Articles tagged with Weather Simulations
A new study reveals that two dam failures and flawed risk assessment amplified the Derna Flood, which was nearly twentyfold more destructive than expected. The research highlights the urgent need for improved flood mitigation strategies, especially in dryland regions.
University of Missouri researchers developed a method using lidar and AI to analyze pedestrian, cyclist, and vehicle interactions at traffic signals. The approach aims to enhance driver awareness, reduce accidents, and improve mobility.
A new real-time surface PM10 retrieval framework uses interpretable automated machine learning to provide accurate data across China. The framework demonstrates robust generalization and stability, outperforming previous studies in cross-validation and rolling iterative validation experiments.
Research reveals Atlantic Ocean storms are primary drivers of Sahara lake filling, contradicting previous theories. Heavy precipitation events triggered by extreme rainstorms reshape water availability in the desert, with implications for ecosystems and human settlements.
A University of Malaga study highlights the importance of accurate estimates of marine litter to address the environmental concern. The research found that current monitoring strategies are inadequate, and simulation experiments identified the most appropriate sampling strategies to achieve more accurate results.
A new study reveals that atmospheric gravity waves play a crucial role in driving latitudinal air currents on Mars, particularly at high altitudes. The findings suggest fundamental differences from Earth's middle atmosphere.
A new study combines satellite collar data with specialized cameras to shed light on the mysterious and important stage of maternal denning. The researchers found that using both methods can accurately predict when polar bears will emerge from their dens and how external factors like temperature influence their behavior.
A new study by MIT confirms the Antarctic ozone layer is healing, with high statistical confidence that reductions in CFCs are the primary cause. The research uses fingerprinting to isolate the anthropogenic signal and rule out natural variability.
The US weather enterprise faces significant risks due to federal science funding cuts, which could lead to reduced weather forecasting accuracy and increased vulnerability to hazardous weather. The value of weather information to the US economy exceeds $100 billion annually.
A new study improves typhoon track and intensity forecasts using advanced parameterization schemes for friction velocity and cloud microphysics. The results show enhanced predictions with improved timing and magnitude of extreme tropical cyclone intensity values.
A new study reveals that downtown Houston skyscrapers designed to withstand hurricanes are actually more vulnerable to intense, localized wind forces called downbursts. The research found that these strong winds can cause significant damage to facade panels, cladding, and windows on lower floors.
A new study reveals that dust storms picking up black carbon from heavily-polluted areas and depositing it in the mountains darkens the ice and snow, causing it to melt faster. This regional environmental problem is a second, more local cause of glacier loss in the Himalayas.
A new study improves solar power forecasts by applying machine learning and post-processing techniques to weather models, revealing the time of day as a crucial factor for accuracy. The research suggests that including hourly data in algorithms can significantly enhance forecasting results.
A study found that excessive Tibetan Plateau spring warming was the primary factor driving catastrophic June 2024 heavy rainfall in southern China. The research used the ESM to simulate extreme warm temperatures over the TP and heavy rainfall in S. China, reproducing approximately 55% of the observed anomaly.
A new study simulates a medium-sized asteroid collision with Earth, revealing dramatic disruptions in climate, atmospheric chemistry, and global photosynthesis. However, ocean plankton growth surprisingly recovers quickly and even increases after the impact, potentially alleviating emerging food insecurity.
Researchers simulate turbulent systems using probability distributions, bypassing chaotic behavior. This approach enables faster computation and opens new avenues for simulating other complex systems.
A recent study has employed machine learning algorithms to improve the accuracy of flood season rainfall predictions. The findings show that combining climate system numerical models with ML-based correction methods results in substantial improvements, increasing prediction scores by up to 7.87%.
Researchers analyzed tropical storm-related precipitation to understand its impact on local water resources and provide insights into climate predictions. The study aims to help communities prepare for extreme storms and manage water resources before and after the storms.
Researchers developed a new data assimilation algorithm using the LETKF technique to enhance global precipitation estimates. The model improved accuracy and computational efficiency, making it suitable for large-scale applications, particularly in mountainous regions.
NRL oceanographers received a Group Achievement Award from NASA for their collaboration with S-MODE, providing real-time ocean model forecasts and glider guidance. The project successfully observed and forecasted ocean features that are too small to see from space, allowing scientists to better understand the global earth system.
