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This month's full moon will come within about 222,000 miles of Earth. The supermoon rises from the sea in Molfetta, Italy, on October 7, 2025. It was the first of three consecutive supermoons in 2025. Breakthroughs, discoveries, and DIY tips sent every weekday. As the year's penultimate month kicks off, the year's brightest supermoon is almost here.

Science Biology This tiny T. rex is actually a new species Nanotyrannus settles a big and'acrimonius' paleontology debate. Breakthroughs, discoveries, and DIY tips sent every weekday. For decades, paleontologists have argued about size. Specifically, whether or not certain fossils found in Montana belonged to a young or a completely separate, smaller species. Researchers at North Carolina State University and Ohio University now say they can settle the debate.

A newly discovered species of Arctic rhino lived 23 million years ago. Breakthroughs, discoveries, and DIY tips sent every weekday. About 23 million years ago, a rhinoceros stomped across the Canadian High Arctic . Now extinct, a team of scientists from the Canadian Museum of Nature (CMN) have found a new species of the enigmatic "Arctic rhino." First uncovered almost 40 years ago in lake deposits in Haughton Crater on Devon Island, Nunavut, was more petite than many of its modern descendants.

Science Dinosaurs Dinosaur'mummies' prove some dinos had hooves'Edmontosaurus annectens' stormed around North America during the Late Cretaceous. Breakthroughs, discoveries, and DIY tips sent every weekday. For the first time, paleontologists have successfully reconstructed the profiles of two massive, duck-billed dinosaurs, right down to their pebbled skin and unexpected hooves. Based in part on remains recovered decades ago in the badlands of Wyoming, the pair of specimens were preserved only thanks to an extremely rare, delicate "mummification" process. At around 39 feet long and weighing about 6.2 tons, was one of the largest and most common dinosaurs in present day North America during the Late Cretaceous period.

This work demonstrates the possibilities for improving wildfire and air quality management in the western United States by leveraging the unprecedented hourly data from NASA's TEMPO satellite mission and advances in self-supervised deep learning. Here we demonstrate the efficacy of deep learning for mapping the near real-time hourly spread of wildfire fronts and smoke plumes using an innovative self-supervised deep learning-system: successfully distinguishing smoke plumes from clouds using GOES-18 and TEMPO data, strong agreement across the smoke and fire masks generated from different sensing modalities as well as significant improvement over operational products for the same cases.

Climate anomalies significantly impact terrestrial carbon cycle dynamics, necessitating robust methods for detecting and analyzing anomalous behavior in plant productivity. This study presents a novel application of variational autoencoders (VAE) for identifying extreme events in gross primary productivity (GPP) from Community Earth System Model version 2 simulations across four AR6 regions in the Continental United States. We compare VAE-based anomaly detection with traditional singular spectral analysis (SSA) methods across three time periods: 1850-80, 1950-80, and 2050-80 under the SSP585 scenario. The VAE architecture employs three dense layers and a latent space with an input sequence length of 12 months, trained on a normalized GPP time series to reconstruct the GPP and identifying anomalies based on reconstruction errors. Extreme events are defined using 5th percentile thresholds applied to both VAE and SSA anomalies. Results demonstrate strong regional agreement between VAE and SSA methods in spatial patterns of extreme event frequencies, despite VAE producing higher threshold values (179-756 GgC for VAE vs. 100-784 GgC for SSA across regions and periods). Both methods reveal increasing magnitudes and frequencies of negative carbon cycle extremes toward 2050-80, particularly in Western and Central North America. The VAE approach shows comparable performance to established SSA techniques, while offering computational advantages and enhanced capability for capturing non-linear temporal dependencies in carbon cycle variability. Unlike SSA, the VAE method does not require one to define the periodicity of the signals in the data; it discovers them from the data.

Keep an eye out for dragonfly swarms. Breakthroughs, discoveries, and DIY tips sent every weekday. When you think of migration, the first creature to pop into your head are probably birds . The second will likely be whales, and the third might be monarch butterflies (). You probably have no idea that migratory dragonfly species exist--and that's because even researchers don't know a whole lot about them. And yet, North America may have up to 18 migratory dragonfly species .

Air pollution is a global hazard, and as of 2023, 94\% of the world's population is exposed to unsafe pollution levels. Surface Ozone (O3), an important pollutant, and the drivers of its trends are difficult to model, and traditional physics-based models fall short in their practical use for scales relevant to human-health impacts. Deep Learning-based emulators have shown promise in capturing complex climate patterns, but overall lack the interpretability necessary to support critical decision making for policy changes and public health measures. We implement an uncertainty-aware U-Net architecture to predict the Multi-mOdel Multi-cOnstituent Chemical data assimilation (MOMO-Chem) model's surface ozone residuals (bias) using Bayesian and quantile regression methods. We demonstrate the capability of our techniques in regional estimation of bias in North America and Europe for June 2019. We highlight the uncertainty quantification (UQ) scores between our two UQ methodologies and discern which ground stations are optimal and sub-optimal candidates for MOMO-Chem bias correction, and evaluate the impact of land-use information in surface ozone residual modeling.