its peak.
Estimates of carbon dioxide (CO2) emissions from volcanoes may have been significantly underestimated, according to new research by The University of Manchester.
When Hunga Tonga–Hunga Haʻapai, an underwater volcano near Tonga in the South Pacific Ocean, erupted in 2022, scientists expected that it would spew enough water vapor into the stratosphere to push global temperatures past the 1.5 C threshold set by the Paris Accords. A new UCLA-led study shows that not only did the eruption not warm the planet, but it actually reduced temperatures over the Southern Hemisphere by 0.1 C.
In a gliding avalanche, the entire snowpack slides down a suitable substratum such as grass or slabs of rock. Such avalanches are always released naturally. This requires the snow on the ground to become moist. In winter, this happens from below, when residual heat from summer is still stored in the ground.
Researchers have made a new discovery that changes our understanding of Earth's early geological history, challenging beliefs about how our continents formed and when plate tectonics began.
People along the densely populated Pacific coasts are exposed to strongly fluctuating rainfall patterns: In East Asia, heavy rain falls in summer, and flooding is already one of the climate risks in this region today. The western U.S., on the other hand, is often hit by extreme drought in summer, and the question of how much precipitation the winter will bring is fundamental to appropriate preventive measures.