Researchers identified alkene ozonolysis as the dominant driver of O₃ formation during cold January days in Lanzhou, China. The study proposes actionable mitigation strategies to reduce O₃ levels by targeting alkene and nitrogen oxide emissions.
The one-core-neuron system (OCNS) minimizes model size while maintaining high performance in deep learning. OCNS employs a single neuron to encode high-dimensional data into a one-dimensional time-series representation, reducing parameters by an average of 0.035%.
Computer models predict that climate change will lead to an increase in spongy moth outbreaks, causing widespread damage to forests. The fungus that normally curbs the moths' spread is expected to decline, allowing the moths to destroy more trees as temperatures rise.
The 105th Annual Meeting of the American Meteorological Society will address key issues in weather, water, and climate. The meeting features a Presidential Forum on physical, social, cultural, and economic impacts of climate change, with experts from the region of the Mississippi River Delta.
A team from the University of Cambridge has developed a model to predict desert locust swarms, enabling national agencies to respond quickly. The model uses weather forecast data and computational models to forecast locust swarm movements both short and long-term.
Researchers developed a unique numerical decision-making framework for solar panel protection against extreme weather conditions. The framework treats individual panels as independent decision-makers, identifying creative solutions to reduce stress and minimize damage during high-wind events.
A new study shows that coordinating the siting of solar farms, wind farms, and storage systems can improve efficiency and reduce costs by aligning renewable power generation with demand. The approach uses fine-grained weather data and energy system modeling to maximize utilization of renewable resources.
New research suggests that regional climate impacts are poorly constrained by climate sensitivity, contradicting expert suggestions to exclude 'hotter' models. The study found no clear correlation between global warming rates and regional drivers of climate change.
The Amazon rainforest is a significant source of condensation nuclei for clouds, according to two studies. The rainforest's plant transpiration and thunderstorms produce aerosol particles that can be transported thousands of kilometers, influencing marine cloud formation.
A new machine learning algorithm reconstructs wind fields quickly and accurately, even with limited observational data. This enhances storm forecasting and hazard preparedness by providing valuable data on a tropical cyclone's intensity and potential impact.
A new study will use advanced integrated hydrologic models to predict New England's water budget changes, tracking shifts in the water table, rainstorm intensity, and drought timing. The research aims to help prepare for an uncertain future by understanding where climate change is altering weather patterns.
Researchers at the Alfred Wegener Institute have developed a new approach called 'storylines' that uses probabilistic attribution studies and 'nudging' techniques to identify the fingerprints of climate change in extreme weather events. By comparing hypothetical scenarios with reality, they can determine how much more probable an event...
The Macrosystems EDDIE modules have been effective in building student and instructor quantitative literacy and data science skills in ecological forecasting, reaching over 35,000 students globally. The modules aim to introduce students to core concepts of forecasting and complement educators' work teaching ecological concepts.
A new study uses deep learning to infer the frequency of atmospheric blocking events over the past 1,000 years, shedding light on their potential impact under climate change. The model was trained using historical data and large ensembles of climate model simulations.
Researchers at Rice University develop OpenSafe.AI, a system that leverages responsible AI and hazard models to provide timely insights for emergency response organizations and communities before, during, and after tropical cyclones and coastal storm events. The goal is to enable better preparation and navigation for severe weather.
Researchers analyzed atmospheric mercury concentrations and found a 10% decline between 2005 and 2020, contrary to global inventories that indicate an increase. The study suggests that human activity-driven emissions are driving the trend, but limitations in data and scientific understanding remain.
A new study led by University of Illinois Chicago scientist Akintomide Akinsanola found that most Americans can expect wetter winters in the future due to global warming. Winter precipitation and extreme weather events are expected to increase across most of the country, with six regions experiencing more frequent very wet winters.
A team of OU scientists, led by Nathan Snook, will use deep learning techniques to analyze numerical simulations of tornadoes. The goal is to improve tornado forecasting by identifying key factors that influence their formation.
A new study published in Nature suggests that Earth's last ice age may provide crucial insights into future El Niño weather events. Researchers used ancient shells of marine organisms and advanced climate modeling to shed light on how El Niño patterns might change in a warming world.
A new study shows that urban forests within walkable distance from residential areas are crucial in reducing heat-related health risks. Researchers found that nearby forests have a pronounced impact on reducing mortality risks, particularly those within 1 kilometre of residential areas.
Researchers from Leipzig University studied cloud movement in the Arctic using a large-scale international research campaign called HALO (AC)³. They observed air mass transformations over open ocean, sea ice, and central Arctic sea ice.
A new model developed by scientists can predict intense, localized heavy rainfall events caused by rapid air rises in clouds. This breakthrough offers potential to improve public safety and preparedness through early warning systems.
A new study by NSF NCAR reveals that detecting Kelvin waves can predict active hurricane periods weeks in advance, allowing forecasters to prepare for potential storms. The research used innovative computer modeling to analyze the connection between Kelvin waves and hurricanes.
A WVU statistician is developing new methods to analyze curve-type data for predicting extreme weather patterns, financial turmoil and pollution levels. The research aims to create innovative tools for understanding high-resolution data and addressing regional risks in mountainous states like West Virginia.
A new AI approach accurately links heat waves to global warming, estimating that record-setting heat waves could occur multiple times per decade under higher warming levels. The method uses actual historical weather data and machine learning to predict the magnitude of extreme events.
Researchers have created a global library of hailstones to better understand and predict damaging storms. The study found that non-spherical, naturally shaped hailstones significantly affect storm modeling outcomes, leading to different pathways, growth, and landing locations.
A new approach incorporating machine learning and a novel radar technique has solved the 'cloud cover' problem in remote sensing, improving land surface temperature tracking accuracy. This is achieved by combining better elevation models with multiple radar echoes from SAR images to reconstruct optical data.
A new study from the University of Illinois Chicago proposes an alternative theory for the formation of Earth's continents, challenging the long-held leading theory. The researchers used computer models to investigate the origin of Archaean zircons, which date back to 2.5-4 billion years ago.
A mathematical model developed by Chris Zobel and colleagues optimizes resource allocation in urban infrastructure while considering social vulnerabilities and projected sea level rise impacts. The approach prioritizes investments that enhance resilience and mitigate socio-economic disparities, enabling informed decision-making that ba...
Researchers from MARUM and the National Oceanography Centre analyzed sensor data over four years to determine seafloor currents in deep waters. The study found that currents sped up, slowed down, reversed direction, and were steered by seafloor relief, contradicting previous steady models.
Researchers have found that the Southern Ocean absorbs 25% more carbon dioxide than previously estimated. The new study used direct measurements to assess existing flux products in the Southern Ocean.
A study by Stanford University researchers reveals a previously unknown relationship between Sahara dust plumes and hurricane rainfall. Thicker dust plumes can lead to heavier rainfall, while thinner ones may suppress hurricane formation over the ocean.
Researchers at USC developed a new method to accurately predict wildfire spread using satellite data and artificial intelligence. The model offers a potential breakthrough in wildfire management and emergency response, providing more precise and timely data for firefighters and evacuation teams battling wildfires.
The University of Maryland has been awarded a five-year, $388 million cooperative funding agreement with NOAA to conduct collaborative research in Earth system science. The Cooperative Institute for Satellite Earth System Studies (CISESS) will continue its national leadership in this field.
Researchers have developed a new method to simulate Convectively Coupled Kelvin Waves in weather forecast models, which could enhance accuracy for predicting hurricanes and heavy rainfall. The study found that current models poorly simulate these waves, indicating a need for future improvements.
Researchers from Kyushu University found that early-onset El Niño leads to warm winters in East Asia due to anomalous warming of the tropical Indian Ocean. Late-onset El Niño, on the other hand, results in colder winters. This study aimed to improve climate modeling and prediction during El Niño events.
Researchers found that higher atmospheric moisture produced weaker and slower-moving African easterly waves, making it harder for hurricanes to form. The study used innovative modeling capabilities to simulate the effects of increased regional moisture on hurricane formation.
A study by Dan Chavas and his team found that the rough texture of the land surface in South America may prevent tornadoes from forming, while a smoother ocean surface in North America enhances their frequency. This challenges established wisdom about the role of the Gulf of Mexico in tornado formation.
Researchers at Drexel University model the potential extent of flooding and combined sewer overflows in Camden, New Jersey, as climate change exacerbates the problem. The team's 'all-pipes' model simulates stormwater flows through every surface and pipe in the area, providing a unique solution to mitigate environmental and health risks.
A new conceptual model, XRO, significantly improves predictive skill of ENSO events at over one year in advance, offering a transparent view into the mechanisms of equatorial Pacific recharge-discharge physics. This improves conventional climate model forecasting and provides robust quantification of extratropical Pacific, tropical Ind...